Learning automata in control planning strategies

Intelligent Computational Optimization has been successfully applied to several control approaches. For instance, Planning Control uses information regarding a problem and its environment to decide whether a plan is the most suitable to achieve a required control objective or not. Such algorithm is commonly embedded into a conveniently located model inside a control loop. Planning provides a general and easy methodology widely used by a number of approaches such as receding horizon control (RHC) and model predictive control (MPC). Actually, MPC is the planning approach that has recently acknowledged a wide acceptance for industrial applications despite being highly constrained by |its computational complexity. For MPC, the evaluation of the overall plan is based upon time-consuming approaches such as dynamic programming and gradient-like methods. This chapter explores the usefulness of planning in order to improve the performance of feedback-based control schemes considering one probabilistic approach known as the Learning Automata (LA). Standard gradient methods develop a plan evaluation scheme whose solution lies on a neighbourhood distance from the previous point, forcing to explore the space extensively. Remarkably, LA algorithms are based on stochastic principles considering newer points for optimization as being determined by a probability function with no constraints whatsoever on how close they lie from previous optimization points. The proposed LA approach is considered as a planning system to select the plan holding the highest probability of yielding the best closed-loop results. The system's performance is tested through a nonlinear benchmark plant, comparing its results to the Levenberg-Marquardt (LM) algorithm and some other Genetic algorithms (GA). © 2011 Springer-Verlag Berlin Heidelberg