Abstract:

An adaptive optimal controller is designed for the hypersonic vehicle attitude control employing adaptive dynamic programming (ADP). An optimal control problem of a nonlinear reentry attitude control system is formed and the single-network integral reinforcement learning (SNIRL) algorithm is proposed to solve this problem. SNIRL simplifies the actor-critic structure of the integral reinforcement learning (IRL) algorithm during iteration so that the optimal controller can be obtained using only one network which approximates the value function. The convergence of this algorithm is guaranteed. Based on the SNIRL algorithm, the adaptive optimal controller is designed and the stability of the closed-loop system is also proved. The simulation examples are provided to show that SNIRL converges faster and has higher calculation efficiency than IRL. The effectiveness of the adaptive optimal attitude controller is also shown in the results.

Key words:  Reentry vehicle, Attitude control, Adaptive optimal control, Single-network integral reinforcement learning