New Publications are available for Time-varying control systems
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New Publications are available now online for this publication.
Please follow the links to view the publication.Implementation of time-varying observers used in direct field orientation of motor drives by trapezoidal integration
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0162
The paper discusses the problem of implementing the state observers associated with direct field orientation (DFO) of motor drives using trapezoidal integration (Tustin method). Typically, the discrete-time equations of observers are obtained by emulating the continuous-time equations using the Euler method (forward rectangular rule). With Euler integration, the resulting equations are simple and the real-time implementation requires low computational effort. However, Euler-based observers become inaccurate if a small sampling time cannot used or if the motor drive operates at high frequency-this is because, as the sampling time increases, the Euler approximation of the integral starts losing more and more area from under the curve. The Tustin method (trapezoidal integration) offers an interesting alternative it is theoretically a more accurate integration method, however, it is more complicated. The paper discusses the emulation procedure required to discretize continuous-time observers based on trapezoidal integration. The permanent magnet synchronous motor (PMSM) is used as an example of a time-varying plant-the paper develops a trapezoidal integration based observer for the PMSM and compares this with an Euler-based observer in terms of computational complexity and performance. The two observers are simulated comparatively in order to establish the conditions when trapezoidal integration outperforms the Euler method. (6 pages)An intelligent integrated navigation and control solution for an unmanned surface craft
http://dl-live.theiet.org/content/conferences/10.1049/cp.2009.1686
An adaptive navigation and control algorithm is presented in this paper based on fuzzy logic and optimal control techniques and applied on an unmanned surface vehicle platform. The navigation system consists of an extended Kalman filter with time-varying parameters. Whilst the autopilots include a fuzzy logic based linear quadratic Gaussian controller and a model predictive controller optimized using a genetic algorithm. Both the controllers use the output of the adaptive navigation system as their feedback and therefore creates an integrated system. A multiple waypoint following scenario is considered and tested in real time. Experimental results are shown that demonstrate the efficacy of the proposed system. (6 pages)Delay-dependent robust stabilization for uncertain linear system
http://dl-live.theiet.org/content/conferences/10.1049/cp_20050313
The problem of delay-dependent robust stabilization for a class of uncertain linear systems with time-varying delay is investigated. The parameter uncertainty under consideration is norm bounded and possibly time-varying. The time delay considered here is assumed to be size-bounded but otherwise arbitrarily fast time-varying. Based on a special Lyapunov-Krasovakii functional, the delay-dependent stability condition is formulated in terms of linear matrix inequalities. The delay-independent result can be derived as a particular case for certain values of the tuning parameters. A state-feedback control law is also given such that the resultant closed-loop system is stable for admissible uncertainties. Two numerical examples are given to demonstrate the efficiency of the obtained results. The abstract of your paper goes here in bold. It is centered of width 4.5 inches with a line the width of the column above and below. Each proceedings paper must not exceed 6 pages, including diagrams and references.A generalisation of the 45° criterion for the stability of time-varying systems with non-real eigenvalues
http://dl-live.theiet.org/content/conferences/10.1049/cp_20050336
In this paper we derive a sufficient condition for the stability of second-order switched systems. The results extend and complete the previously presented 45° criterion. The stability condition is formulated in terms of the eigenvalues of the constituent systems and is therefore coordinate independent and easily applicable. Moreover two types of Lyapunov functions (a quadratic and a piecewise linear type) are employed to establish stability. In case of the existence of the piecewise linear Lyapunov function we show that the set of Lyapunov functions can be easily constructed in terms of the subsystems' parameters.Restricted structure control loop performance assessment and benchmarking
http://dl-live.theiet.org/content/conferences/10.1049/ic_20020225
The problem of restricted-structure benchmarking for multi-input, multi-output (MIMO) systems controlled by multi-loop PID controllers is formulated in state space. The particular criterion shown in this paper is that for a linear quadratic generalised predictive controller, which combines the properties of linear quadratic Gaussian with those of generalized predictive control. (5 pages)Second variation method and optimal control in the presence of model-reality differences
http://dl-live.theiet.org/content/conferences/10.1049/ic_19981066
