Control of a Solar Energy Systems

8th IFAC Symposium on Advanced Control of Chemical ProcessesThe International Federation of Automatic Control Singapore, July 10-13This work deals with the main control problems found in solar power systems and the solutions proposed in literature. The paper first describes the main solar power technologies, its development status and then describes the main challenges encountered when controlling solar power systems. While in other power generating processes, the main source of energy can be manipulated, in solar energy systems, the main source of power which is solar radiation cannot be manipulated and furthermore it changes in a seasonal and on a daily base acting as a disturbance when considering it from a control point of view. Solar plants have all the characteristics needed for using industrial electronics and advanced control strategies able to cope with changing dynamics, nonlinearities and uncertainties.Ministerio de Ciencia e Innovación PI2008-05818Ministerio de Ciencia e Innovación DPI2010-21589-C05-01/04Junta de Andalucía P07-TEP-0272

Applications of Model Predictive Controllers in a Sugar Factory

INTERNATIONAL SYMPOSIUM ON ADVANCED CONTROL OF CHEMICAL PROCESSThis paper presents two applications of Model Predictive Control in a sugar factory: temperature control in the diffusion process and density control in the wastewater treatment plant. The implementation is done using a Generalized Predictive Controller (GPC) designed for a wide class of industrial process, with the same computational requirements as a PID routine and embedded in the existing control system. The processes have in common the existence of long and uncertain dead times, therefore the original GPC algorithm is improved by the use of the T polynomial, which increases the stability robustness by filtering the predictions.Comisión Interministerial de Ciencia y Tecnología (CICYT) TAP 96-0884Comisión Interministerial de Ciencia y Tecnología (CICYT) TAP 98-0541Comisión Interministerial de Ciencia y Tecnología (CICYT) 1FD97-083

Application of simple cascade GPC with robust behaviour to a sugar refinery

This paper presents the application of a Generalized Pre dictive Controller (GPC) to sludge density control in a sugar factory. The loop is controlled by a cascade strategy where both the master and the slave controllers are predic tive ones. The control law is extremely simple to compute and the tuning is straightforward since a method to im plement GPC previously developed by the authors which is very simple to implement and tune has been used. The controllers are embedded in the existing control system needing the same computational requirements as pid rou tines. The original GPC algorithm is improved by the use of the socalled T polynomial which increases the stabil ity robustness by ltering the predictions in order to cope with model uncertainties and di erent process dynamics caused by changes in the process operating pointsMinisterio de Ciencia y Tecnología s TAP 96-884 (CICYT)Ministerio de Ciencia y Tecnología TAP 95-370 (CICYT

Implementation of GPC for integrating··processes with low computational. Requirenients

This paper presents a straightforward method for im­plementing generalized predictive self-tuning controllers with low computational requirements. The method makes use of the fact that a generalized predictive con­troller results in a control law that can be described with few parameters. The controller has been developed for processes having an integral effect. A set of simple functions relating the controller parameters to the process parameters has been obtained. With this set of functions either a fixed or a selftuning GPC can be implemented in a straight­forward manner. An application to the control of a DC motor is given

Bounded Error Identification of Systems With Time-Varying Parameters

This note presents a new approach to guaranteed system identification for time-varying parameterized discrete-time systems. A bounded description of noise in the measurement is considered. The main result is an algorithm to compute a set that contains the parameters consistent with the measured output and the given bound of the noise. This set is represented by a zonotope, that is, an affine map of a unitary hypercube. A recursive procedure minimizes the size of the zonotope with each noise corrupted measurement. The zonotopes take into account the time-varying nature of the parameters in a nonconservative way. An example has been provided to clarify the algorithm

Qualitative Supervision of Naval Diesel Engine Turbocharger Systems

FAC Intelligent Components and Instruments for Control Applications, Malaga, Spain, 1992This paper presents a qualitative model the diesel engine turbocharger system of a ship. The paper also shows how qualitative models can be use for an intelligent monitoring of the process concerned

Dead-Time compensators: A unified approach

IFAC Linear Time Delay Systems,Grenoble,France,1998This paper shows how most dead-time compensators can be considered as a particular case ofa proposed general control structure. The proposed structure can be tuned taking into account the performance and robustness ofthe closed-loop. The obtained controller is more general and allows better results than previous algorithms. In order to illustrate the results, some simulation examples are shown

Prediction for control

5th IFAC Conference on System Structure and Control 1998 (SSC'98), Nantes, France, 8-10 JulyThis paper shows that "optimal" controllers based on "optimal" predictor structures are not "optimal" in their closed loop behaviour and that predictors should be designed taking into account closed-loop considerations. This is first illustrated with a first order plant with delay. The ISE index is computed for two typical optimal controllers (minimum variance controller and generalized predictive controller) when a stochastic disturbance is considered. The results are compared to those obtained by the use of a non optimal PI controller that uses a non optimal Smith predictor and performs better than the optimal controllers for the illustrative example. A general structure for predictors is proposed. In order to illustrate the results, some simulation examples are shown.Ce papier montre que des lois de commandes "optimales" basees sur des structures predictives "optimales" ne sont pas "optimales" dans leur comportement en boucle fermee et que la synthese de predicteurs devrait prendre en compte des considerations de boucle fermee. Cela est d'abord illustre avec un systeme du premier ordre a retard. l'index ISE est calcule pour deux lois de commandes optimales typiques (loi de commande a variance minim ale et loi de commande predictive generalisee), quand une perturbation stochastique est consideree. Les resultats sont compares a. ceux obtenus avec un regulateur PI non optimal base sur un predicteur de Smith non optimal et sont, pour l'exemple illustratif, meilleurs que ceux obtenus avec un regulateur optimal. Vne structure generale de predicteur est proposee. Pour illustrer les resultats, des exemples de simulations sont montres

Enlarging the domain of attraction of MPC controllers

This paper presents a method for enlarging the domain of attraction of nonlinear model predictive control (MPC). The usual way of guaranteeing stability of nonlinear MPC is to add a terminal constraint and a terminal cost to the optimization problem such that the terminal region is a positively invariant set for the system and the terminal cost is an associated Lyapunov function. The domain of attraction of the controller depends on the size of the terminal region and the control horizon. By increasing the control horizon, the domain of attraction is enlarged but at the expense of a greater computational burden, while increasing the terminal region produces an enlargement without an extra cost. In this paper, the MPC formulation with terminal cost and constraint is modified, replacing the terminal constraint by a contractive terminal constraint. This constraint is given by a sequence of sets computed off-line that is based on the positively invariant set. Each set of this sequence does not need to be an invariant set and can be computed by a procedure which provides an inner approximation to the one-step set. This property allows us to use one-step approximations with a trade off between accuracy and computational burden for the computation of the sequence. This strategy guarantees closed loop-stability ensuring the enlargement of the domain of attraction and the local optimality of the controller. Moreover, this idea can be directly translated to robust MPC.Ministerio de Ciencia y Tecnología DPI2002-04375-c03-0