Notification:
We are currently experiencing intermittent issues impacting performance. We apologize for the inconvenience.
By Topic

Control Theory and Applications, IEE Proceedings -

Issue 3 • Date May 2000

Filter Results

Displaying Results 1 - 17 of 17
  • Multirate-sampled digital feedback system design via a predictive control approach

    Publication Year: 2000 , Page(s): 293 - 302
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (740 KB)  

    Utilising a model-based predictive control methodology, the paper describes the synthesis of two-degrees-of-freedom digital controllers in which either the control or measured variable is relatively fast-sampled. In contrast with previous related contributions, which were predicated upon state-space representations and viewed the use of unconventional sampling strategies as a means of providing additional flexibility in pole-assignment, input-output models are employed exclusively and in this context multirate sampling is regarded as a prospective design or implementational aid. With respect to the latter consideration, it is demonstrated that the intrinsic low pass filtering nature of fast output signal-sampled predictive compensators significantly reduces the sensitivity of a digital feedback system to measurement noise View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Neurofuzzy network model construction using Bezier-Bernstein polynomial functions

    Publication Year: 2000 , Page(s): 337 - 343
    Cited by:  Papers (1)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (680 KB)  

    Neurofuzzy modelling systems combine fuzzy logic with quantitative artificial neural networks via a concept of fuzzification by using a fuzzy membership function usually based on B-splines and algebraic operators for inference, etc. The paper introduces a neurofuzzy model construction algorithm using Bezier-Bernstein polynomial functions as basis functions. The new network maintains most of the properties of the B-spline expansion based neurofuzzy system, such as the non-negativity of the basis functions, and unity of support but with the additional advantages of structural parsimony and Delaunay input space partitioning, avoiding the inherent computational problems of lattice networks. This new modelling network is based on the idea that an input vector can be mapped into barycentric co-ordinates with respect to a set of predetermined knots as vertices of a polygon (a set of tiled Delaunay triangles) over the input space. The network is expressed as the Bezier-Bernstein polynomial function of barycentric co-ordinates of the input vector. An inverse de Casteljau procedure using backpropagation is developed to obtain the input vector's barycentric co-ordinates that form the basis functions. Extension of the Bezier-Bernstein neurofuzzy algorithm to n-dimensional inputs is discussed followed by numerical examples to demonstrate the effectiveness of this new data based modelling approach View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Robust frequency shaping sliding mode control

    Publication Year: 2000 , Page(s): 312 - 320
    Cited by:  Papers (8)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (556 KB)  

    Frequency shaping, linked with linear quadratic optimal and sliding mode control, is a technique for controlling systems with uncertainties. The authors propose some methods for designing the sliding surface and sliding mode control when the LQ weighting functions are not constant at all frequencies. Furthermore, they introduce conditions for which the spectrum of the original reduced system is a subset of the spectrum of the augmented system, and develop an iterative optimal construction procedure for the sliding mode View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Decentralised control of multimachine power systems with guaranteed performance

    Publication Year: 2000 , Page(s): 355 - 365
    Cited by:  Papers (19)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (648 KB)  

    Focuses on a robust decentralised excitation control of multimachine power systems. The authors are concerned with the design of a decentralised state feedback controller for the power system to enhance its transient stability and ensure a guaranteed level of performance when there exist variations of generator parameters due to changing load and/or network topology. It is shown that the power system can be modelled as a class of interconnected systems with uncertain parameters and interconnections. The authors develop a guaranteed cost control technique for the interconnected system using a linear matrix inequality (LMI) approach. A procedure is given for the minimisation of the cost by employing the powerful LMI tool. The proposed controller design is simulated for a three-machine power system example. Simulation results show that the decentralised guaranteed cost control greatly enhances the transient stability of the power system in the face of various operating points, faults in different locations or changing network parameters View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Design of dynamic neural observers

    Publication Year: 2000 , Page(s): 257 - 266
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (696 KB)  

    A design of a nonlinear dynamic observer is proposed for determining the states of a nonlinear system. The design method uses a multi-layered feedforward neural network (MFNN) to approximate the nonlinear Kalman gain. Two different criteria are proposed for the network training. The training is based on a gradient descent algorithm that uses block partial derivatives. Simulation results on Van der Pol's equation and the classical inverted pendulum model are presented to validate the usefulness of the scheme View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Polynomial LQG synthesis of subrate digital feedback systems

