Precision compensation method of manipulator based on neural network and particle swarm optimization

In the present era, robot industry is experiencing rapid development. Manipulators have emerged as essential robotic systems that play crucial roles in various domains. Ensuring high precision and reliability of manipulators holds paramount significance, particularly within the industrial sector. Numerous approaches have been explored to enhance the precision and reliability of manipulators by calculating and compensating for errors. However, there are some shortcomings in the existing methods. To address the existing issues and enhance the overall performance,based on the modified DH (MDH) model, this paper proposes an error prediction and compensation approach that combines back propagation (BP) neural network and particle swarm optimization (PSO). In this method, the attitude of IRB1410 manipulator is described by MDH model, and the forward and inverse kinematics are simulated by Matlab software. To complete the training of the BP neural network model, a small disturbance is randomly given to the manipulator within the constraint condition to obtain the corresponding data set. The model serves as a simulator for predicting errors, while the particle swarm optimization algorithm is employed to compute the compensation value, thereby circumventing local optima. Combined with the particle swarm optimization algorithm, the root mean square error before and after compensation is reduced by 78.65%. It can improve the accuracy of the manipulator, so that it can better complete people's needs.