摘要：In this article, an adaptive proportional–integral–derivative–type neural network constrained control method based on radial basis function neural network model identifier is presented for automatic parking system. In the design process of the control method, the parameters of proportional–integral–derivative–type neural network controller can be adjusted online using the Jacobian information (the sensitivity of system output with respect to its input) of the controlled system. In this way, the proposed method will have a better adaptability. Meanwhile, we design a novel dynamic anti-windup compensation unit to solve the magnitude saturation and rate constraint problems of automatic parking system. The stability analysis based on Lyapunov function is given to prove the convergence of the proposed control algorithm. The final simulation results for automatic parking system show the effectiveness of the proposed method.