This article concerns the finite horizon optimal control of continuous-time deterministic processes in the presence of model-reality differences. The latter occurs either due to the complexity of the real physical process and hence inability in accurate and exact modelling or the desire of the control engineers in employing simplified models, e.g. linear time varying models. A typical optimal control algorithm in the presence of model-reality differences may be as follows. A nominal control signal is applied to the process and all the states in the process are measured for the duration of time horizon (let us assume this is possible). Using the state measurements, one can construct a linear time-varying model for the small variations in state and control around the nominal trajectories, i.e. a small signal model. Now by solving a minimisation problem based on the linear time-varying model, one can calculate a new control signal which reduces the objective function compared to the previous step. The new control signal is applied to the process and all the calculations are repeated again. These calculations continue iteratively until the control signal converges to its optimal value. In this algorithm instead of using the exact physical model of the process, a linear time-varying model is used which is updated after each iteration. This makes the calculation of optimal control in each iteration much easier. (3 pages)Intelligent modelling, estimation and fusion
http://dl-live.theiet.org/content/conferences/10.1049/ic_19981028
Summary form only given. A precursor to control is modelling. For nonlinear, uncertain, time-varying, unknown dynamical processes, neurofuzzy algorithms have many useful properties including convergence in learning, real-time adaptability, and transparency. Unfortunately, they suffer from the curse of dimensionality, and recent research on parsimonious modelling schemas such as LOLIMOT, ASMOD, MASMOD, MENN, have shown how this problem can be effectively overcome. Equally, this method of data-based modelling coupled with local operating point models enables classical linear control and estimation algorithms to be applied directly to these processes. In this seminar the basic theory of intelligent modelling via neurofuzzy algorithms will be developed, and local models, local controllers, intelligent estimators and applications in modelling, control and estimation (tracking) for advanced transportation will be used to illustrate the basic principles. (1 page)A hybrid method for the optimal control of chemical processes
http://dl-live.theiet.org/content/conferences/10.1049/cp_19980352
A new hybrid method for the (open loop) optimal control of chemical processes is presented. This method combines stochastic and deterministic techniques in order to compensate for their weaknesses while enhancing their strengths. The significant advantages of this approach are demonstrated considering three difficult case studies.Prediction of chaotic time series using hidden Markov models
http://dl-live.theiet.org/content/conferences/10.1049/cp_19980438
This paper describes a methodology of prediction of a chaotic time series as an equivalent stochastic process. It is shown that there is theoretical justification for such a model, and a model is constructed analytically for a known simple chaotic mapping. Possible models for unknown chaotic systems along with methods for estimating their parameters from time series are suggested and their characteristics discussed.Adaptive stochastic path planning for robots in real-time
http://dl-live.theiet.org/content/conferences/10.1049/cp_19980478
The problem of the adaptive stochastic path planning for robots can be solved by means of some stochastic optimization and mathematical programming techniques. To make the method capable for online applications, the strategy of neural network approximation is considered.Dynamic modelling of a paper making process based on bilinear system modelling and genetic neural networks
http://dl-live.theiet.org/content/conferences/10.1049/cp_19980411
The dynamic modelling of the wet end of the paper machines has been recognised as a challenging problem due to its nonlinear, complex, time-varying, time-delayed, and multivariable interactive properties. This paper presents a methodology based on bilinear system modelling and multilayer perceptron (MLP) neural network for modelling of such a complex system. Genetic algorithm (GA) search and optimisation technique is proposed to train the neural network weights. This logical combination has advantages of both physical and genetic neural modellings.Adaptive control design for linear time-varying system based on internal model principle
http://dl-live.theiet.org/content/conferences/10.1049/cp_19980328
We propose a new adaptive control system design using internal model principle for a linear system with polynomial function parameters. In this method, we regard system parameters as variable disturbance and design an estimating law using the internal model of the disturbance so that the disturbance can be rejected. Our method has the features that the tracking error can converge to zero. Furthermore, we give a sufficient condition for the stability in terms of a small-gain theorem. We show that our proposed method relaxes the stability condition more than the conventional methods based on the passivity theorem. Finally, we present a numerical simulation to show the effectiveness of our system.Control of non-linear time-varying systems using fuzzy relational models
http://dl-live.theiet.org/content/conferences/10.1049/cp_19980202
The majority of chemical processes are non-linear in nature. Increasingly, nonlinear models are being used as key parts of chemical plant control schemes. A problem with any model is that it can become inaccurate over time and consequently for good control of time-varying processes some sort of model adaptation is required. In this paper a fuzzy relational model (FRM) is incorporated into a multivariable fuzzy internal model controller (FIMC). A mechanism for online adaptation of the model is described and implemented. Results are presented for a simulated MIMO system.Stable adaptive control of stochastic distributions and its application
http://dl-live.theiet.org/content/conferences/10.1049/cp_19980197
