融合强化学习的无模型自适应控制末端执行器恒力控制OA

The pneumatic grinding system faces challenges such as nonlinearity,disturbance sensitivity,and modeling issues.The thesis proposes a model-free adaptive control strategy based on reinforcement learning,using the twin delayed deep deterministic policy gradient.This approach is named twin delayed deep deterministic policy gradient-model-free adaptive control.We build a model of the pneumatic loading system in MATLAB/Simulink,design the proposed control strategy,and define state-action and composite reward functions.Simulations show that the proposed control strategy is the optimal controller.Under constant force loading,the proposed control strategy exhibits no significant overshoot and achieves a 0.94 s settling time.Under disturbance loading,the two disturbance peaks are limited to 3.0 N and 3.2 N.For sine force loading,the proposed control strategy tracks the reference with an error of 0.16 N.Under varying angular loading,the proposed control strategy reduces error by 0.4 N.In conclusion,the proposed control strategy exhibits higher control accuracy,faster response,and greater robustness in complex nonlinear pneumatic systems,proving its potential for engineering applications.