A. Chotai

Non-linear control by input–output state variable feedback pole assignment

This article considers pole assignment control of non-linear dynamic systems described by state-dependent parameter (SDP) models. The approach follows from earlier research into linear proportional-integral-plus 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. This article 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 non-linear system is guaranteed at the design stage.

True digital control:statistical modelling and non–minimal state space design

Develops a true digital control design philosophy that encompasses data–based model identification, through to control algorithm design, robustness evaluation and implementation. With a heritage from both classical and modern control system synthesis, this book is supported by detailed practical examples based on the authors’ research into environmental, mechatronic and robotic systems. Treatment of both statistical modelling and control design under one cover is unusual and highlights the important connections between these disciplines. Starting from the ubiquitous proportional–integral controller, and with essential concepts such as pole assignment introduced using straightforward algebra and block diagrams, this book addresses the needs of those students, researchers and engineers, who would like to advance their knowledge of control theory and practice into the state space domain; and academics who are interested to learn more about non–minimal state variable feedback control systems. Such non–minimal state feedback is utilised as a unifying framework for generalised digital control system design. This approach provides a gentle learning curve, from which potentially difficult topics, such as optimal, stochastic and multivariable control, can be introduced and assimilated in an interesting and straightforward manner.

Non-minimal state variable feedback decoupling control for multivariable continuous-time systems

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 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 this 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.

Self-adaptive and self-tuning control of a nutrient film technique (NFT) system

Abstract There has been considerable interest during the past few years in the development of self-adaptive and self-tuning control methods for systems whose dynamic characteristics change over time. In this paper, we report the develoment of new approaches to adaptive control based on the Proportional-Integral-Plus (PIP) controller applied to Nutrient Film Technique (NFT) Systems used in glasshouse horticulture.

FORECASTING AND CONTROL OF INTERURBAN TRAFFIC NETWORKS USING A STATE-SPACE FORMULATED TRAFFIC MODEL

This paper first presents a statistical ‘data assimilation’ approach to handling the large volume of real time measurements now available from instrumented traffic networks. The dynamic state-space modelling technique employed, herein called the Statistical Traffic Model (STM), transforms on-line roadside measurements of traffic flow into explicit assessments of the current and future state of an inter-urban road network, and provides transport management with a tool for monitoring, prediction and control. The paper then goes on to discuss the application of adaptive Proportional-Integral-Plus (PIP) control systems to a non-linear STM simulation of the Amsterdam ring road.

Modelling and control design for open top chambers used in plant physiology climate change experiments

Abstract The paper describes the identification of a control model for carbon dioxide concentration in an Open-Top Chamber (OTC) used in plant physiology climate change experiments. This model is used to design an advanced, model-based, gain-scheduled controller using the Proportional-Integral-Plus (PIP) control design methodology developed by Young et al . (1987). Prior to implementation, the controller is evaluated by simulation, utilising realistic disturbance data

Multivariable DELTA (δ) Operator Control in a Plant Physiology Experiment

Abstract This paper describes the use of the True Digital Control (TDC) approach (Young, et al ., 1987) in the design, simulation and implementation of Proportional-Integral-Plus (PIP) control of the temperature and relative humidity within an enclosed plant physiology cabinet. Control of the temperature and relative humidity was originally carried out using separate single-input single-output (SISO) controllers running simultaneously (McKenna, et al ., 1997). Due to the high degree of cross coupling which exists between these two channels it is more appropriate to use an optimal linear quadratic (LQ) design philosophy based upon the estimation of a multi-input multi-output (MIMO) model.

The Modelling and Multivariable Control of Glasshouse Systems

Abstract The paper presents a multivariable simulation model of a glasshouse microclimate. A linear reduced order model is obtained using identification techniques. This control model is used as the basis of a True Digital Control design for temperature in the glasshouse. Finally multivariable extensions to the glasshouse control design methodology are discussed.

A CAD Program for Direct Digital Pole-assignment System Design

Abstract The paper describes the main features of a CAD program which is being developed for the design of true digital control (TDC) systems, based on a new method of input-output, state variable feedback pole-assignment. The program provides the user with assistance at all stages in the design process; from multivariable model identification and estimation to final control system design and evaluation. Discretetime, MIMO model identification and estimation is achieved by the use of recursive instrumental variable (IV) methods. The control system design is based on the definition of a particular non-minimal state space (NMSS) model form, obtained directly from the identification/estimation results. The state vector of this NMSS model is composed only of the present and past sampled values of input and output variables, together with certain integral-of-error states, which ensure “type-1” servomechanism performance.