A Look-Up Table Based Fractional Order Composite Controller Synthesis Method for the PMSM Speed Servo System

Considering the performance requirements in actual applications, a look-up table based fractional order composite control scheme for the permanent magnet synchronous motor speed servo system is proposed. Firstly, an extended state observer based compensation scheme was adopted to suppress the motor parametric uncertainties and convert the speed servo plant into a double-integrator model. Then, a fractional order proportional-derivative (PDμ) controller was adopted as the speed controller to provide the optimal step response performance for the servo system. A universal look-up table was established to estimate the derivative order of the PDμ controller, according to the optimal samples collected by an improved differential evolution algorithm. With the look-up table, the optimal PDμ controller can be tuned analytically. Simulation and experimental results show that the servo system using the composite control scheme can achieve optimal tracking performance and has robustness to the motor parametric uncertainties and disturbance torques.

Design of Position Control Method for Pump-Controlled Hydraulic Presses via Adaptive Integral Robust Control

This paper takes the position control performance of pump-controlled hydraulic presses as the research object. The control methods are designed respectively for the two motion stages of rapid descent and slow descent of hydraulic presses in order to improve the control performance of the system. First of all, the accuracy model of the pump-controlled hydraulic presses position servo system (the pump-controlled hydraulic presses position servo system, which is called PCHPS) and its MATLAB/Simulink simulation platform are established. Based on the theoretical analysis and experimental data, the interference factors affecting the tracking accuracy and positioning accuracy of the PCHPS are analyzed. Then, an adaptive integral robust control (the adaptive integral robust control, which is called AIRC) for PCHPS is designed to reduce the influence of nonlinear factors on the system, and the effectiveness of the controller is verified by simulation. Finally, the position control experiment of PCHPS is designed, and the experimental results show that the AIRC can effectively reduce nonlinear factors such as unknown interference in the slow-down stage of the system. The positioning accuracy is raised to within 0.008 mm, which improves the process level of the hydraulic presses.

Internal model control for rocket launcher position servo system based on improved wavelet neural network

This paper proposes an improved wavelet neural network-internal model controller (WNN-IMC) for the rocket launcher position servo system. Due to complex nonlinearities and uncertainties of external disturbances in the rocket launcher position servo system, it is vitally challenging to establish its accurate model by the mechanical modeling technique. A wavelet neural network (WNN) identification method is proposed to determine the system mathematical model through test datum, which optimized by the hybrid algorithm of differential evolution (DE) and particle swarm optimization (PSO). Then, the proposed method is applied to identify the semi-physical simulation platform of the rocket launcher velocity servo system. The results demonstrate that the validity of the DEPSO-WNN method is better than that of the WNN and PSO-WNN methods. Finally, compared with the WNN-IMC controller and the ADRC controller, the effectiveness of the improved WNN-IMC controller is verified by the semi-physical simulation experiments.

Control Method for Flexible Joints in Manipulator Based on BP Neural Network Tuning PI Controller

With the development of robot technology, integrated joints with small volume and convenient installation have been widely used. Based on the double inertia system, an integrated joint motor servo system model considering gear angle error and friction interference is established, and a joint control strategy based on BP neural network and pole assignment method is designed to suppress the vibration of the system. Firstly, the dynamic equation of a planetary gear system is derived based on the Lagrange method, and the gear vibration of angular displacement is calculated. Secondly, the vibration displacement of the sun gear is introduced into the motor servo system in the form of the gear angle error, and the double inertia system model including angle error and friction torque is established. Then, the PI controller parameters are determined by pole assignment method, and the PI parameters are adjusted in real time based on the BP neural network, which effectively suppresses the vibration of the system. Finally, the effects of friction torque, pole damping coefficient and control strategy on the system response and the effectiveness of vibration suppression are analyzed.