Unknown Plant (unknown + plant)

Distribution by Scientific Domains

Selected Abstracts

Online trained support vector machines-based generalized predictive control of non-linear systems

S. Iplikci
Abstract In this work, an online support vector machines (SVM) training method (Neural Comput. 2003; 15: 2683,2703), referred to as the accurate online support vector regression (AOSVR) algorithm, is embedded in the previously proposed support vector machines-based generalized predictive control (SVM-Based GPC) architecture (Support vector machines based generalized predictive control, under review), thereby obtaining a powerful scheme for controlling non-linear systems adaptively. Starting with an initially empty SVM model of the unknown plant, the proposed online SVM-based GPC method performs the modelling and control tasks simultaneously. At each iteration, if the SVM model is not accurate enough to represent the plant dynamics at the current operating point, it is updated with the training data formed by persistently exciting random input signal applied to the plant, otherwise, if the model is accepted as accurate, a generalized predictive control signal based on the obtained SVM model is applied to the plant. After a short transient time, the model can satisfactorily reflect the behaviour of the plant in the whole phase space or operation region. The incremental algorithm of AOSVR enables the SVM model to learn the new training data pair, while the decremental algorithm allows the SVM model to forget the oldest training point. Thus, the SVM model can adapt the changes in the plant and also in the operating conditions. The simulation results on non-linear systems have revealed that the proposed method provides an excellent control quality. Furthermore, it maintains its performance when a measurement noise is added to the output of the underlying system. Copyright 2006 John Wiley & Sons, Ltd. [source]

Designing predictors for MIMO switching supervisory control

Edoardo Mosca
Abstract The paper studies the problem of inferring the behaviour of a linear feedback loop made up by an uncertain MIMO plant and a given candidate controller from data taken from the plant possibly driven by a different controller. In such a context, it is shown here that a convenient tool to work with is a quantity called normalized discrepancy. This is a measure of mismatch between the loop made up by the unknown plant in feedback with the candidate controller and the nominal ,tuned-loop' related to the same candidate controller. It is shown that discrepancy can in principle be obtained by resorting to the concept of a virtual reference, and conveniently computed in real time by suitably filtering an output prediction error. The latter result is of relevant practical value for on-line implementation and of paramount importance in switching supervisory control of uncertain plants, particularly in the case of a coarse distribution of candidate models. Copyright 2001 John Wiley & Sons, Ltd. [source]

Support vector machines-based generalized predictive control

S. Iplikci
Abstract In this study, we propose a novel control methodology that introduces the use of support vector machines (SVMs) in the generalized predictive control (GPC) scheme. The SVM regression algorithms have extensively been used for modelling nonlinear systems due to their assurance of global solution, which is achieved by transforming the regression problem into a convex optimization problem in dual space, and also their higher generalization potential. These key features of the SVM structures lead us to the idea of employing a SVM model of an unknown plant within the GPC context. In particular, the SVM model can be employed to obtain gradient information and also it can predict future trajectory of the plant output, which are needed in the cost function minimization block. Simulations have confirmed that proposed SVM-based GPC scheme can provide a noticeably high control performance, in other words, an unknown nonlinear plant controlled by SVM-based GPC can accurately track the reference inputs with different shapes. Moreover, the proposed SVM-based GPC scheme maintains its control performance under noisy conditions. Copyright 2006 John Wiley & Sons, Ltd. [source]

An Automatic Building Approach To Special Takagi-Sugeno Fuzzy Network For Unknown Plant Modeling And Stable Control

Chia-Feng Juang
ABSTRACT In previous studies, several stable controller design methods for plants represented by a special Takagi-Sugeno fuzzy network (STSFN) have been proposed. In these studies, the STSFN is, however, derived directly from the mathematical function of the controlled plant. For an unknown plant, there is a problem if STSFN cannot model the plant successfully. In order to address this problem, we have derived a learning algorithm for the construction of STSFN from input-output training data. Based upon the constructed STSFN, existing stable controller design methods can then be applied to an unknown plant. To verify this, stable fuzzy controller design by parallel distributed compensation (PDC) method is adopted. In PDC method, the precondition parts of the designed fuzzy controllers share the same fuzzy rule numbers and fuzzy sets as the STSFN. To reduce the controller rule number, the precondition part of the constructed STSFN is partitioned in a flexible way. Also, similarity measure together with merging operation between each neighboring fuzzy set are performed in each input dimension to eliminate the redundant fuzzy sets. The consequent parts in STSFN are designed by correlation measure to select only the significant input terms to participate in each rule's consequence and reduce the network parameters. Simulation results in the cart-pole balancing system have shown that with the proposed STSFN building approach, we are able to model the controlled plant with high accuracy and, in addition, can design a stable fuzzy controller with small parameter number. [source]