This short paper presents a stable adaptive control algorithm for the control of the output probability density function for unknown time-invariant stochastic systems. An online parameter estimation algorithm is constructed using the measured output probability density functions of the system. Based upon the same type of dynamic model proposed by Wang (1997, 1998), a functional weighted type performance function is employed in the formulation of the adaptive control algorithm. It is shown that the stability of the closed loop system can be guaranteed under certain conditions. An applicability study of the proposed algorithm to the solid flocculation control in paper making is included, where a simulated example is employed to illustrate the use of the developed control algorithm.Stability and optimality of robust stochastic multivariable self-tuning tracker
http://dl-live.theiet.org/content/conferences/10.1049/cp_19980427
This paper considers properties of Astrom-Wittenmark's self tuning tracker for MIMO systems described with the ARX model. It is supposed that the stochastic noise has the non-Gaussian distribution. Consequence of that fact is nonlinear transformation of tracking error in the direct adaptive minimum variance controller. System under consideration is minimum phase with different dimensions for input and output vectors. Using concept of Kronecker product it is possible to represent unknown parameters in the form of vector, so the tensor calculus is avoided. Global stability is proved without any modification of matrix gain in the recursive algorithm. Also, the assumption about the absolutely continuous finite-dimensional distributions and different modification of high frequency gain is discussed.Sliding mode control for robot manipulators using time-varying switching gain and boundary layer
http://dl-live.theiet.org/content/conferences/10.1049/cp_19980398
A methodology of smoothing the control output of a sliding mode controller for a robot manipulator is proposed. The method uses a time-varying switching gain and a time-varying boundary layer, which is a function of the tracking error is used to reduce the undesirable chattering while keeping the robust characteristic that rejects system uncertainties. Simulation results show that the proposed controller gives a good system performance in the face of uncertain system parameters and external disturbances.SISO and MIMO variable structure control of fixed bed bioreactors
http://dl-live.theiet.org/content/conferences/10.1049/cp_19980232
In this paper, SISO and MIMO robust variable structure controls of fixed bed bioreactors are developed. The process considered must regulate the nitrogen content of drinkable water at desired values imposed by international norms. Micro-organisms fixed in the reactor absorb the nutrients in such a way that the substrate concentrations decrease in the outflowing water. The addition of a carbon source is needed in this operation. A SISO variable structure control is used to regulate only the total concentration of nitrates and nitrites by acting either on the influent flow rate, or on the ethanol concentration. In order to optimise the addition of the ethanol which is the carbon source and regulate the ethanol concentration of drinkable water, a MIMO variable structure control is used. The complexity of a control problem is due to nonlinear and time varying behaviour of micro-organisms used for consuming harmful substrates. The performances of the control laws are illustrated by simulations.Learning and control by vector field modelling
http://dl-live.theiet.org/content/conferences/10.1049/cp_19980327
An online method is developed for learning a model of a dynamic process, which can be used to implement control through a form of feedback linearisation. Simulation results are presented to demonstrate the applicability of the method in control of nonlinear and time-varying systems.Orthogonal Lyapunov transformations and stability
http://dl-live.theiet.org/content/conferences/10.1049/cp_19980439
This paper shows how to determine pointwise orthogonal Lyapunov transformations, reveals their advantage over standard Lyapunov transformations for non-periodic time-varying systems, and outlines some potential applications for stability assessment of time-varying linear systems and nonlinear systems.Intelligent control toolkit for an advanced control system
http://dl-live.theiet.org/content/conferences/10.1049/cp_19980270
This paper describes the development of a genetic algorithm based nonlinear controller. It builds on the successful integration of the modelling capability of an artificial neural network approach within the advanced control package Connoisseur<sup xmlns="http://pub2web.metastore.ingenta.com/ns/">TM</sup>. The case for the long standing need for a generalised nonlinear controller for handling practical nonlinear and time varying systems is made. The motivation in terms of achieving tighter control, leading to increased efficiency and profitability are stressed. Existing techniques of gain scheduling and multiple models are briefly discussed, as well as their limitations. In the approach adopted in this paper the genetic algorithm based controller is employed to search for an optimal set of control outputs to minimise a given performance index. The paper gives examples of simulation studies and comments of the various factors that affect the performance of the controller and the practical implementation of the controller.Fault detection and isolation for a three tank system based on a bilinear model of the supervised process
http://dl-live.theiet.org/content/conferences/10.1049/cp_19980449
