C J Taylor - Charles James Taylor - Lancaster University - Control Engineering

Taylor, C.J., Chotai, A. and Cross, P. (2012) Non–minimal state variable feedback decoupling control for multivariable continuous–time systems, International Journal of Control, 85, 722–734.

Most research into non–minimal state variable feedback control, in which the state vector is implemented directly from the measured input and output signals of the controlled process, has considered discrete–time systems represented using the either the backward shift or delta operator. However, mechanistic models with physically meaningful parameters are often expressed in terms of differential equations, represented using the Laplace transform or s–operator, and the present article is concerned with multivariable design for such models. The controllability conditions are developed and it is shown how the introduction of a diagonal polynomial matrix for filtering yields a control system that is immediately realisable in practice. Worked examples include optimal control with multi–objective optimisation and pole assignment design with analytical multivariable decoupling, with the latter illustrated by its application to a nonlinear wind turbine simulation.

Taylor, C.J., Chotai, A. and Burnham, K.J. (2011) Controllable forms for stabilising pole assignment design of generalised bilinear systems, Electronics Letters, 47, 437–439.

Bilinear structures are able to represent nonlinear phenomena more accurately than linear models, and thereby help to extend the range of satisfactory control performance. However, closed loop characteristics are typically designed by simulation and stability is not guaranteed. In this reported work, it is shown how bilinear systems are a special case of the more general state dependent parameter (SDP) model, which can subsequently be utilised to design stabilising feedback controllers using a special form of nonlinear pole assignment. To establish the link, however, an important generalisation of the SDP pole assignment method is developed.

Exadaktylos, V. and Taylor, C.J. (2010) Multi–objective performance optimisation for model predictive control by goal attainment, International Journal of Control, 83, 1374–1386.

This paper proposes an approach for performance tuning of Model Predictive Control (MPC) using goal–attainment optimisation of the cost function weighting matrices. The approach is developed for three formulations of the control problem: (i) minimal and (ii) non–minimal design based on the same cost function; and (iii) a non–minimal MPC approach with an explicit integral–of–error state variable and modified cost function. This approach is based on earlier research into multi–objective optimisation for Proportional–Integral–Plus (PIP) control systems. Simulation experiments for a 3–input, 3–output Shell Heavy Oil Fractionator model illustrate the feasibility of MPC goal–attainment for multivariable decoupling and attainment of a specific output response. For this example, the integral–of–error state variable offers improved design flexibility and hence, when it is combined with the proposed tuning method, yields an improved closed loop response in comparison to minimal MPC.

Taylor, J. and Seward, D. (2010) Control of a dual–arm robotic manipulator, Nuclear Engineering International, Vol. 55, No. 673, August, 24–26.

The focus of a recently–awarded Nuclear Decommissioning Authority (NDA) doctoral bursary in the UK is to develop a modular approach for feedback control and intelligent decision making of nonlinear hydraulic actuators to reduce the time and cost of developing bespoke robotic solutions.

Taylor, C.J., Chotai, A. and Young, P.C. (2009) Nonlinear control by input–output state variable feedback pole assignment, International Journal of Control, 82, 1029–1044.

This paper considers pole assignment control of nonlinear dynamic systems described by State Dependent Paramete (SDP) models. The approach follows from earlier research into linear Proportional–Integral–Plus (PIP) methods but, in SDP system control, the control coefficients are updated at each sampling instant on the basis of the latest SDP relationships. Alternatively, algebraic solutions can be derived off–line to yield a practically useful control algorithm that is relatively straightforward to implement on a digital computer, requiring only the storage of delayed system variables, coupled with straightforward arithmetic expressions in the control software. Although the analysis is limited to the case when the open–loop system has no zeros, time delays are handled automatically. The paper shows that the closed–loop system reduces to a linear transfer function with the specified (design) poles. Hence, assuming pole assignability at each sample, global stability of the nonlinear system is guaranteed at the design stage.

Wang, L., Gawthrop, P., Young, P.C. and Taylor, C.J. (2009) Non–minimal state space model–based continuous–time model predictive control with constraints, International Journal of Control, 82, 6, 1122–1137.

This paper proposes a model predictive control scheme based on a non–minimal state–space (NMSS) structure. Such a combination yields a continuous–time state–space model predictive control system that permits hard constraints to be imposed on both plant input and output variables, whilst using NMSS output–feedback without the need for an observer. A comparison between the NMSS and observer–based approaches using Monte Carlo uncertainty analysis shows that the former design is considerably less sensitive to plant–model mismatch than the latter. Through simulation studies, the paper also investigates the role of the implementation filter in noise attenuation, disturbance rejection and robustness of the closed–loop predictive control system. The results show that the filter poles become a subset of the closed–loop poles and this provides a straightforward method of tuning the closed–loop performance to achieve a reasonable balance between speed of response, disturbance rejection, measurement noise attenuation and robustness.

Exadaktylos, V., Taylor, C.J., Wang, L. and Young, P.C. (2009) Forward path model predictive control using a non–minimal state space form, IMECHE Proceedings Journal of Systems and Control, 223, 353–369.

This paper considers Model Predictive Control (MPC) using a Non–Minimal State Space (NMSS) form, in which the state vector consists only of the directly measured system variables. Two control structures emerge from the analysis, namely the conventional feedback form and an alternative forward path structure. There is a close analogy with Proportional–Integral–Plus (PIP) control system design, which is also based on the definition of a NMSS model with two control structures. However, the MPC/NMSS approach has the advantage of handling system constraints at the design stage. Also, since the NMSS model is obtained directly from the identified transfer function model, the covariance matrix for the parameter estimates can be used to evaluate the robustness of the predictive control system to model uncertainty using Monte–Carlo simulation. The effectiveness of the approach is demonstrated by means of simulation examples, including the IFAC'93 benchmark and the ALSTOM nonlinear gasifier problem. For the simulation examples considered here, the forward path form preserves the good performance properties of the original MPC/NMSS controller, whilst at the same time yielding improved robustness.

