Research on trajectory tracking control of mechanical motion based on convolution neural network

In order to better improve the effect of mechanical motion trajectory tracking control and ensure the quality and safety of mechanical operation, a mechanical motion trajectory tracking control method based on convolution neural network is proposed. In the process of mechanical motion trajectory tracking control, due to the influence of small disturbance piecewise linear error, the mechanical system will produce multivariable nonlinear motion phenomenon in the operation process. Boundary layer tracking error is easy to occur. In order to solve the above problem, the convolution neural network is used to optimize the mechanical trajectory tracking control algorithm, and the simulation model of mechanical motion environment is established. The spatial coordinates of the trajectory are abstracted as the virtual world of genetic population, and the spatial grid structure characteristics are obtained. According to the matching conditions and parameters of convolution neural network, an error tracking integral term is designed and improved. By accurately tracking the mechanical trajectory, the accuracy of path planning and autonomous positioning is effectively improved. The experimental results show that the control method based on convolution neural network has the advantages of fast convergence speed, high accuracy and high calculation accuracy.