倒立摆模糊确定性策略梯度控制方法研究

As a typical non-minimum phase system, the inverted pendulum system exhibits significant nonlinear and unstable characteristics, making it challenging to control. In response to the problems of insufficient interpretability of neural networks and difficulty in converging state variables to expected values in traditional deep reinforcement learning-based control methods for the inverted pendulum, a fuzzy deterministic policy gradient (FDPG) control algorithm is proposed. This algorithm integrates the deterministic policy gradient method with a Takagi-Sugeno (T-S) fuzzy model, exploiting the excellent function approximation capabilities of the T-S fuzzy model to approximate the Actor structure within the Actor-Critic framework, thereby expressing control strategies intuitively through fuzzy rules and enhancing the practical significance of the controller. In addition, by exploiting the interpretability of the T-S fuzzy model, the optimal control law derived from the linear quadratic regulator (LQR) is incorporated into the T-S model as prior knowl-edge, which ensures the local stability of the controller. Finally, through comparative analysis with the traditional deep deterministic policy gradient (DDPG) algorithm and the piecewise fuzzy control method, the proposed algorithm is shown to offer superior control performance and generalization ability in controlling the inverted pendulum system.