Jarvis, A., Leedal, D., Taylor, C.J. and Young, P. (2009) Stabilizing global mean surface temperature: a feedback control perspective, Environmental Modelling and Software, 24, 665–674.

In this paper, we develop a discrete time, state variable feedback control regime to analyze the closed–loop properties associated with stabilizing the global mean surface temperature anomaly at 2ºC within a sequential decision making framework made up of 20 year review periods beginning in 2020. The design of the feedback control uses an optimal control approach that minimizes the peak deceleration of anthropogenic CO2 emissions whilst avoiding overshooting the 2ºC target. The peak value for emissions deceleration that satisfies the closed–loop optimization was found to be linearly related to climate sensitivity and a climate sensitivity of 3.5ºC gave a deceleration of –1.9 GtC/a/20 years2. In addition to accounting for the predicted climate dynamics, the control system design includes a facility to emulate a robust corrective action in the face of uncertainty. The behavior of the overall control action is evaluated using an uncertainty scenario for climate model equilibrium sensitivity.

Exadaktylos, V., Silva, M., Ferrari, S., Guarino, M., Taylor, C.J., Aerts, J.–M. and Berckmans, D. (2008) Time–series analysis for online recognition and localization of sick pig (sus scrofa) cough sounds, Journal of the Acoustical Society of America, 124, 3803–3809.

This paper considers the online localization of sick animals in pig houses. It presents an automated online recognition and localization procedure for sick pig cough sounds. The instantaneous energy of the signal is initially used to detect and extract individual sounds from a continuous recording and their duration is used as a pre–classifier. Auto–regression (AR) analysis is then employed to calculate an estimate of the sound signal and the parameters of the estimated signal are subsequently evaluated to identify the sick cough sounds. It is shown that the distribution of just 3 AR parameters provides an ade–quate classifier for sick pig coughs. A localization technique based on the time difference of arrival is evaluated on field data and is shown that it is of acceptable accuracy for this particular application. The algorithm is applied on continuous recordings from a pig house to evaluate its effectiveness. The correct identification ratio ranged from 73% (27% false positive identifications) to 93% (7% false positive identifications) depending on the position of the microphone that was used for the recording. Although the false negative identifications are about 50% it is shown that this accuracy can be enough for the purpose of this tool. Finally, it is suggested that the presented application can be used to online monitor the welfare in a pig house, and provide early diagnosis of a cough hazard and faster treatment of sick animals.

Exadaktylos, V., Silva, M., Aerts, J.–M., Taylor, C.J. and Berckmans, D. (2008) Real–time recognition of sick pig cough sounds, Computers and Electronics in Agriculture, 63, 207–214.

This paper extends existing cough identification methods and proposes a real–time method for identifying sick pig cough sounds. The analysis and classification is based on the frequency domain characteristics of the signal, while an improved procedure to extract the reference is presented. This technique evaluates fuzzy c–means clustering to parts of the training signals and provides a frequency content reference that mirrors the characteristics of sick pig cough. The extraction of the reference is performed in such a way that allows for the identification process to be implemented in real–time applications that would speed up the diagnosis and treatment process and improve animal welfare in pig houses. Preliminary results for the evaluation of the algorithm are based on individual sounds of healthy and sick animals acquired in laboratory conditions. An 85% overall correct classification ratio is achieved with 82% of the sick cough sounds being correctly identified.

Shaban, E.M., Ako, S., Taylor, C.J. and Seward, D.W. (2008) Development of an automated verticality alignment system for a vibro–lance, Automation in Construction, 17, 645–655.

This paper describes the automation of a vibro–lance for ground improvement on a construction site. Here, a vibrating probe is lowered into the ground and penetrates downwards by means of a two arm excavator, compacting the surrounding soil. The control system is straightforward to install on a conventional, hydraulically controlled excavator, such as the one utilised for the field tests reported in the paper. The new system supports the operator by automatically maintaining the verticality of the probe, increasing the speed of operation by a factor of three, whilst also yielding improved accuracy and, therefore, a potential increase in tool life. In particular, the research demonstrates the successful design and implementation of Proportional–Integral–Plus (PIP) control systems for joint control on–site. To the authors knowledge, this represents the first operational use of automation for vibro–lance ground compaction.

This paper considers proportional–integral–plus (PIP) control of non–linear systems defined by state–dependent parameter models, with particular emphasis on three practical demonstrators: a microclimate test chamber, a 1/5th–scale laboratory representation of an intelligent excavator, and a full–scale (commercial) vibrolance system used for ground improvement on a construction site. In each case, the system is represented using a quasi–linear state–dependent parameter (SDP) model structure, in which the parameters are functionally dependent on other variables in the system. The approach yields novel SDP–PIP control algorithms with improved performance and robustness in comparison with conventional linear PIP control. In particular, the new approach better handles the large disturbances and other non–linearities typical in the application areas considered.

Taylor, C.J., Pedregal, D.J., Young, P.C. and Tych, W. (2007) Environmental time series analysis and forecasting with the Captain toolbox, Environmental Modelling and Software, 22, 797–814.

The Data–Based Mechanistic (DBM) modelling philosophy emphasises the importance of parametrically efficient, low order, ‘dominant mode’ models, as well as the development of stochastic methods and the associated statistical analysis required for their identification and estimation. Furthermore, it stresses the importance of explicitly acknowledging the basic uncertainty in the process, which is particularly important for the characterisation and forecasting of environmental and other poorly defined systems. The paper focuses on a Matlab compatible toolbox that has evolved from this DBM modelling research. Based around a state space and transfer function estimation framework, Captain extends Matlab to allow, in the most general case, for the identification and estimation of a wide range of unobserved components models. Uniquely, however, Captain focuses on models with both time variable and state dependent parameters and has recently been implemented with the latest methodological developments in this regard. Here, the main innovations are: the automatic optimisation of the hyper–parameters, which define the statistical properties of the time variable parameters; the provision of smoothed as well as filtered parameter estimates; the robust and statistically efficient identification and estimation of both discrete and continuous time transfer function models; and the availability of various special model structures that have wide application potential in the environmental sciences.

