Adaptive Controllers (adaptive + controllers)

Distribution by Scientific Domains


Selected Abstracts


Adaptive identification of two unstable PDEs with boundary sensing and actuation

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 2 2009
Andrey Smyshlyaev
Abstract In this paper we consider a problem of on-line parameter identification of parabolic partial differential equations (PDEs). In the previous study, on the actuation side, both distributed (SIAM J. Optim. Control 1997; 35:678,713; IEEE Trans. Autom. Control 2000; 45:203,216) and boundary (IEEE Trans. Autom. Control 2000; 45:203,216) actuations were considered in the open loop, whereas for the closed loop (unstable plants) only distributed one was addressed. On the sensing side, only distributed sensing was considered. The present study goes beyond the identification framework of (SIAM J. Optim. Control 1997; 35:678,713; IEEE Trans. Autom. Control 2000; 45:203,216) by considering boundary actuation for the unstable plants, resulting in the closed-loop identification, and also introducing boundary sensing. This makes the proposed technique applicable to a much broader range of practical problems. As a first step towards the identification of general reaction,advection,diffusion systems, we consider two benchmark plants: one with an uncertain parameter in the domain and the other with an uncertain parameter on the boundary. We design the adaptive identifier that consists of standard gradient/least-squares estimators and backstepping adaptive controllers. The parameter estimates are shown to converge to the true parameters when the closed-loop system is excited by an additional constant input at the boundary. The results are illustrated with simulations. Copyright © 2008 John Wiley & Sons, Ltd. [source]


Intelligent control using multiple neural networks

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 6 2003
Lingji Chen
Abstract In this paper a framework for intelligent control is established to adaptively control a class of non-linear discrete-time dynamical systems while assuring boundedness of signals. A linear robust adaptive controller and multiple non-linear neural network based adaptive controllers are used, and a switching law is suitably defined to switch between them, based upon their performances in predicting the plant output. Boundedness of signals is established with minimum requirements on the parameter adjustment mechanisms of the neural network controllers, and thus the latter can be used in novel ways to better detect changes in the system being controlled, and to initiate fast adaptation. Simulation studies show the effectiveness of the proposed approach. Copyright © 2003 John Wiley & Sons, Ltd. [source]


Mixture-based adaptive probabilistic control

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 2 2003
Miroslav Kárný
Abstract Quasi-Bayes algorithm, combined with stabilized forgetting, provides a tool for efficient recursive estimation of dynamic probabilistic mixture models. They can be interpreted either as models of closed-loop with switching modes and controllers or as a universal approximation of a wide class of non-linear control loops. Fully probabilistic control design extended to mixture models makes basis of a powerful class of adaptive controllers based on the receding-horizon certainty equivalence strategy. Paper summarizes the basic elements mentioned above, classifies possible types of control problems and provides solution of the key one referred to as ,simultaneous' design. Results are illustrated on mixtures with components formed by normal auto-regression models with external variable (ARX). Copyright © 2003 John Wiley & Sons, Ltd. [source]


Adaptive AQM controllers for IP routers with a heuristic monitor on TCP flows

INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS, Issue 1 2006
Yang Hong
Abstract We propose adaptive proportional (P) and proportional-integral (PI) controllers for Active Queue Management (AQM) in the Internet. We apply the classical control theory in the controller design and choose a proper phase margin to achieve good performance of AQM. We have identified a simple heuristic parameter that can monitor the changes of network environment. Our adaptive controllers would self-tune only when the dramatic change in the network parameters drift the monitoring parameter outside its specified interval. When compared to P controller, a PI controller has the advantage of regulating the TCP source window size by adjusting the packet drop probability based on the knowledge of instantaneous queue size, thus steadying the queue size around a target buffer occupancy. We have verified our controllers by OPNET simulation, and shown that with an adaptive PI controller applied, the network is asymptotically stable with good robustness. Copyright © 2005 John Wiley & Sons, Ltd. [source]


Adaptive exponential stabilization of mobile robots with unknown constant-input disturbance

JOURNAL OF FIELD ROBOTICS (FORMERLY JOURNAL OF ROBOTIC SYSTEMS), Issue 6 2001
Weiguo Wu
This paper concentrates on the discussions on stabilization of mobile robots with unknown constant-input disturbance. Continuous time-varying adaptive controllers are designed for mobile robots in a chain-form by using Lyapunov approach. With the property of homogeneous systems, uncertain mobile robots governed by the proposed control algorithms become homogeneous of order 0 to achieve exponential stability. Simulation results validate the theoretical analysis. © 2001 John Wiley & Sons, Inc. [source]


Multiple UPFC damping control scheme using ANN coordinated adaptive controllers,

ASIAN JOURNAL OF CONTROL, Issue 5 2009
Tsao-Tsung Ma
Abstract This paper presents a novel design of an adaptive damping control scheme using artificial neural network (ANN) coordinated multiple unified power flow controllers (UPFCs). In this study, a centralized global control scheme is proposed in which three UPFCs are first assumed to be strategically installed in the system to achieve a steady state power flow control objective, then utilized to demonstrate the proposed control scheme in enhancing the damping of low frequency electromechanical oscillations exhibited by a three-area, six-machine power system. The coordination of controllers is accomplished by a genetic algorithm based tuning process that is based on considering various system operating conditions and minimizing a set of predefined coordinated damping performance indices (CDPI). The task of real-time adaptation of system uncertainties is carried out using a trained ANN as an adaptive coordinator to achieve the robust control objectives. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source]


DIRECT ADAPTIVE CONTROL FOR NONLINEAR MATRIX SECOND-ORDER SYSTEMS WITH TIME-VARYING AND SIGN-INDEFINITE DAMPING AND STIFFNESS OPERATORS

ASIAN JOURNAL OF CONTROL, Issue 1 2007
Wassim M. Haddad
ABSTRACT A direct adaptive control framework for a class of nonlinear matrix second-order systems with time-varying and sign-indefinite damping and stiffness operators is developed. The proposed framework guarantees global asymptotic stability of the closed-loop system states associated with the plant dynamics without requiring any knowledge of the system nonlinearities other than the assumption that they are continuous and bounded. The proposed adaptive control approach is used to design adaptive controllers for suppressing thermoacoustic oscillations in combustion chambers. [source]