Time varying innovation generators combined with generalized likelihood ratio (GLR) tests are designed for detection and isolation of faults in a three tank system. This diagnosis system is based on a bilinear model of the supervised process. It is shown to work properly in a larger working range than a fault detection and isolation (FDI) system based on a linear model. As the faults enter in a bilinear way in the model, achieving exact decoupling of the residuals with respect to some of the faults is not possible. One has to resort to approximation methods such as the approach developed in Patton and Chen (1993). The whole FDI system is designed and tuned on the basis of a simulation of the three tank system. Next it is applied to actual pilot plant data and it is shown to perform well. To be able to detect temporary faults (namely fault appearance and disappearance) with the GLR test, a strategy based on the use of two Kalman filters running in parallel is used.A practical and useful self-learning fuzzy controller
http://dl-live.theiet.org/content/conferences/10.1049/cp_19980243
Describes the implementation and application tests of a simple and useful self-learning fuzzy controller. The proposed controller is able to perform a model-free control of time-variable plants with little prior information. Based on a simple and fast learning algorithm that emulates the human learning process, the fuzzy controller self-adapts its control surface at a rate of 150 updates per second. The present implementation was based on the inexpensive and standard 8-bit microcontroller Intel 8031, which makes the self-learning fuzzy controller very cost effective, even to small-scale embedded applications, like home appliances, for instance. Practical experiments using a servo-mechanical plant show the good performance of the intelligent fuzzy controller to regulate the plant output and to reject severe time variable disturbances.On the strict modelling for Stefan problems with random convection and its temperature control
http://dl-live.theiet.org/content/conferences/10.1049/cp_19980440
The paper considers the strict modelling of Stefan problems proposed by Rubinstein (1971) with random convection, and the temperature control problems for the proposed model. It is well known that the parabolic equation has an infinite thermal propagation speed. In order to avoid this physically unacceptable aspect, the heat conduction model of the hyperbolic type is derived from the physical point of view. First, taking the randomness in the velocity of the fluid by convection into consideration, the hyperbolic heat conduction model with random convection is proposed. Next, the free boundary problem for the proposed model is studied. It is shown that the considered free boundary problem is formulated by the stochastic variational inequality of a new type. The existence and uniqueness theorem of the solution to the stochastic variational inequality is given. Finally, the temperature control problem for the hyperbolic Stefan system with random convection is considered and a simple but very useful temperature control method is proposed.A stochastic method for neural-adaptive control of multi-modal nonlinear systems
http://dl-live.theiet.org/content/conferences/10.1049/cp_19980200
The multiple model adaptive control approach is extended to a class of nonlinear stochastic systems whose underlying functions are unknown and which can change arbitrarily in time. Gaussian radial basis function neural networks are used to learn the nonlinear functions characterising the different plant modes online, without resorting to a separate learning phase. Function estimation, mode change detection and control signal generation are based on probabilistic techniques utilising concepts of Kalman filtering, the multiple model algorithm and dual control.Robust PIP control of multivariable stochastic systems
http://dl-live.theiet.org/content/conferences/10.1049/ic_19971286
This paper discusses the development of robust versions of multivariable non-minimal state space design procedures, for the proportional-integral-plus (PIP) control systems previously introduced by Young et al. (1994). Robust control design aims to ensure good closed loop performance under difficult conditions, such as model uncertainty and “worst case” disturbance inputs. In this regard, the practical utility of the PIP controllers are evaluated on two systems, namely a multivariable coupled drive rig and the IFAC93 benchmark. The first of these examples is a laboratory scale plant representing a materials handling system, where control of speed and tension is required; while the latter is a stochastic simulation, whose parameters vary randomly within specified ranges. (3 pages)Using modal analysis to guarantee the closed loop behaviour of a class of hybrid systems
http://dl-live.theiet.org/content/conferences/10.1049/ic_19961367
Several issues in the control of a class of hybrid systems are discussed. The class of hybrid systems considered are linear time-varying plants controlled by means of switching between homogeneous controllers. In this paper modal analysis is used to analyse the behaviour of switching systems with real eigensystems. Conditions guaranteeing the stability of such systems are presented and guidelines for shaping their transient response given. It is demonstrated that for SISO systems, controlled by means of dynamic state feedback, a local state approach can be employed which results in a closed loop system with guaranteed asymptotic stability irrespective of switching frequency. The theory presented is used in the design of a hybrid speed control system for a real automobile which guaranteed nominal stability and good transient response properties. (5 pages)Path-following and point-stabilization control laws for a wheeled mobile robot
http://dl-live.theiet.org/content/conferences/10.1049/cp_19960668
Two main problems, concerning wheeled mobile robots, have attracted extensively the attention of many authors in the literature: the path following and the stabilization problems. In this paper, we present a nonlinear pure-state feedback based on partial state feedback linearization and Lyapunov method for the path following problem, and a discontinuous time-varying state feedback for the stabilization problem. The desired path is described by the motion of a fictitious reference robot with the same kinematics constraints as the real mobile robot.Using GBT for MATLAB, version 5.1, in identification and control