Stables, M.A. and Taylor, C.J. (2006) Nonlinear control of ventilation rate using state dependent parameter models, Biosystems Engineering, 95, 7–18.

The objective of this paper is to develop non–linear proportional–integral–plus (PIP) control algorithms for regulating ventilation rate in mechanically ventilated agricultural buildings. State–dependent parameter (SDP) models are developed for an environmental test chamber, representing a section of a livestock building or glasshouse. Here, the system is modelled using the quasi–linear SDP model structure in which the parameters are functionally dependent on other variables in the system. The model is subsequently utilised to develop a new approach to control system design, based on non–linear PIP pole assignment, with a discrete–time Smith Predictor to handle the sampled time delays. Implementation results for the test chamber demonstrate improved control of ventilation rate, with a faster response to disturbances in comparison with both linear and conventional (linearised) scheduled PIP control. The approach has application to a wide class of other non–linear systems, as demonstrated by simulation examples.

Taylor, C.J. and Shaban, E.M. (2006) Multivariable Proportional–Integral–Plus (PIP) control of the ALSTOM nonlinear gasifier simulation, IEE Proceedings: Control Theory and Applications, 153, 277–285.

Multivariable proportional–integral–plus (PIP) control methods are applied to the nonlinear ALSTOM Benchmark Challenge II. The approach utilises a data–based combined model reduction and linearisation step, which plays an essential role in satisfying the design specifications. The discrete–time transfer function models obtained in this manner are represented in a non–minimum state space form suitable for PIP control system design. Here, full state variable feedback control can be implemented directly from the measured input and output signals of the controlled process, without resorting to the design and implementation of a deterministic state reconstructor or a stochastic Kalman filter. Furthermore, the non–minimal formulation provides more design freedom than the equivalent minimal case, a characteristic that proves particularly useful in tuning the algorithm to meet the Benchmark specifications. The latter requirements are comfortably met for all three operating conditions by using a straightforward to implement, fixed gain, linear PIP algorithm.

Taylor, C.J., Leigh, P.A., Chotai, A., Young, P.C., Vranken, E. and Berckmans, D. (2004) Cost effective combined axial fan and throttling valve control of ventilation rate, IEE Proceedings: Control Theory and Applications, 151, 577–584.

This paper is concerned with Proportional–Integral–Plus (PIP) control of ventilation rate in mechanically ventilated agricultural buildings. In particular, it develops a unique fan and throttling valve control system for a 22m3 test chamber, representing a section of a livestock building or glasshouse, at the Katholieke Universiteit Leuven. Here, the throttling valve is employed to restrict airflow at the outlet, so generating a higher static pressure difference over the control fan. In contrast with previous approaches, however, the throttling valve is directly employed as a second control actuator, utilising airflow from either the axial fan or natural ventilation. The new combined fan/valve configuration is compared with a commercially available PID–based controller and a previously developed scheduled PIP design, yielding a reduction in power consumption in both cases of up to 45%.

Taylor, C.J., Mckenna, P.G., Young, P.C., Chotai, A. and Mackinnon, M. (2004) Macroscopic traffic flow modelling and ramp metering control using Matlab/Simulink, Environmental Modelling and Software, 19, 975–988.

Computer programs to simulate traffic flow offer an opportunity to evaluate new strategies for reducing delays, congestion, fuel consumption and pollution. This paper describes a Statistical Traffic Model or STM, which is based on accepted macro–modelling concepts, such as the conservation of vehicles and the fundamental traffic diagram. In this case, the model is constructed using the well known Matlab/Simulink™ software package, so providing an integrated approach for data processing, graphical presentation of data, control system design and macroscopic simulation in one straightforward to use, widely available environment. To illustrate the methodology, the STM is applied to a section of the M3/M27 Ramp Metering Pilot Scheme in the UK. This Highways Agency sponsored project, based in the Southampton area, utilises traffic lights at the on–ramp entrances to regulate access to the main carriageway of the motorway, in an attempt to maintain flow close to the capacity. The paper utilises the model to help design a locally–coordinated ramp metering algorithm, based on proportional–integral–plus (PIP) control methods. In this manner, the STM proves particularly valuable for the application of multi–objective optimisation techniques in the design of new traffic management systems.

Taylor, C.J., Leigh, P., Price, L., Young, P.C., Berckmans, D. and Vranken, E. (2004) Proportional–Integral–Plus (PIP) control of ventilation rate in agricultural buildings, Control Engineering Practice, 12, 225–233.

This paper is concerned with proportional–integral–plus (PIP) control of ventilation rate in mechanically ventilated agricultural buildings. The PIP controller can be interpreted as a logical extension of conventional proportional–integral/proportional–integral–derivative (PI/PID) controllers, but with inherent model–based predictive control action. In particular, the paper considers the design of an optimal, scheduled gain PIP algorithm for a 22 m3 forced ventilation test chamber at the Katholieke Universiteit Leuven. Such a PIP approach proves more robust to pressure disturbances than an equivalent PID design and constitutes a preliminary step towards the development of the complete micro–climate controller.

Gu, J., Taylor J. and Seward, D. (2004) The automation of bucket position for the intelligent excavator LUCIE using the Proportional–Integral–Plus (PIP) control strategy, Journal of Computer–Aided Civil and Infrastructure Engineering, 19, 16–27.

This article considers the application of Proportional–Integral–Plus (PIP) control to the Lancaster University Computerised Intelligent Excavator (LUCIE), which is being developed to dig foundation trenches on a building site. Previous work using LUCIE was based on the ubiquitous PI/PID control algorithm, tuned on–line, and implemented in a rather ad hoc manner. By contrast, the present research utilizes new hardware and advanced model–based control system design methods to improve the joint control and so provide smoother, more accurate movement of the excavator arm. In this article, a novel nonlinear simulation model of the system is developed for MATLAB/SIMULINK©, allowing for straightforward refinement of the control algorithm and initial evaluation. The PIP controller is compared with a conventionally tuned PID algorithm, with the final designs implemented on–line for the control of dipper angle. The simulated responses and preliminary implementation results demonstrate the feasibility of the approach.

