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Plant Output (plant + output)
Selected AbstractsNear optimal LQR performance for uncertain first order systemsINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 4 2004L. Luo Abstract In adaptive control, the objective is to provide stability and acceptable performance in the face of significant plant uncertainty. However, often there are large transients in the plant output and the control signal can become excessively large. Here, we consider the first order case with the plant parameters restricted to a compact set; we show how to design a (linear time-varying) adaptive controller which provides near optimal LQR performance. This controller is periodic with each period split into two parts: during the Estimation Phase, an estimate of the optimal control signal is formed; during the Control Phase, a suitably scaled estimate of this signal is applied to the system. We demonstrate the technique with a simulation and discuss the benefits and limitations of the approach. Copyright © 2004 John Wiley & Sons, Ltd. [source] Optimality for the linear quadratic non-Gaussian problem via the asymmetric Kalman filterINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 1 2004Rosario Romera Abstract In the linear non-Gaussian case, the classical solution of the linear quadratic Gaussian (LQG) control problem is known to provide the best solution in the class of linear transformations of the plant output if optimality refers to classical least-squares minimization criteria. In this paper, the adaptive linear quadratic control problem is solved with optimality based on asymmetric least-squares approach, which includes least-squares criteria as a special case. Our main result gives explicit solutions for this optimal quadratic control problem for partially observable dynamic linear systems with asymmetric observation errors. The main difficulty is to find the optimal state estimate. For this purpose, an asymmetric version of the Kalman filter based on asymmetric least-squares estimation is used. We illustrate the applicability of our approach with numerical results. Copyright © 2004 John Wiley & Sons, Ltd. [source] Intelligent control using multiple neural networksINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 6 2003Lingji 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] Model reference adaptive control using a low-order controllerINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 3 2001Daniel E. Miller Abstract In the model reference adaptive control problem, the goal is to force the error between the plant output and the reference model output asymptotically to zero. The classical assumptions on a single-input,single-output (SISO) plant is that it is minimum phase, and that the plant relative degree, the sign of the high-frequency gain, and an upper bound on the plant order are known. Here we consider a modified problem in which the objective is weakened slightly to that of requiring that the asymptotic value of the error be less than a (arbitrarily small) pre-specified constant. Using recent results on the design of generalized holds for model reference tracking, here we present a new switching adaptive controller of dimension two which achieves this new objective for every minimum phase SISO system; no structural information is required. Copyright © 2001 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] Achieving state estimation equivalence for misassigned disturbances in offset-free model predictive controlAICHE JOURNAL, Issue 2 2009Murali R. Rajamani Abstract Integrated white noise disturbance models are included in advanced control strategies, such as Model Predictive Control, to remove offset when there are unmodeled disturbances or plant/model mismatch. These integrating disturbances are usually modeled to enter either through the plant inputs or the plant outputs or partially through both. There is currently a lack of consensus in the literature on the best choice for the structure of this disturbance model to obtain good feedback control. We show that the choice of the disturbance model does not affect the closed- loop performance if appropriate covariances are used in specifying the state estimator. We also present a data based autocovariance technique to estimate the appropriate covariances regardless of the plant's true unknown disturbance source. The covariances estimated using the autocovariance technique and the resulting estimator gain are shown to compensate for an incorrect choice of the source of the disturbance in the disturbance model. © 2009 American Institute of Chemical Engineers AIChE J, 2009 [source] Non-Linear Model Predictive Control: A Personal Retrospective,THE CANADIAN JOURNAL OF CHEMICAL ENGINEERING, Issue 4 2007B. Wayne Bequette Abstract An overview of non-linear model predictive control (NMPC) is presented, with an extreme bias towards the author's experiences and published results. Challenges include multiple solutions (from non-convex optimization problems), and divergence of the model and plant outputs when the constant additive output disturbance (the approach of dynamic matrix control, DMC) is used. Experiences with the use of fundamental models, multiple linear models (MMPC), and neural networks are reviewed. Ongoing work in unmeasured disturbance estimation, prediction and rejection is also discussed. On présente un aperçu général du contrôle prédictif par modèles non linéaires (NMPC), en mettant l'accent en particulier sur les expériences des auteurs et les résultats publiés. Les défis incluent des solutions multiples (à partir des problèmes d'optimisation non convexes), ainsi que la divergence entre les sorties de modèle et d'installation lorsque la perturbation de sortie additive constante (la méthode du contrôle de matrice dynamique, DMC) est utilisée. Les expériences avec les modèles fondamentaux, les modèles linéaires multiples (MMPC) et les réseaux neuronaux sont examinées. Le travail actuellement mené sur l'estimation, la prédiction et le rejet des perturbations non mesurées est également examiné. [source] | |||||||||||||