RADIAL BASIS NEURAL NETWORK FOR THE SOLUTION OF OPTIMAL CONTROL PROBLEMS VIA SIMULINK

Abstract

This study uses radial basis neural network to solve optimum control problems via simulink. Using Pontryagini's principle, the optimal control problem's optimum system is constructed. MATLAB is used to simulate the optimality system and generate the simulink architecture for the trial value. A radial basis neural network is then used to train the system and produce the optimal solution. This approach's effectiveness is evaluated using a few control problems, and it is shown to be effective because of the reliable, accurate, and consistent results that are produced. The performance of this strategy is superior to that of other approaches.