Robust Adaptive Critic Control With Hybrid Iteration for Continuous-time Systems

By integrating a hybrid iteration mechanism with an adaptive critic framework, a robust control method is designed for disturbed continuous-time nonlinear systems. The goal of accelerating learning and relaxing preset conditions are achieved by improving the traditional value iteration method. By incorporating adjustable parameters, the admissibility of the control policy during the iteration process is ensured, thereby relaxing the conditions for setting the accelerated factor. Combined the idea of generalized policy iteration, a novel hybrid iteration mechanism is constructed to acquire better convergence performance. Finally, two simulation examples are used to verify the performance of the proposed method. The simulation results of the linear system show the higher convergence accuracy of the method in this paper. In the simulation of the missile autopilot system, it is demonstrated that the convergence speed is improved by approximately 49% without relying on an initial admissible control policy compared to value iteration method.