Since conventional PID (Proportional–Integral–Derivative) controllers hardly control the robot to stabilize for constant force grinding under changing environmental conditions, it is necessary to add a compensation term to conventional PID controllers. An optimal parameter finding algorithm based on SAC (Soft-Actor-Critic) is proposed to solve the problem that the compensation term parameters are difficult to obtain, including training state action and normalization preprocessing, reward function design, and targeted deep neural network design. The algorithm is used to find the optimal controller compensation term parameters and applied to the PID controller to complete the compensation through the inverse kinematics of the robot to achieve constant force grinding control. To verify the algorithm's feasibility, a simulation model of a grinding robot with sensible force information is established, and the simulation results show that the controller trained with the algorithm can achieve constant force grinding of the robot. Finally, the robot constant force grinding experimental system platform is built for testing, which verifies the control effect of the optimal parameter finding algorithm on the robot constant force grinding and has specific environmental adaptability.

Background: Signal generator plays a crucial role in the field of automatic measurement. However, Domestic signal generator usually uses a number of discrete phases locked loop components, whose circuits are usually complex with low precision and are easily affected by the external environment. Objective: Based on direct digital synthesis (DDS) technology, a small sized high precision economic waveform generator with a simple structure and operational convenience is developed. Methodology: The hardware circuit is designed with an AT89S52 controller, and the corresponding peripheral circuit. A DDS integrated chip AD9834 is employed as a signal source, and the modular programming principle with C and assembly language is used to accomplish the software design, through which the sine wave, square wave, triangular wave signals with adjustable amplitude, frequency, and TTL signal with adjustable duty ratio can be generated. Conclusion: The whole system is advantageous with several aspects such as high frequency precision, wide frequency range, and operational convenience. The experimental results show that the stability and reliability of the waveform generator are good with a simple hardware circuit structure.

In order to solve the problems of high dependence on the accuracy of environmental model and poor environmental adaptability of traditional control methods, the robot constant force grinding controller that based on proximal policy optimization was proposed. Training the controller model between grinding force difference and end-effector compensation displacement using the proximal policy optimization algorithm. Complete compensation using robot inverse kinematics. In order to validate the algorithm, a simulation model of the grinding robot with perceivable force information is established. The simulation results demonstrate that the controller trained using this algorithm can achieve constant force grinding without setting up the environment model in advance and has some environmental adaptability.

Since conventional PID (Proportional-Integral-Derivative) controllers hardly control the robot to stabilize for constant force grinding under changing environmental conditions, it is necessary to add a compensation term to conventional PID controllers. An optimal parameter finding algorithm based on SAC(Soft-Actor-Critic) is proposed to solve the problem that the compensation term parameters are difficult to obtain, including training state action and normalization preprocessing, reward function design, and targeted deep neural network design. The algorithm is used to find the optimal controller compensation term parameters and applied to the PID controller to complete the compensation through the inverse kinematics of the robot to achieve constant force grinding control. To verify the algorithm's feasibility, a simulation model of a grinding robot with sensible force information is established, and the simulation results show that the controller trained with the algorithm can achieve constant force grinding of the robot. Finally, the robot constant force grinding experimental system platform is built for testing, which verifies the control effect of the optimal parameter finding algorithm on the robot constant force grinding and has specific environmental adaptability.