    Publication Year: 2000 , Page(s): 247 - 256
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (764 KB)  

    Describes the optimal synthesis of digital feedback systems in which the plant output signal is sampled at a faster rate than the control is activated. The design methodology is predicated upon the extension of the `polynomial equations' approach to multirate-sampled configurations via the use of input-output models constituting sets of cyclically time-varying difference equations View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Resilient guaranteed cost control to tolerate actuator faults for discrete-time uncertain linear systems

    Publication Year: 2000 , Page(s): 277 - 284
    Cited by:  Papers (7)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (484 KB)  

    Studies a discrete-time resilient guaranteed cost control (GCC) design that tolerates some forms of controller gain uncertainties that are closely related to the actuator fault. The systems under consideration are discrete-time uncertain linear systems with norm-bounded structured uncertainties in both the state and input matrices. The resilient GCC designs guarantee that the cost of the closed-loop systems be within a certain bound for all these admissible uncertainties. A numerical example is given to illustrate the design method View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Anti-windup schemes for uncertain nonlinear systems

    Publication Year: 2000 , Page(s): 321 - 329
    Cited by:  Papers (11)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (576 KB)  

    Several processes are nonlinear and operate under input constraints. Their effective control requires model-based techniques whose performance is often affected by modelling uncertainties. The paper addresses the control of single-input single-output nonlinear systems with input constraints in the presence of modelling errors. A nonlinear internal model control based on input output linearisation is designed and an internal model control with antiwindup which removes the nonlinear mismatch due to input constraints is given. An adaptive internal model control with anti-windup to account for parameter uncertainty and an augmented internal model control with anti-windup to attenuate the effect of modelling errors, are proposed and analysed theoretically. Simulation results on temperature control of a continuous stirred tank reactor demonstrate the benefits of the proposed anti-windup schemes View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Counterexamples for sufficient and necessary conditions of internal stability

    Publication Year: 2000 , Page(s): 371 - 372
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (100 KB)  

    The authors discuss sufficient and necessary conditions guaranteeing the internal stability of systems. Counterexamples are constructed, and it is shown that conditions given by some current literature are not sufficient. The rigorous version is then given View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Recursive least determinant self-tuning regulator

    Publication Year: 2000 , Page(s): 285 - 292
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (524 KB)  

    An algorithm for the self-tuning regulator of a Box-Jenkins model control system is suggested. The algorithm calculates the control action by minimising the determinant of a positive definite matrix formed by the values of the input and output variables. The algorithm will gradually reduce a sequence of these matrices to a singular matrix. Under this condition, the parameters of the regulator can be obtained from an eigenvector corresponding to the zero eigenvalue of this matrix. The algorithm does not need to know the delay of the process dynamics which is its strength over the algorithm of the recursive least squares self-tuning regulator algorithm View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Multi-objective genetic optimisation of GPC and SOFLC tuning parameters using a fuzzy-based ranking method

    Publication Year: 2000 , Page(s): 344 - 354
    Cited by:  Papers (4)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (916 KB)  

    A multi-objective genetic algorithm is developed for optimising the tuning parameters relating to the generalised predictive control (GPC) and performance index table of the self-organising fuzzy logic (SOFLC) algorithms, using a multi-objective ranking method based on fuzzy logic theory. A comparative study with more traditional Pareto, average and minimum distance ranking methods shows that the proposed method is superior. The study shows that the approach leads to a more effective set of tuning parameters, especially those relating to the important observer polynomial for GPC and to a good reference trajectory for SOFLC. Up to two objective functions were used in the study, although the method can be extended to more objectives. A nonlinear muscle-relaxant anaesthesia model is used as a case study to demonstrate the robustness of the method View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Adaptive inverse control algorithm for shock testing

    Publication Year: 2000 , Page(s): 267 - 276
    Cited by:  Papers (4)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (800 KB)  