http://dl-live.theiet.org/content/conferences/10.1049/cp_19960555
The Geometric Bounding Toolbox (GBT) is a set of routines (m-files) for MATLAB which can also be used in robust modelling and control problems of industrial dynamic systems. The paper gives some simple examples of applications for robust controller settings of PID or pole placement, robust parameter tracking of time varying parameters, testing for model order reduction. Beyond the applications presented, other major application areas are in nonlinear control of linear plants under state and input constraints (D.Q. Mayne and W.S. Schroeder, 1994) and worst case dual control for use in adaptive control problems (S.M. Vers, 1995).Robustness analysis of a remotely piloted vehicle
http://dl-live.theiet.org/content/conferences/10.1049/cp_19960650
The stability robustness of the longitudinal dynamics of a closed-loop remotely piloted vehicle-the X7 half scale flying model of the ARMOR RPV-with uncertain parameters is considered. Techniques based on Lyapunov analysis and Gershgorin's theorem are used to predict a guaranteed parameter space for closed-loop stability. A large stochastic sampling is used to assess the conservatism of the results.Robust tracking control design for a class of uncertain nonlinear systems
http://dl-live.theiet.org/content/conferences/10.1049/cp_19960567
The problem of tracking control design for a class of uncertain nonlinear systems is considered in this paper. The design approach adopted in this paper is similar to the one used by Wang and Goodall (1996). The main contribution of the paper is that the bounding condition on `residual' uncertainty is modified so it is easier to verify than the condition given in Wang and Goodall (1996). The analysis shows that the controller developed in the paper is more robust if the function thetas(t,e)>1. To encompass all possible realizations of uncertainty, differential inclusions are used to describe the class of uncertain systems in this paper. In particular, the uncertain systems are modelled as nonlinear perturbations to a known nominal nonlinear affine control system. The nonlinear tracking problem is reformulated to the problem of stabilizing nonlinear time-varying systems and then, a class of discontinuous feedback controls is developed such that the uncertain nonlinear system tracks desired trajectories to {0}.Analysis of relationship between chaotic dynamics and stochastic processes
http://dl-live.theiet.org/content/conferences/10.1049/cp_19960729
This paper presents an equivalence relation between chaotic and stochastic systems based on a combinatorial study of chaotic dynamics and stochastic processes. The study focuses on the dynamic density of chaotic systems and Brownian motion in stochastic systems. The equivalency in invariant measure and ergodicity between the two systems is defined. Based on the definition, the equivalence relation is studied and the equivalent stochastic system model of the chaotic system is derived analytically, which leads to an equivalent stochastic system model. The concept of the equivalency can be used to achieve system reconstruction using time series of chaotic systems and to develop a control strategy for control of chaotic dynamics.Optimal quadratic filtering of quantization noise in non-Gaussian systems
http://dl-live.theiet.org/content/conferences/10.1049/cp_19960705
This work deals with the problem of state estimation for a class of discrete time linear systems forced by non-Gaussian noise where the quantization on measured output is modeled, as usual, as additive noise having uniform probability distribution. The best linear estimate, computed through the Kalman filter, in this case may not give good results. To improve the covariance of the estimation error the best estimator with quadratic structure is developed in this paper. The optimal quadratic filter, proposed by Santis et al. (1995), is preliminarily introduced using a geometric approach. Then its application is shown in a case in which the state noise is strongly non-Gaussian to best appreciate the improvement w.r.t. standard linear filtering.A robust and recursive identification method for MISO Hammerstein model
http://dl-live.theiet.org/content/conferences/10.1049/cp_19960558
The problem of identification of MISO Hammerstein model in case of correlated measurement noise is addressed. Because of the special structure of this kind of model, global convergence of the proposed estimation algorithm is proved while the model is nonlinear in the parameters. The analysis is in fact a generalisation of the work by M. Boutayeb et al. (1996) and consists first in transforming the nonlinear model into an input-output one linear in parameters. Afterwards, four successive stages based on the pseudo-inverse technique, are derived and lead us to a consistent estimator of the initial realisation as well as the model of the noise. Accuracy and performances of the proposed technique are shown through numerical examples with different signal to noise ratio values.Frequency domain self-tuning for vibration control
http://dl-live.theiet.org/content/conferences/10.1049/ic_19960828
Synergetic interaction of identification and control has been investigated for a frequency domain approach to adaptive semi-active suspension scheme for vehicle vibration control. It is proved that the loop of identification and control can be closed while the adaptive scheme remains stable for time varying plant dynamics. The control scheme presented is a special type of dual controller-an online worst-case weakly-dual scheme. The paper outlines the hardware of the system including the adaptive semi-active vibration controller, and describes the adaptive control scheme and a result on stability. (5 pages)Self-tuning PID control structures
http://dl-live.theiet.org/content/conferences/10.1049/ic_19961463
