Predictive control of a solar air conditioning plant with simultaneous identification

This paper presents the application of a predictive controller with simultaneous identification to a solar air conditioning plant. The time varying nature of the process makes necessary an adjustment of the controller parameters to the varying operational conditions. The main novelty with respect to classic adaptive MPC scheme is to penalize the identification error in the cost function used for control. The behaviour of the controller is illustrated by simulations and experimental results. The integration of identification and control avoids the tedious identification procedure that is necessary before the start-up of any predictive controller. This new adaptive MPC scheme shows its effectiveness in controlling the outlet temperature in the solar thermal plant.Ministerio de Ciencia y Tecnología DPI2004-07444-C04-0

The application of a new PID autotuning method for the steam/water loop in large scale ships

In large scale ships, the most used controllers for the steam/water loop are still the proportional-integral-derivative (PID) controllers. However, the tuning rules for the PID parameters are based on empirical knowledge and the performance for the loops is not satisfying. In order to improve the control performance of the steam/water loop, the application of a recently developed PID autotuning method is studied. Firstly, a 'forbidden region' on the Nyquist plane can be obtained based on user-defined performance requirements such as robustness or gain margin and phase margin. Secondly, the dynamic of the system can be obtained with a sine test around the operation point. Finally, the PID controller's parameters can be obtained by locating the frequency response of the controlled system at the edge of the 'forbidden region'. To verify the effectiveness of the new PID autotuning method, comparisons are presented with other PID autotuning methods, as well as the model predictive control. The results show the superiority of the new PID autotuning method

Robust multivariable predictive control: how can it be applied to industrial test stands ?

To cope with recent technological evolutions of air conditioning systems for aircraft, the French Aeronautical Test Center built a new test stand for certification at ground level. The constraints specified by the industrial users of the process seemed antagonistic for many reasons. First, the controller had to be implemented on an industrial automaton, not adaptable to modern algorithms. Then the specified dynamic performances were very demanding, especially taking into account the wide operating ranges of the process. Finally, the proposed controller had to be easy for nonspecialist users to handle. Thus, the control design and implementation steps had to be conducted considering both theoretical and technical aspects. This finally led to the development of a new multivariable predictive controller, called alpha-MPC, whose main characteristic is the introduction of an extra tuning parameter alpha that has enhanced the overall control robustness. In particular, the H1-norm of the sensitivity functions can be significantly reduced by tuning this single new parameter. It turns out to be a simple but efficient way to improve the robustness of the initial algorithm. The other classical tuning parameters are still physically meaningful, as is usual with predictive techniques. The initial results are very promising and this controller has already been adopted by the industrial users as the basis of the control part for future developments of the test stand

Nonlinear predictive control applied to steam/water loop in large scale ships

In steam/water loop for large scale ships, there are mainly five sub-loops posing different dynamics in the complete process. When optimization is involved, it is necessary to select different prediction horizons for each loop. In this work, the effect of prediction horizon for Multiple-Input Multiple-Output (MIMO) system is studied. Firstly, Nonlinear Extended Prediction Self-Adaptive Controller (NEPSAC) is designed for the steam/water loop system. Secondly, different prediction horizons are simulated within the NEPSAC algorithm. Based on simulation results, we conclude that specific tuning of prediction horizons based on loop’s dynamic outperforms the case when a trade-off is made and a single valued prediction horizon is used for all the loops

Vibration suppression in multi-body systems by means of disturbance filter design methods

This paper addresses the problem of interaction in mechanical multi-body systems and shows that subsystem interaction can be considerably minimized while increasing performance if an efficient disturbance model is used. In order to illustrate the advantage of the proposed intelligent disturbance filter, two linear model based techniques are considered: IMC and the model based predictive (MPC) approach. As an illustrative example, multivariable mass-spring-damper and quarter car systems are presented. An adaptation mechanism is introduced to account for linear parameter varying LPV conditions. In this paper we show that, even if the IMC control strategy was not designed for MIMO systems, if a proper filter is used, IMC can successfully deal with disturbance rejection in a multivariable system, and the results obtained are comparable with those obtained by a MIMO predictive control approach. The results suggest that both methods perform equally well, with similar numerical complexity and implementation effort

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

Neural Networks for Modeling and Control of Particle Accelerators

We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.Comment: 21 p