The paper describes the utility of a low cost, 1m2 by 2m forced ventilation, micro–climate test chamber, for the support of research and teaching in mechatronics. Initially developed for the evaluation of a new ventilation rate controller, the fully instrumented chamber now provides numerous learning opportunities and individual projects for both undergraduate and postgraduate research students.

Ghavipanjeh, F., Taylor, C.J., Young, P.C. and Chotai, A. (2001) Data–based modelling and Proportional–Integral–Plus (PIP) control of nitrate in an activated sludge benchmark, Water Science and Technology, 44, 87–94.

This paper presents the result of an investigation into the Proportional Integral Plus (PIP) control of nitrate in the second zone of an activated sludge benchmark. A data–based reduced order model is used as the control model and identified using the Simplified Refined Instrumental Variable (SRIV) identification and estimation algorithm. The PIP control design is based on the Non Minimum State Space (NMSS) form and State Variable Feedback (SVF) methodology. The PIP controller is tested against dynamic load disturbances and compared with the response of a well–tuned PI controller.

Proportional–integral–plus (PIP) controllers exploit the full power of optimal state variable feedback within a nonminimum state space (NMSS) setting. They are simple to implement and provide a logical extension of conventional proportional–integral/proportional–integral–derivative (PI/PID) controllers, with additional dynamic feedback and input compensators introduced automatically when the process is of greater than first order or has appreciable pure time delays. The present paper provides a tutorial introduction to the NMSS/PIP control design methodology and associated system identification procedure. The latter is based on the utilization of the simplified refined instrumental variable (SRIV) algorithm for the estimation of transfer function models. The practical utility of these techniques is illustrated by their application to the IFAC93 benchmark system, a seventh–order stochastic simulation whose parameters vary randomly within specified ranges. This benchmark provides a good simulation example for tutorial purposes, since it requires the control engineer to work through all the usual design steps, including identification of a low–order control model, control system design, and implementation using a standard programming language, in this case ‘C’. Finally, note that the statistical estimation tools described in the paper have been assembled as a tool–box within the MatlabTM software environment.

McCabe, A.P., Young P., Chotai, A. and Taylor, C.J. (2000) Proportional–Integral–Plus (PIP) control of non–linear systems, Systems Science, 26, 25–46.

A control design procedure is proposed for a large class of discrete–time non–linear systems which combines exact linearization by feedback techniques with an established linear controller design methodology. The combined system thus inherits many of the characteristics of the linear system with regards to tracking, disturbance rejection and robustness. The design both of the linearizing feedback and of the overall linear controller is analyzed and example systems are used to illustrate the design method and demonstrate the performance characteristics of the closed–loop system. Special consideration is given to the effect of the non–linear feedback components on the performance of the controlled system under disturbance conditions and in the case of model mismatch.

Although it is able to exploit the full power of optimal state variable feedback within a non–minimum state–space (NMSS) setting, the proportional–integral–plus (PIP) controller is simple to implement and provides a logical extension of conventional proportional–integral and proportional–integral–derivative (PI/PID) controllers, with additional dynamic feedback and input compensators introduced automatically by the NMSS formulation of the problem when the process is of greater than first order or has appreciable pure time delays. The present paper applies the PIP methodology to the ALSTOM benchmark challenge, which takes the form of a highly coupled multi–variable linear model, representing the gasifier system of an integrated gasification combined cycle (IGCC) power plant. In particular, a straightforwardly tuned discrete–time PIP control system based on a reduced–order backward–shift model of the gasifier is found to yield good control of the benchmark, meeting most of the specified performance requirements at three different operating points.

Taylor, C.J., Chotai, A. and Young P.C. (2000) State space control system design based on non–minimal state–variable feedback: further generalisation and unification results, International Journal of Control, 73, 1329–1345.

This paper shows how proportional–integral–plus linear–quadratic (PIP–LQ) control, based on non–minimal state space (NMSS) control system design, can be constrained to yield exactly the same control algorithm as both generalized predictive control (GPC) and standard, minimal state, linear quadratic gaussian (LQG) design methods. However, while NMSS includes these other approaches as special cases, it is less constrained and so more flexible in general terms: for example, while PIP–LQ has the simplicity of GPC, it is formulated like LQG in the powerful context of state variable feedback (SVF) control, which allows for ready access to modern robust control methods. Furthermore, the paper suggests that the NMSS approach provides the greater design freedom, with a wider range of possible LQ solutions than its minimal state space equivalent.

Taylor, C.J., Young, P.C. Chotai A., Mcleod, A.R. and A.R. Glasock (2000) Modelling and Proportional–Integral–Plus control design for free air carbon dioxide enrichment systems, Journal of Agricultural Engineering Research, 75, 365–374.

Proportional–integral–plus (PIP) control is employed to maintain gas concentration in a small–scale free air carbon dioxide enrichment (FACE) system. FACE systems are designed to produce controlled concentrations of elevated carbon dioxide, or other atmospheric gases, enabling plant growth experiments to be carried out for in situ vegetation without the use of chambers or other enclosures. Current FACE systems employ control algorithms based on classically derived two– or three–term control laws with manually tuned parameters. However, small FACE plots are more susceptible to turbulent eddies than larger scale systems, making control of concentration particularly difficult. The research described in the present paper employs data from planned FACE experiments to develop PIP control algorithms exploiting model–based predictive control action. Initial trials utilizing this approach yield good results for a small–scale FACE system operating in an uncut arable meadow.

Price, L., Young, P., Berckmans, D., Janssens, K.and Taylor, J. (1999) Data–Based Mechanistic Modelling (DBM) and control of mass and energy transfer in agricultural buildings, Annual Reviews in Control, 23, 71–82.

