Min-max control of nonlinear systems using universal learning networks

A min-max robust control method is proposed for nonlinear systems based on the use of the higher order derivatives calculation of universal learning networks (ULNs). An extended criterion function containing sensitivity terms is considered for controller design and the criterion function is evaluated at several specific operating points corresponding to certain system parameters. The ULNs learning is then performed in such a way that, at each step, it minimizes the worst criterion function among several operating points. It is found that the proposed control method is less time-consuming in the ULNs learning and a obtained controller has better performance than the conventional methods