Research On Trajectory Tracking Control Method Of Mobile Manipulator Based On Recurrent Neural Network

Due to the expansion of the operating space of the mobile manipulator and the improvement of the interactive ability between the mobile platform and the environment,it has been widely used in transportation,search and rescue,security and monitoring.In the transportation process,in order to save transportation costs,the mobile platform needs to carry out path planning to perform complex tasks,that is,to select the optimal node from the starting point to the target point.However,in the actual working conditions,there are widespread external forces,environmental impact forces,electromagnetic interference and various noises in the signal transmission process that interfere with the movement of the mobile manipulator.These disturbances will have a significant impact on the model solving accuracy,stability and task completion,resulting in the failure of the trajectory tracking task.Moreover,the mobile manipulator has some disadvantages such as difficult to realize cooperative control and poor ability to resist external disturbance.Therefore,it is necessary to design a noise-tolerant recurrent neural network algorithm to solve the trajectory tracking control problem of mobile manipulator with disturbance.This paper conducts in-depth research on the above issues,and the main research contents are as follows:(1)Aiming at the tracking control problem of the mobile manipulator,the kinematics equations of the mobile platform and the manipulator were established,and the global kinematics equations of the mobile manipulator based on the world coordinates were obtained by using the spatial coordinate transformation matrix,which laid a foundation for the subsequent collaborative control of the mobile platform and the manipulator.(2)To solve the trajectory tracking problem of a mobile manipulator,the trajectory tracking problem is transformed into a time-varying nonlinear equations problem.Based on traditional zeroing neural network(ZNN)and gradient neural network(GNN),an anti-noise zeroing neural network(NTZNN)is designed to eliminate external noise disturbance.It is proved theoretically that NTZNN model can eliminate external noise disturbance.Numerical simulation results show that the NTZNN model converges to the exact solution of nonlinear equations with external disturbance.(3)According to path planning problem,construct a discrete nonlinear function is related to the energy function,and makes the lowest energy state of solution corresponding to the state of the optimal path solution,the continuity of ZNN discretization by discrete ZNN for solving nonlinear optimization problem,the simulation results show that the discrete ZNN iteration to find the minimum energy function,the state of the corresponding optimal solution,then determine the motion path of the mobile platform.(4)An NTZNN controller was designed to drive the end-effector to complete the tracking task.The simulation results show that NTZNN algorithm has better denoising ability than GNN algorithm and ZNN algorithm.Finally,the NTZNN model has good superiority and noise resistance through the physical platform.