This paper discusses the Data–Based Mechanistic (DBM) approach to modelling the micro–climate in agricultural buildings. Here, the imperfect mixing processes that dominate the system behaviour during forced ventilation are first modelled objectively, in purely data–based terms, by continuous–time transfer function relationships. In their equivalent differential equation form, however, these models can be interpreted in terms of the Active Mixing Volume (AMV) concept, developed previously at Lancaster in connection with pollution transport in rivers and soils and, latterly, in modelling the micro–climate of horticultural glasshouses. The data used in the initial stages of the research project, as described in the paper, have been obtained from a series of planned ventilation experiments carried out in a large instrumented chamber at Leuven. The overall objectives of this collaborative study are two–fold: first, to gain a better understanding of the mass and heat transfer dynamics in the chamber; and second, to develop models that can form the basis for the design of optimal Proportional–Integral–Plus, Linear Quadratic (PIP–LQ) climate control systems for livestock buildings of a kind used previously for controlling the micro–climate in horticultural glasshouses.

Jarvis, A.J., Young, P.C., Taylor, C.J., Davies, W.J. (1999) An analysis of the dynamic response of stomatal conductance to a reduction in humidity over leaves of cedrella odorata, Plant, Cell and Environment, 22, 913–924.

Single leaves of 3–month–old Cedrella odorata seedlings were exposed to a step reduction in the ambient dew point. The resultant time series of dynamic variations in leaf surface water vapour concentration, leaf surface water vapour concentration gradient, transpiration rate and stomatal conductance to water vapour, are analysed using the data–based mechanistic (DBM) modelling methodology of Young (e.g. Young & Lees 1992; Minchin et al. 1996). It is shown that the identified second–order, dynamic model between transpiration rate (as the input) and stomatal conductance (as the output) provides an appropriate, physiologically meaningful, description of the system. In particular, the dynamic relationship between these two variables is remarkably linear and can be resolved in terms of two parallel, first–order, subsystems; a model which complements the results of Cowan (1977) for cotton. The model is also compared with the recently published simulation model of Haefner, Buckley & Mott (1997).

Taylor, C.J., Young, P.C., Chotai, A. and Whittaker, J. (1998) Non–minimal state space approach to multivariable ramp metering control of motorway bottlenecks, IEE Proceedings: Control Theory and Applications, 145, 568–574.

The paper discusses the automatic control of motorway traffic flows utilising ramp metering, i.e. traffic lights on the on–ramp entrances. A multivariable ramp metering system is developed, based on the nonminimal state space (NMSS) approach to control system design using adaptive proportional–integral–plus, linear quadratic (PIP–LQ) optimal controllers. The controller is evaluated on a nonlinear statistical traffic model (STM) simulation of the Amsterdam motorway ring road near the Coen Tunnel.

The paper shows that the digital proportional–integral–plus (PIP) controller formulated within the context of non–minimum state space (NMSS) control system design methodology is directly equivalent, under certain non–restrictive pole assignment conditions, to the equivalent digital Smith predictor (SP) control system for time delay systems. This allows SP controllers to be considered within the context of NMSS state variable feedback control, so that optimal design methods can be exploited to enhance the performance of the SP controller. Alternatively, since the PIP design strategy provides a more flexible approach, which subsumes the SP controller as one option, it provides a superior basis for general control system design. The paper also discusses the robustness and disturbance response characteristics of the two PIP control structures that emerge from the analysis and demonstrates the efficacy of the design methods through simulation examples and the design of a climate control system for a large horticultural glasshouse system.

Lees, M.J., Taylor, C.J., Young, P.C. and Chotai, A. (1998) Modelling and PIP control design for open top chambers, Control Engineering Practice, 6, 1209–1216.

The paper first describes the identification of a control model for carbon dioxide concentration in an open–top chamber (OTC) used in plant physiology atmospheric change experiments. This model is then employed in the design of a gain–scheduled controller utilising the Proportional–Integral–Plus (PIP) control design methodology developed by Young et al. (1987). The system has been evaluated in a number of field trials, yielding good control, well within the required design specifications.

Book Chapters

This chapter describes the generalisation of the State Dependent Parameter (SDP) approach to the modelling of nonlinear dynamic systems, to now include Multi–State Dependent Parameter (MSDP) nonlinearities. The recursive estimation of the MSDP model parameters in a multivariable state space occurs along a multi–path trajectory, employing the Kalman Filter and Fixed Interval Smoothing algorithms. The novelty of the method lies in redefining the concepts of sequence (predecessor, successor), allowing for their use in a multi–state dependent context, so producing efficient parameterisation for a fairly wide class of non–linear, stochastic dynamic systems. The format of the estimated model allows its direct use in control system design. Two worked examples in Matlab are considered.

This tutorial chapter uses case studies based on recent engineering applications, to re–examine the non–minimal, state variable feedback approach to control system design. We show how the non–minimal state space (NMSS) representation seems to be the natural description of a discrete–time Transfer Function, since its dimension is dictated by the complete structure of the model. This is in contrast to minimal state space descriptions, which only account for the order of the denominator and whose state variables, therefore, usually represent combinations of input and output signals. The resulting control algorithm can be interpreted as a logical extension of the conventional Proportional–Integral (PI) controller, facilitating its straightforward implementation using a standard hardware–software arrangement. Finally, the basic NMSS approach is readily extended into multivariable, model–predictive and nonlinear control systems, hence the chapter briefly discusses these areas and gives pointers to the latest research results.

This chapter illustrates the utility of, and provides the basic documentation for, SSpace, a recently developed MATLAB toolbox for the analysis of State Space systems. The key strength of the toolbox is its generality and flexibility, both in terms of the particular state space form selected and the manner in which generic models are straightforwardly translated into MATLAB code. With the help of a relatively small number of functions, it is possible to fully exploit the power of state space systems, performing operations such as filtering, smoothing, forecasting, interpolation, signal extraction and likelihood estimation. The chapter provides an overview of SSpace and demonstrates its usage with several worked examples.

Lees, M.J., Taylor, J., Chotai, A., Young, P.C. and Chalabi, Z.S. (1996) Design and implementation of a Proportional–Integral–Plus (PIP) control system for temperature, humidity, and carbon dioxide in a glasshouse, Acta Horticulturae, Vol. 406, 115–123.