    An adaptive inverse control algorithm is proposed for shock testing an arbitrary specimen using an electrodynamic actuator. The purpose is to ascertain whether the specimen can survive and continue to function under severe shock conditions. The main difficulty in shock control is that the specimen dynamics vary significantly and a control algorithm is required that adapts to the characteristics of a new specimen. The control algorithm used is the adaptive inverse control method which approximates an inverse model of the loaded shaker with a finite impulse response adaptive filter, such that the reference input is reproduced at the shaker output. The standard filtered-x least mean square control structure used in the adaptive inverse control algorithm is modified to a block-processing structure, with the frequency-domain adaptive filter as the adaptation algorithm. Practical results show that the filtered-x frequency-domain adaptive filter control algorithm allows convergence of the shaker output to the assigned reference shock pulse View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Neural controllers for nonlinear state feedback L2-gain control

    Publication Year: 2000 , Page(s): 239 - 246
    Cited by:  Papers (2)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (648 KB)  

    Design of an L2-gain disturbance rejection neural controller for nonlinear systems is presented. The control input is generated from a radial basis network, which is trained offline such that a computed partial derivative of the network output satisfies a Hamilton-Jacobi inequality. Once the network is successfully trained for a given manifold in the state space, the closed-loop system ensures a finite gain between the system disturbance and the system input-output as long as the system states remain within the state manifold. The proposed method may also be applied to obtain an H controller View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Neural adaptive tracking controller for robot manipulators with unknown dynamics

    Publication Year: 2000 , Page(s): 366 - 370
    Cited by:  Papers (2)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (360 KB)  

    A neural network (NN)-based adaptive control law is proposed for the tracking control of an n-link robot manipulator with unknown dynamic nonlinearities. Basis-function-like networks are employed to approximate the plant nonlinearities, and the bound on the NN reconstruction error is assumed to be unknown. The proposed NN-based adaptive control approach integrates the NN approach and an adaptive implementation of the discrete variable structure control, with a simple estimation mechanism for the upper bound on the NN reconstruction errors and an additional control input as a function of the estimate. Lyapunov stability theory is used to prove the uniform ultimate boundedness of the tracking error, and simulation results demonstrate the applicability of the proposed method to achieve desired performance View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Criterion for global exponential stabilisability of a class of nonlinear control systems via an integral manifold approach

    Publication Year: 2000 , Page(s): 330 - 336
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (444 KB)  

    A simple necessary and sufficient criterion, without the so-called `interconnection conditions', for the global exponential stabilisability of a general class of nonlinear singularly perturbed systems is proposed. A globally exponentially stabilising composite feedback control is also proposed such that the chosen design manifold becomes an exact integral manifold and the trajectories of the closed-loop systems, starting from any initial states, are steered along the integral manifold to the origin for all sufficiently small singular perturbation parameters. The composite Lyapunov function technique is adopted in the paper View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Stability analysis of iterative optimal control algorithms modelled as linear unit memory repetitive processes

    Publication Year: 2000 , Page(s): 229 - 238
    Cited by:  Papers (5)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (768 KB)  

    The theory of unit memory repetitive processes is used to investigate local convergence and stability properties of algorithms for the solution of discrete optimal control problems. In particular, the properties are addressed of a method for finding the correct solution of an optimal control problem where the model used for optimisation is different from reality. Limit profile and stability concepts of unit memory linear repetitive process theory are employed to demonstrate optimality and to obtain necessary and sufficient conditions for convergence. Two main stability theorems are obtained from different approaches and their equivalence is proved. The theoretical results are verified through simulation and numerical analysis, and it is demonstrated that repetitive process theory provides a useful tool for the analysis of iterative algorithms for the solution of dynamic optimal control problems View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Neural network-based H tracking control for robotic systems

    Publication Year: 2000 , Page(s): 303 - 311
    Cited by:  Papers (10)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (656 KB)  

    An adaptive H tracking control design is proposed for robotic systems under plant uncertainties and external disturbances. Three important control design techniques, i.e. nonlinear H tracking theory, variable structure control algorithm and neural network control design, are combined to construct a hybrid adaptive-robust tracking control scheme which ensures that the joint positions track the desired reference signals. It is shown that an H tracking control is achieved in the sense that all variables of the closed-loop system are bounded and the effect due to the external disturbance on the tracking error can be attenuated to any pre-assigned level. The solution of H control performance relies only on an algebraic Riccati-like matrix equation. A simple design algorithm is proposed such that the proposed adaptive neural network-based H tracking controller can easily be implemented. A simulation example demonstrates the effectiveness of the proposed control algorithm View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.