Home About us Contact
|
Home About us Contact | ||
|
|
||
Generalized Predictive Control (generalized + predictive_control)
Selected AbstractsImprovement of tracking performance in designing a GPC-based PID controller using a time-varying proportional gainIEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, Issue 4 2006Takao Sato An effective design method of a proportional-integral-derivative (PID) controller is proposed. The PID parameters of the PID controller are designed on the basis of a generalized predictive control (GPC) law. The PID controller has a time-varying proportional gain, and the PID parameters are designed using the future reference trajectory of the GPC. Finally, numerical examples are shown for illustrating the effectiveness of the proposed method. © 2006 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [source] Online trained support vector machines-based generalized predictive control of non-linear systemsINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 10 2006S. 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] Constrained closed-loop control of depth of anaesthesia in the operating theatre during surgeryINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 5 2005M. Mahfouf Abstract The constrained version of generalized predictive control (GPC) which employs the quadratic programming (QP) approach is evaluated for on-line administration of an anaesthetic drug in the operating theatre during surgery. In the first instance, a patient simulator was developed using a physiological model of the patient and the necessary control software was validated via a series of extensive simulation experiments. Such a validated system was then transferred into the operating theatre for a series of clinical evaluation trials. The clinical trials, which were performed with little involvement of the design engineer, led to a good regulation of unconsciousness using fixed-parameters as well the adaptive version of the algorithm. Furthermore, the constrained algorithm displayed good robustness properties against disturbances such as high stimulus levels and allowed for safe and economically effective administration of the anaesthetic agent isoflurane. Copyright © 2005 John Wiley & Sons, Ltd. [source] Support vector machines-based generalized predictive controlINTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 17 2006S. 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] | ||||||||||