There has been much work on self-tuning PID controllers. There are many possible structures for PID controllers. The basic concepts of PID control can be generalised within the same structure but allowing for the control of complicated dynamic systems using advanced control design algorithms. This structure arises naturally from the system description and does not need to be imposed artificially. The concept of PID control can be usefully generalised to make contact with recent methods such as internal-model control and generalised predictive control. One should not start with a PID controller and then decide how to tune it, but rather one should start with a rational design method and system model from which a (generalised) PID structure will then materialise. Recent advances in local model networks using multiple-model self-tuning PID controllers give a neat extension of the basic (generalised) PID structure to handle nonlinear or time-varying systems. The PID concept is alive and well. (4 pages)Extending process monitoring by event recognition
http://dl-live.theiet.org/content/conferences/10.1049/cp_19940665
Dealing with temporal aspects is one of the essential issues for the qualitative analysis of dynamic system behavior. This paper focuses on the recognition of situations defined by qualitative temporal relations in an incoming stream of quantitative sensor readings. We present an effective incremental recognition method based on hierarchical event structures and an algebraic treatment of temporal relations. The techniques are illustrated using example problems from the domain of driverless transport systems.Adaptive control based on special compensation methods for ill-modelled time-varying plants
http://dl-live.theiet.org/content/conferences/10.1049/cp_19940276
An adaptive controller based on some compensation methods that preserves stability for fast time-varying plants in the presence of bounded output disturbances and unmodelled dynamics is presented. The unknown plant parameters are estimated by a normalized algorithm with dead zone. The design is completed with a modified control law which adds an internal impulse to the system whenever the controllability of the estimated model is lost. This strategy confers a self-excitation capability to the system, so that it makes unnecessary the presence of external persistently exciting signals. As a result, the estimated models are sufficiently controllable by the underlying control law based on the special compensation methods and global stability is ensured. The performance of the given scheme is evaluated by simulations.Using expert systems for on-line data qualification and state variable estimation for an industrial fermentation process
http://dl-live.theiet.org/content/conferences/10.1049/cp_19940284
An industrial fed batch fermentation process is a nonlinear time-varying process. Important internal state variables such as biomass, substrate and product concentrations cannot be measured online and are usually determined by infrequent and time consuming off-line laboratory analysis. The online measurements are usually noisy and sometimes this leads to misinterpretation of the real situation inside the fermenter. These problems can lead to poor control of the batch and low productivity subsequently. To overcome these problems a real time expert system has been proposed which is based on the Poplog Flex real time expert system shell. The system is used to monitor the state variables of the process, diagnose any fault that might occur in the process, estimate the important unmeasurable state variables and to design a controller to control the state around a desired level. A neural network has been adopted for the online estimation of the unmeasurable state variables. Pattern recognition ideas have been used to improve the modelling ability of the neural network. Predictive control techniques have been used to control the state around a desired level. The model and the controller for the process have been designed and implemented within the Poplog Flex environment.Robust fault diagnosis of stochastic systems with unknown disturbances
http://dl-live.theiet.org/content/conferences/10.1049/cp_19940331
This paper studies the robust fault diagnosis of stochastic systems with unknown disturbances based on a full order observer. This observer can give disturbance decoupling minimum variance state estimation for time-varying systems with both noise and unknown disturbances. The existence condition and the design procedure are presented in the paper. The output estimation error with disturbance decoupling and minimum variance properties is used as a residual signal to diagnose faults. The developed method is applied to an illustrative example and simulation results show that the approach taken is able to detect faults reliably in the presence of both modelling errors and noise.Optimal diagnosis of changes in stochastic systems
http://dl-live.theiet.org/content/conferences/10.1049/cp_19940211
The purpose of this paper is to give a statistical approach to the change diagnosis (detection/isolation) problem. The change detection problem has received extensive research attention. On the contrary, change isolation is mainly an unsolved problem. The author considers a stochastic dynamical system with abrupt changes and investigates the multihypothesis extension of Lorden's results. The author introduces a joint criterion of optimality for the detection/isolation problem and then designs a change detection/isolation algorithm. The author also investigates the statistical properties of this algorithm. The author proves a lower bound for the criterion in a class of sequential change detection/isolation algorithms. It is shown that the proposed algorithm is asymptotically optimal in this class. The theoretical results are applied to the case of additive changes in linear stochastic models.Proportional-integral-plus (PIP) design for stochastic delta operator systems