Conventional glasshouse climate controllers are based upon continuous-time PI controllers manually tuned to achieve adequate, although rather poor, tracking of set point changes. In this paper we consider the alternative, model based, Proportional–Integral–Plus (PIP) control system design which, although only slightly more complex than a PI controller, achieves much tighter control of the climate variables, allowing optimal setpoints to be realised. A linear control model is identified and estimated from experimental data collected in a Venlo glasshouse at Silsoe Research Institute (SRI). A Non–Minimum State Space (NMSS) representation of this control model is then used to design a robust PIP controller which was implemented during the 1993/94 winter growing season with a tomato crop. Control results were excellent with very tight control to the desired setpoints in all three variables. Air temperature was controlled to within 0.5C of the setpoint for 85% of the validation period, and was shown to be very robust to model uncertainty and extreme weather conditions. Relative humidity was controlled to within 2% RH for 90% of the validation period, and CO2 was controlled to within 15 ppm for 80% of the validation period.

Taylor, J., Young, P.C. and Chotai, A. (1994) On the relationship between GPC and PIP control, appears in D.W. Clarke (Ed.), Advances in Model–Based Predictive Control, Oxford University Press, Oxford, 53–68.

The relationship between GPC, and another modern control system design method, Proportional–Integral–Plus (PIP), is explored. The similarities in structural terms are assessed, and a number of examples are provided which illustrate their design equivalence.

Conference Articles

2012

Robertson, D., Taylor, C.J. and Lokuciewski, C. (2012) State–dependent system identification for control of a hydraulically–actuated nuclear decommissioning robot, 16th IFAC Symposium on System Identification (SYSID–12), July, Brussels, Belgium.

Young, P.C. and Taylor, C.J. (2012) Recent developments in the Captain Toolbox for Matlab, 16th IFAC Symposium on System Identification (SYSID–12), July, Brussels, Belgium.

Taylor, C.J., Young, P.C. and Cross, P. (2012) Practical experience with unified discrete and continuous–time, multi–input identification for control system design, 16th IFAC Symposium on System Identification (SYSID–12), July, Brussels, Belgium.

2011

Gunn, K.J., Lingwood, C.J. and Taylor, C.J. (2011) An independent validation of the optimality of latching and de–clutching control by evolutionary methods, 9th European Wave and Tidal Energy Conference (EWTEC–11), September, University of Southampton, UK.

Cross, P., Taylor, C.J. and Aggidis, G.A. (2011) State dependent feed–forward control of a wave energy converter model, 9th European Wave and Tidal Energy Conference (EWTEC–11), September, University of Southampton, UK.

2010

Taylor, C.J., Chotai, A. and Robertson, D. (2010) State Dependent Control of a Robotic Manipulator used for Nuclear Decommissioning Activities, IEEE International Conference on Intelligent Robots and Systems (IROS–10), October, Taipei, Taiwan.

Taylor, C.J. and Chotai, A. (2010) Nonlinear control by input–output pole assignment: state space derivation, UKACC International Conference (Control–10), September, Coventry, UK.

Cross, P., Taylor, C.J. and Aggidis, G.A. (2010) Continuous–Time Feedforward Proportional–Integral–Plus Control, UKACC International Conference (Control–10), September, Coventry, UK.

Taylor, C.J. and Young, P.C. (2010) Captain Toolbox Analysis of the Hyperfast Switching Peltier Cooling System Benchmark, UKACC International Conference (Control–10), September, Coventry, UK.

Gunn, K.J., Lingwood, C.J. and Taylor, C.J. (2010) Multi–Objective Evolutionary Optimisation of the Geometry of a Class of Controlled Wave Energy Converter, UKACC International Conference (Control–10), September, Coventry, UK.

2009

Bakari, M.J., Seward, D.W. and Taylor, C.J., The Development of a Prototype of a Multi–Arm Robotic System for Decontamination and Decommissioning (D&D) Applications within the Nuclear Industry (2009) 12th International Conference on Environmental Remediation and Radioactive Waste Management, October, Liverpool, UK.

Cross, P., Taylor, C.J. and Aggidis, G. (2009) Feed–forward control of a nonlinear power take–off simulation for wave energy conversion, 20th International Conference on Systems Engineering (ICSE–08), September, Coventry, UK.

Gunn, K., Taylor, C.J. and Lingwood, C. (2009) Evolutionary algorithms for the development and optimisation of wave energy converter control systems, 8th European Wave and Tidal Energy Conference (EWTEC–09), September, Uppsala, Sweden.

Taylor, C.J., Stables, M.A., Cross, P., Gunn K. and Aggidis, G. (2009) Linear and nonlinear control of a power take–off simulation for wave energy conversion, 8th European Wave and Tidal Energy Conference (EWTEC–09), September, Uppsala, Sweden.

Taylor, C.J. and Chotai, A. (2009) State dependent ramp metering control of motorway bottlenecks using nonlinear pole assignment, 12th IFAC Symposium on Control in Transportation Systems (CTS–09), September, Redondo Beach, California, USA.

Ma, X., Taylor, J. and Joyce, M. (2009) An interactive PC–based electrical power system simulator for engineering education, 5th International CDIO Conference: Reframing Engineering Education: Impact and Future Direction, June, Singapore Polytechnic, Singapore.

2008

Gunn, K. and Taylor, C.J. (2008) Genetic algorithms for the development and optimisation of wave energy converter control systems, 23rd European Institute for Applied Research Workshop on Advanced Control and Diagnosis (IAR–ACD–08), November, Coventry University, UK.

Taylor, C.J., Chotai, A. and Young, P.C. (2008) Nonlinear pole assignment control of state dependent parameter models with time delays, UKACC International Conference (Control–08), September, Manchester, UK.

Exadaktylos, V., Taylor, C.J. and Chotai, A. (2008) Constraint handling for state dependent parameter models, UKACC International Conference (Control–08), September, Manchester, UK.

Taylor, C.J. and Chotai, A. (2008) Non–minimal state dependent Riccati equation and pole assignment control of nonlinear systems, 19th International Conference on Systems Engineering (ICSE–08), August, Las Vegas, USA.

2007

Exadaktylos, V., Silva, M., Aerts, J.–M., Taylor, C.J. and Berckmans, D. (2007) Frequency analysis for real–time identification of sick pigs and disease monitoring in pig houses, 3rd European Conference on Precision Livestock Farming (ECPLF–07), June, Skiathos, Greece.

