Moreover, when the input signal is a time-varying signal with interference, the motor load system shows better anti-interference performance after using RSC-EPID. This method combines RSC-PID control theory with expert PID control ideas, further improves the stability and rapidity of the system, reduces the overshoot. Based on the RSC-PID control method, a hybrid controller combining RBF neural network supervisory control and expert PID control (RSC-EPID) is proposed. But RSC-PID is also unsatisfactory in terms of overshoot. This method can make the motor load system reach a steady state faster than simple PID control. First of all, the related algorithms of RBF neural network supervisory control composed of RBF neural network and PID control (RSC-PID) is introduced. The simulation results show that the nonlinear PD algorithm is better than the PD algorithm, meanwhile, the tracking speed and control precision of the system are improved.Ĭonsidering the contradiction among the response speed, overshoot and stability of system when the motor load system adopts PID control, a control strategy combining RBF (Radial Basis Function) neural network supervisory control and expert PID control is designed to effectively improve this problem in this paper. The algorithm automatically adjusts the weights according to the error magnitude to complete the controller parameter adjustment, thus reducing the error of the system. Considering that the nonlinear object has the characteristic of rapid change with time, the article improves the PD algorithm to nonlinear PD control algorithm to complete the design of the system. The algorithm learns online through a neural network while optimizing the output of the PD, which ultimately enables the actual output of the system to track up to the desired output. Based on the nonlinear U model, a control algorithm with cerebellar model articulation controller and proportional derivative (PD) in parallel is proposed. In this paper, the nonlinear U model with time-varying coefficients is investigated and the transformation of the nonlinear model is accomplished by the Newton iterative algorithm.
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