http://dl-live.theiet.org/content/conferences/10.1049/cp_19940112
The paper shows how proportional-integral-plus (PIP) control system design for rapidly sampled systems described by delta (δ) operator transfer function models is based on the formulation of a special non-minimum state space (NMSS) representation, whose state variables are the discrete-time derivatives of the output and input signals. The stochastic version of this model provides the basis for linear quadratic Gaussian (LQG) control system design in the δ domain. As in the more conventional minimum state space situation, the state variable feedback PIP control law is obtained straightforwardly by the introduction of a Kalman filter and the application of the separation principle. Although the resulting PIP control system is simple to implement, its exploitation of state variable feedback (SVF) ensures the power and flexibility of its operation.The design of dynamics for cross-directional controllers in papermaking
http://dl-live.theiet.org/content/conferences/10.1049/cp_19940203
In order to improve the quality of the final product from the papermaking process, it is necessary to control the variations of properties such as basis weight (mass per unit area) and moisture content. In modern plants, it is common to use cross-directional control systems to regulate these properties in the direction perpendicular to the movement of the sheet. This paper describes the design of an observer that forms part of a control system for crossdirectional control. Starting from the underlying, continuous-time description of the plant, a “sample and hold” model is developed, where changes are made to the actuator set points on the basis of measurements from a scan and then these set points remain fixed until the next control action is applied. This is then augmented to include the effect of the time delay between a control action being applied at the actuators and its effect being seen by the gauge, as well as the “uneven” sampling introduced by the scanning gauge. The plant is described in terms of a large, time-varying, discrete, state space model, which has a high degree of structure. The design of an observer for the system which gives an estimate of the controllable states is described. By exploiting the structure of the model, the resulting observer has a particularly simple form which makes it easy to implement. An LQ regulator is designed for the process and the performance of the combined observer and controller is simulated.Efficient adaptive minimum variance control for discrete stochastic linear plant under unknown noise density: a NN-approach
http://dl-live.theiet.org/content/conferences/10.1049/cp_19940250
We propose the recursive procedure for neural network approximation of the optimal transformation function using indirect adaptive control algorithm. The convergence and asymptotic normality theorems formulated above represent a theoretical basis for implementation of the adaptive version of the asymptotically efficient algorithm for the problem considered.Model-based control for BEMS
http://dl-live.theiet.org/content/conferences/10.1049/cp_19940254
The stochastic multivariable identification technique is used to model the thermal and moisture behaviour of a full-scale test room with HVAC plant. Models are derived for the unfurnished and furnished conditions and these are shown to predict room temperature and relative humidity, over short and long terms, to a remarkable degree of accuracy. This vindicates the adoption of the time series based identification technique for the modelling of buildings. A robustness assessment shows that the models can adequately cover the range of operating conditions, with the exception of moisture prediction. It is therefore concluded that some degree of moisture-absorbing material is present during the identification phase in order that all the dynamics are properly treated and included in such models.A learning automaton methodology for control system design in active vehicle suspensions
http://dl-live.theiet.org/content/conferences/10.1049/cp_19940153
Concerns a control system design methodology, applied to the problem of active vehicle suspension system design. Although discussion is limited to a simple chassis system, the methodology is very general, and has the potential to be developed for much more complex industrial systems. The general approach combines concepts from stochastic optimal control with those of learning automata, and extends results obtained previously by the authors (1993). Active suspension system control has been the subject of much research, and many different ideas have been applied, e.g. optimal control, preview control and adaptive control. Two active suspension systems are to be considered. Suspension force actuation is under feedback control; in the first case an ideal full-bandwidth actuator will be assumed, incorporating full-state feedback for both sensor sets. In the second case, a more realistic configuration is considered, with limited bandwidth actuation, and one sensor set consisting of only a single bodymounted accelerometer. The learning automaton selects controller gains, evaluates a performance index, and updates its own internal states, in a way that tends to improve closed-loop system performance. It can be thought somewhat similar to optimization with 'hardware in the loop', although the automaton is required to work in a stochastic environment. The learning control may also be likened to self-tuning adaptive control; the crucial difference is that for practical application, the automaton does nor require any explicit system model.Continuous Time Controller Design
http://dl-live.theiet.org/content/books/ce/pbce039e