Exadaktylos, V., Silva, M., Aerts, J.–M., Taylor, C.J., Ferrari, S., Guarino, M. and Berckmans, D. (2007) Online recognition and localisation of sick pig cough sounds, 13th International Congress in Animal Hygiene (ISAH–07), Tartu, Estonia.

2006

Taylor, C.J., Shaban, E.M., Chotai, A. and Ako, S. (2006) Development of an automated verticality alignment system for a vibro–lance, 7th Portuguese Conference on Automatic Control (Controlo–07), September, Lisbon, Portugal.

Sidiropoulou, E., Shaban, E.M., Taylor, C.J., Tych, W. and Chotai, A. (2006) Linear, nonlinear and classical control of a 1/5th scale automated excavator, 18th International Conference on Systems Engineering (ICSE–06), September, Coventry, UK.

Stables, M.A., Taylor, C.J. and Chotai, A. (2006) Control of micro–climate using time delay state dependent parameter models, 18th International Conference on Systems Engineering (ICSE–06), September, Coventry, UK.

Exadaktylos, V., Taylor, C.J. and Chotai, A. (2006) Non–minimal model predictive control with an integral–of–error state variable, UKACC International Conference (Control–06), August, Glasgow, UK.

Taylor, C.J., Shaban, E.M., Chotai, A. and Ako, S. (2006) Nonlinear control system design for construction robots using state dependent parameter models, UKACC International Conference (Control–06), August, Glasgow, UK.

Ding, L., Bradshaw, A. and Taylor, C.J. (2006) Design of discrete–time RIDE control system, UKACC International Conference (Control–06), August, Glasgow, UK.

2005

Dixon, R., Taylor, C.J. and Shaban, E.M. (2005) Comparison of classical and modern control applied to an excavator–arm, International Federation of Automatic Control 16th Triennial World Congress (IFAC–05), July, Prague, Czech Republic.

Shaban, E.M. and Taylor, C.J. (2005) Proportional–Integral–Plus control of a class of nonlinear systems using exact and partial linearisaton by feedback, International Congress for Global Science and Technology 1st International Conference on Automatic Control and System Engineering (ACSE–05), December, Cairo, Egypt (appears as an article in ACSE journal Automatic Control Specific Applications, 6, 55–70, 2006).

Shaban, E.M., Zied, K., Taylor, C.J. and Seward, D.W. (2005) Nonlinear control system design for construction robots: estimation, partial linearization by feedback and state–dependent–parameter control, 22nd International Symposium on Automation and Robotics in Construction (ISARC–05), September, Ferrara, Italy.

2004

Gu, J., Taylor, J. and Seward, D. (2004) Proportional–Integral–Plus (PIP) gain scheduling control of an intelligent excavator, 21st International Symposium on Automation and Robotics in Construction (ISARC–04), September, Jeju, Korea.

Taylor, C.J., Stables, M.A. and Young, P.C. (2004) Identification of a multivariable state dependency for airflow rate in a forced ventilation test chamber, UKACC International Conference (Control–04), September, Bath, UK.

Taylor, C.J. and Shaban, E.M. (2004) Multivariable Proportional–Integral–Plus (PIP) control of the ALSTOM nonlinear gasifier model, UKACC International Conference (Control–04), September, Bath, UK.

Ding, L., Bradshaw, A. and Taylor, C.J. (2004) Robustness comparison of control systems for a nuclear power plant, Paper–38, UKACC International Conference (Control–04), September, Bath, UK.

Shaban, E.M., Taylor, C.J. and Chotai, A. (2004) State dependent parameter Proportional–Integral–Plus (SDP–PIP) control of a nonlinear robot digger arm, UKACC International Conference (Control–04), September, Bath, UK.

2003

Bradshaw, A., Ding, L. and Taylor, C.J. (2003) Characteristic comparison of control systems for a nuclear power plant, Proceedings of the International Conference on Advances in Agile Manufacturing (ICAM–03) December, Beijing, China, Published by China Machine Press, 695–699.

Taylor, C.J., Mckenna, P.G., Young, P.C., Chotai, A. and Scariza, J. (2003) Traffic flow modelling and linked ramp metering control for the M3/M27 pilot scheme, appears in K.J. Burnham and O.C.L Haas (Editors), Proceedings 16th International Conference on Systems Engineering (ICSE–03), September, Coventry University, 687–692.

Leigh, P., Taylor, C.J., Chotai, A., Young, P.C. and Vranken, E., Implementation of a combined fan and valve control design in a ventilation chamber (2003) appears in K.J. Burnham and O.C.L Haas (Editors), Proceedings 16th International Conference on Systems Engineering (ICSE–03), September, Coventry University, 423–428.

Kontoroupis, P., Young, P.C., Chotai, A. and Taylor, C.J. (2003) State Dependent Parameter Proportional–Integral–Plus (SDP–PIP) control of nonlinear systems, appears in K.J. Burnham and O.C.L Haas (Editors), Proceedings 16th International Conference on Systems Engineering (ICSE–03), September, Coventry University, 373–378.

Kontoroupis, P., Mckenna, P.G., Taylor, C.J., Chotai A. and Young, P.C. (2003) State Dependent Parameter (SDP) modelling of a traffic corridor based on a macroscopic Statistical Traffic Model (STM), appears in K.J. Burnham and O.C.L Haas (Editors), Proceedings 16th International Conference on Systems Engineering (ICSE–03), September, Coventry University, 367–372.

2002

Taylor, C.J., Bradshaw, A., Chaplin, R.V., French, M. and Widden, M.B. (2002) Wave energy research at Lancaster University: PS FRog and Frond, World Renewable Energy Congress VII, June–July, Cologne, Germany, Proceedings Edited by A.A.M. Sayigh. Pergamon, Elsevier Science Ltd., UK.

