Research On Speed Control Method Of Driving Motor For Base System

In order to simulate the spacecraft's spatial motion on the ground simulation,the researchers proposed that the spacecraft's space motion can be equivalently transformed into the ground reference motion,and can then be tracked and controlled by a ground moving base that satisfies a given position and velocity requirement.To achieve ground simulation of spacecraft orbital motion,thereby reducing the cost of space motion simu-lation,and improve development efficiency.In this paper,the rotational speed tracking control of the bottom motor of the mobile base system is studied.Because the moving base needs to satisfy the given position and speed requirements at the same time during the movement,the movement of the motor in the low speed zone is difficult to avoid,but the high-precision speed control of the brushed DC motor in the low speed zone is difficult to achieve.This is mainly due to the fact that disturbances in the low speed zone adversely affect the motor speed tracking.Therefore,based on the requirements of wide-speed and high-precision drive motors for mobile pedestal actuators,an adaptive robust nonlinear control method for brushed DC motors based on reinforce-ment learning was proposed to solve the problem of low-speed disturbance suppression in brushed DC motors.Improve the tracking accuracy of the speed.Firstly,based on the structure and principle of brushed DC motor,the dynamic model of the motor including armature circuit model and rigid body dynamics model is given.The phenomena of dead zone,crawling and so on in the low-speed fluctuation and dynamic process of brushed DC motor are analyzed.s reason.In view of the nonlinear characteristics of LuGre friction torque,in order to avoid the instability phenomenon of the observer when estimating the unknown state of the LuGre dynamic friction model under high-speed operating conditions,the LuGre dynamic friction model was improved to enable it to operate at both high and low speeds.The friction behavior is well described and observer instability can be avoided;the cogging torque is replaced by the dominant harmonic component of the first order harmonic component of the cogging torque and determined by the Fast Fourier Transform.The cogging torque base frequency deduces an affine cogging torque model.Then,a control-oriented motor nonlinear dynamic model was established.Then,according to the nonlinearity and parameter uncertainty of the brushed DC motor system,an adaptive robust nonlinear control method for brushed DC motor based on reinforcement learning is proposed.In order to accurately compensate the cogging torque and friction torque of the brushed DC motor,taking into account the uncertainties of the model parameters,a parameter adaptive law was designed based on the adaptive robust control method,and the model parameters were identified online.For the unknown disturbances such as modeling error and system load in the system,the disturbance estimation method based on the reinforcement learning Actor-Critic method is adopted to estimate the unknown disturbance on line.Based on the differential flatness characteristics of brushed DC motor current loop and speed loop system,a two-degree-of-freedom current loop and speed loop controller based on differential flatness is designed.Finally,in order to verify the effectiveness and superiority of the design method of brushed DC motor speed control in this paper,the relevant hardware circuit of the motor system was designed based on dSPACE's motor rapid prototype test platform.Exper-iments in different steady state and transient conditions have verified the effectiveness of the proposed method.And compared with the control performance of dual-loop PID control method and adaptive robust control method.The experimental results show that the adaptive robust control method of brushed DC motor based on reinforcement learning designed in this paper has better steady state and transient control performance.