A New Minimax Control Method for Nonlinear Systems Using Universal Learning Networks

In neural network based control systems, if system environments, that is, system parameters and disturbances at training stage, are much different from those at control stage, performances of control systems may become worse. To solve this problem, robust control design is needed. In this paper, we propose a new minimax control method using Universal Learning Networks, in which the criterion function is evaluated at several specific operating points, and at each training step the worst criterion function among the operating points is optimized. Moreover, a sensitivity term calculated on the operating point is included in the criterion function in order to improve the performance of the control system between two operating points. The minimax control method including sensitivity term is shown to have better robustness against the changes of system parameters.