2001

2000

Ghavipanjeh, F., Taylor, C.J., Young, P.C. and Chotai, A. (2000) Data–based modelling and Proportional–Integral–Plus (PIP) control of nitrate in an activated sludge benchmark, Aquatech Conference on Wastewater and EU Nutrient Guidelines, September, Amsterdam, The Netherlands.

Taylor, C.J., Chotai, A. and Young P.C. (2000) Non–minimal state space design: a unified approach to state variable feedback, 15th International Conference on Systems Engineering (ICSE–00), September, Coventry University.

McCabe, A.P., Chotai, A., Young, P.C. and Taylor, C.J. (2000) Proportional–Integral–Plus (PIP) control of non–linear systems, 15th International Conference on Systems Engineering (ICSE–00), September, Coventry University. Proceedings Vol. 2, pp. 407–412.

Leigh, P., Taylor, C.J., Price, L., Chotai, A., Young, P.C., Vranken, E., Gevers, R. and Berckmans, D. (2000) Modelling and Proportional–Integral–Plus (PIP) control of ventilation rate in a Fan Test Chamber. 15th International Conference on Systems Engineering (ICSE–00), September, Coventry University.

Ghavipanjeh, F., Taylor, C.J., Chotai, A. and Young, P.C. (2000) Modelling and PIP control of dissolved oxygen in a wastewater treatment benchmark system, UKACC International Conference (Control–00), September, University of Cambridge.

McCabe, A.P., Taylor, C.J., Chotai, A. and Young, P.C. (2000) Proportional–Integral–Plus control of feedback–dependent systems with input and output constraints, UKACC International Conference (Control–00), September, University of Cambridge.

Taylor, C.J., Price, L., Leigh, P., Young, P.C., Berckmans, D., Janssens, K., Vranken, E. and Gevers, R. (2000) Proportional–Integral–Plus (PIP) control of agricultural buildings, International Conference on Modelling and Control in Agriculture, Horticulture and Post–Harvest Processing (Agricontrol–00), July, Wageningen, The Netherlands.

1999

Leigh, P., Young, P., Chotai, A., Price, L., Taylor, J., Vranken, E., Berckmans, D., Modelling and control of forced ventilation in agricultural livestock buildings (1999) 5th International congress of Biometeorology and Urban Climatology, November, Sydney, Australia.

1998

Taylor, C.J., Chotai, A., Young, P.C. (1998) Continuous time Proportional–Integral–Plus (PIP) control with filtering polynomials, UKACC International Conference (Control–98), September, University of Wales, Swansea, Institute of Electrical Engineering, conference publication no. 455, vol. 2, 1391–1396.

Kotsialos, A., Ellouni, N., Middelham, F., van Schuppen, J., Taylor, C.J. (1998) DACCORD: Integrated and co–ordinated control, 8th World Conference on Transportation Research, Antwerp–Belgium, July, Paper 1190 (session II–C4/02 the DACCORD project).

1997

Taylor, C.J., Young, P.C., Chotai, A. and Mcleod, A.R. (1997) Modelling and control design for Free Air CO2 Enrichment (FACE) systems used in plant physiology climate change experiments, International Federation of Automatic Control (IFAC) 3rd workshop on Mathematical and Control applications in agriculture and horticulture, September–October, Hannover, Germany, IFAC preprint (Eds. A Munack and H.–J. Tantau), Pergamon, 237–242.

Taylor, J., Young, P.C. and Chotai, A. (1997) Forecasting, simulation and control of interurban traffic networks, 12th International Conference on Systems Engineering (ICSE–97), September, Coventry University, vol. 2, 679–684.

Taylor, J., Young, P.C. and Chotai, A. (1997) Robust PIP control of multivariable stochastic systems, 12th International Conference on Systems Engineering (ICSE–97), September, Coventry University, vol. 2, 673–678.

Taylor, J., Whittaker, J., Young, P. and Chotai, A. (1997) Forecasting and control of interurban traffic networks using a state space formulated traffic model, International Federation of Automatic Control (IFAC) 8th symposium on Transportation systems, June, Chania, Greece, IFAC/IFIP/IFORS preprint (Eds. M. Papageorgiou and A. Pouliezos), vol. 3, 1054–1059.

Lees, M.J., Taylor, J., Young, P.C. and Chotai, A. (1997) Modelling and control design for open top chambers used in plant physiology climate change experiments, International Federation of Automatic Control (IFAC) 3rd symposium on Modelling and Control in Biomedical Systems (including biological systems), University of Warwick, March, Elsevier (Eds. Linkins and Carson), Oxford, 325–323.

1996

Taylor, C.J., Young, P.C. and Chotai, A. and Dixon, R. (1996) Structural and predictive aspects of Proportional–Integral–Plus (PIP) control, UKACC International Conference (Control–96), September, University of Exeter, Institute of Electrical Engineering, conference publication no. 427, vol. 2, 1374–1379.

Taylor, C.J., Young, P.C., Chotai, A., Tych, W. and Lees, M.J. (1996) The importance of structure in PIP control design, UKACC International Conference (Control–96), September, University of Exeter, Institute of Electrical Engineering, conference publication no. 427, vol. 2, 1196–1201.

Taylor, C.J., Young, P.C. and Chotai, A. (1996) PIP optimal control with a risk sensitive criterion, UKACC International Conference (Control–96), September, University of Exeter, Institute of Electrical Engineering, conference publication no. 427, vol. 2, 959–964.

Taylor, C.J., Lees, M.J., Young, P.C. and Minchin, P.E.H. (1996) True Digital Control of carbon dioxide in agricultural crop growth experiments, International Federation of Automatic Control 13th Triennial World Congress (IFAC–96), June–July, San Francisco, USA, vol. B, 405–410, Elsevier.

Taylor, C.J., Young, P.C. and Chotai, A. (1996) True Digital Control of interurban traffic networks, International Federation of Automatic Control 13th Triennial World Congress (IFAC–96), June–July, San Francisco, USA, vol. P, 325–330, Elsevier.

Dr C. James Taylor

Senior Lecturer, Engineering Department

Journal Articles

Book Chapters

Conference Articles

2012

2011

2010

2009

2008

2007

2006

2005

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2003

2002

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1996