<p xmlns="http://pub2web.metastore.ingenta.com/ns/">State space analysis of systems. Modal control. Quadratic optimal control. Design of observers. Other selected design methods in the state space. Frequency domain analysis of multivariable systems. The Inverse Nyquist array method. The characteristic locus method. Frequency domain design by factorisation methods. Selected stochastic problems.</p>Wavelet-based adaptive sliding-mode control with H<sub xmlns="http://pub2web.metastore.ingenta.com/ns/">∞</sub> tracking performance for pneumatic servo system position tracking control
http://dl-live.theiet.org/content/journals/10.1049/iet-cta.2011.0434
An adaptive sliding-mode controller developed from an orthogonal Haar wavelet is proposed for a pneumatic servo control system experiment to overcome its non-linear and time-varying characteristics. To achieve real-time control of the pneumatic servo system, the orthogonal Haar wavelet is employed to quickly and accurately fit a non-linear function, thus bypassing the model-based prerequisite. The adaptive laws for the coefficients of the Haar wavelet series are derived from a Lyapunov function to guarantee system stability. One of the authors’ purposes is to enhance the stability, reliability and working performance of the pneumatic servo system. Hence, the <i xmlns="http://pub2web.metastore.ingenta.com/ns/">H</i><sub xmlns="http://pub2web.metastore.ingenta.com/ns/">∞</sub> tracking technique is incorporated into the conventional adaptive sliding-mode control method [Haar wavelet-based adaptive sliding-mode controller with <i xmlns="http://pub2web.metastore.ingenta.com/ns/">H</i><sub xmlns="http://pub2web.metastore.ingenta.com/ns/">∞</sub> tracking performance (HWB-ASMC + <i xmlns="http://pub2web.metastore.ingenta.com/ns/">H</i><sub xmlns="http://pub2web.metastore.ingenta.com/ns/">∞</sub>)] to attenuate the vibration of servo valve, which is caused by the chattering effect. The authors also show that the proposed HWB-ASMC + <i xmlns="http://pub2web.metastore.ingenta.com/ns/">H</i><sub xmlns="http://pub2web.metastore.ingenta.com/ns/">∞</sub> is robust against approximated errors, un-modelled dynamics and disturbances, and can reduce the control chattering problem. The advantages of the proposed method include that no system dynamic models being required to achieve the controller design and no trial-and-error efforts are needed in selecting an approximation function. Consequently, practical experiments on a pneumatic servo system are successfully implemented with different position tracking profiles, which validates the proposed method.Quadratic L2-gain performance linear parameter-varying realisation of parametric transfer functions and state-feedback gain scheduling control
http://dl-live.theiet.org/content/journals/10.1049/iet-cta.2011.0362
The study deals with the quadratic L2-gain performance linear parameter-varying realisation of parametric transfer functions and state-feedback gain scheduling control. It is shown that any parametric transfer function, which has L2-gain performance with a bound for all constant parameters values, admits a state space realisation guaranteeing quadratic L2-gain performance with the bound under arbitrary parameter variations. A reasonably general procedure is provided to find such a realisation with linear matrix inequality optimal technique. Furthermore, according to the realisation, the state-feedback gain scheduling control design is also dealt with. Finally, a simple numerical example is given to illustrate the efficiency of the proposed method.Finite-time stability for continuous-time switched systems in the presence of impulse effects
http://dl-live.theiet.org/content/journals/10.1049/iet-cta.2011.0529
The problem of finite-time stability for a class of continuous-time switched systems with impulse effects is studied in this article. A criterion is proposed which ensures that the system’s state trajectory remains in a bounded region of the state space over a pre-specified finite-time interval if the authors give a bound on the initial condition. Contrary to the existing results on finite-time stability of switched systems, the average dwell time approach, rather than the Lyapunov-based ones, is utilised to realise such a purpose. The difference between the finite-time stability and the Lyapunov stability is clearly shown. A numerical example is given to illustrate the proposed design method.Modelling and implementation of fixed switching frequency sliding mode controller for negative output elementary super lift Luo-converter
http://dl-live.theiet.org/content/journals/10.1049/iet-pel.2011.0442
This study presents a reduced-order state-space average model and a fixed switching frequency (FSF) sliding mode controller (SMC) for the negative output elementary super lift Luo-converter (NOESLLC) operated in continuous conduction mode for application fields requiring the constant power source such as medical, telecommunication, industrial, military etc. The NOESLLC is an attractive dc-dc converter that can provide high-voltage transfer gain. Owing to the time-varying switched mode operation, the dynamic performance of the NOESLLC becomes highly non-linear. In order to enhance the dynamics performance and output voltage regulation of the NOESLLC, the FSFSMC is developed. The proposed FSFSMC is more appropriate to the inherently variable-structured NOESLLC when represented in the reduced-order state-space average-based mathematical model. The three conditions of FSMSMC applicable to the NOESLLC, namely, existence, reaching and stability conditions are analysed. The performance of the developed controller is validated for its robustness to perform over a wide range of working conditions through both in MATLAB/Simulink models and as well as in the laboratory prototype with the comparative study of a typical proportional-integral-controller. Theoretical analysis, simulation and experimental results are presented to demonstrate the feasibility of the designed FSFSMC along with the complete systematic design procedure.