AN EMPIRICAL-APPROACH TO OPTIMAL SELF-CONTROL

This paper describes an application of the empirical modeling of natur al natural phenomena to the optimal self-control of an autonomous syst em in a chaotic environment. The system consists of a network of senso rs, a modeler a controller, a plant, and a utility estimator. The mode ler contains a self-organizing neural network and a conditional averag e estimator An empirical model, which incorporates the influences from the environment, the system response and the utility, is formed in th e modeler during training, The sensors provide signals representing th e joint state of the environment and the system, while the utility est imator transforms these signals into a utility signal. A vector compri sing the joint state, the control, and the utility variable is then ut ilized in a self-organized adaptation of prototype vectors. During ada ptation, samples of the control variable are generated either randomly or by a reinforcement procedure, while during application the optimal control variable is estimated by a conditional average taken over the prototype vectors. The control variable drives the plant, and improve s its performance. The method is demonstrated, using as examples the o ptimal selection of cutting depth in a chaotic manufacturing process, the self-stabilization of a randomly influenced system, and reversing a vehicle. (C) 1997 Elsevier Science Ltd. All rights reserved.