An Approximate Neuro-Optimal Solution of Discounted Guaranteed Cost Control Design

The adaptive optimal feedback stabilization is investigated in this article for discounted guaranteed cost control of uncertain nonlinear dynamical systems. Via theoretical analysis, the guaranteed cost control problem involving a discounted utility is transformed to the design of a discounted optimal control policy for the nominal plant. The size of the neighborhood with respect to uniformly ultimately bounded stability is discussed. Then, for deriving the approximate optimal solution of the modified Hamilton-Jacobi-Bellman equation, an improved self-learning algorithm under the framework of adaptive critic designs is established. It facilitates the neuro-optimal control implementation without an additional requirement of the initial admissible condition. The simulation verification toward several dynamics is provided, involving the F16 aircraft plant, in order to illustrate the effectiveness of the discounted guaranteed cost control method.

Automated summary

This article investigates adaptive optimal feedback stabilization for discounted guaranteed cost control of uncertain nonlinear dynamical systems. The problem is transformed to design a discounted optimal control policy for the nominal plant, and an improved self-learning algorithm under the framework of adaptive critic designs is established for deriving the approximate optimal solution. The effectiveness of the method is illustrated through simulation verification on the F16 aircraft plant and other dynamics.