二自由度飞行姿态模拟器的模糊强化学习控制

Aiming at the attitude stabilization problem of two-degrees-of-freedom flight attitude simulator, an attitude stabilization controller was designed based on the policy iteration algorithm in the reinforcement learning.The policyiteration learning algorithm and the polynomial T-S fuzzy systems were combined together, conducting parameters' adjustment of the controller, and achievingthe optimization of the attitude stability control performance of the two-degrees-of-freedom flight attitude simulator.By approximating the policy function of the actor and the value function of the critic with the polynomial T-S fuzzy models, the actor-critic structure based on the polynomial T-S fuzzy models was established. Through the policy iteration process, the optimal parameters of the controller were learned to minimize the value function.The simulation results show that the policy iteration algorithm based on polynomial T-S fuzzy models is effective in controlling aircraft attitude stabilization.