Skip to Main Content
This paper describes a learning control system using a reinforcement technique. The controller is capable of controlling a plant that may be nonlinear and nonstationary. The only a priori information required by the controller is the order of the plant. The approach is to design a controller which partitions the control measurement space into sets called control situations and then learns the best control choice for each control situation. The control measurements are those indicating the state of the plant and environment. The learning is accomplished by reinforcement of the probability of choosing a particular control choice for a given control situation. The system was stimulated on an IBM 1710-GEDA hybrid computer facility. Experimental results obtained from the simulation are presented.