Research On Planar Path Tracking Control Method Of Mobile Robot

With the gradual decline of the demographic dividend of today,the tendency of machines to perform tasks instead of people has become increasingly prominent.As an important member of the robot field,mobile robots,with their flexibility,maneuverability,adaptability and efficiency,have greatly expanded the area of daily production work and brought great convenience to people's lives.Mobile robots are typical non-holonomic systems.Whether they can be accurately controlled is the key to judging the performance of mobile robots.This article takes wheeled mobile robots as the research object,and mainly studies the path tracking control problem in the two-dimensional plane.In the process of mobile robots performing tasks,there are disturbances and noise outside the system,and unknown model parameters and unstructured factors inside,which will make the stability of the path tracking control system worse and the cumulative error increase,which will eventually lead to a decline in control performance and serious affect the normal operation of the system.In response to the above problems,this paper adopts sliding mode control technology as the core control strategy for the planar path tracking of mobile robots,and improves the control effect on this basis.In the path tracking control system,noise is an important factor that affects the accuracy and stability of the control system.Measurement noise will be generated when the mobile robot measures the reference path.The adaptive Kalman filter algorithm can be used to estimate the path after noise reduction and use it as a new reference path to reduce the impact of measurement noise on the controller and improve path tracking.The accuracy of the control system.At the same time,in view of the phenomenon that high frequency chattering will occur in sliding mode control,a low-pass filter is added to the sliding mode controller to reduce the high frequency gain of the controller and improve the stability of the path tracking control system.Through the Lyapunov stability theory,the sliding mode control law is designed,and the effectiveness of the double filtering strategy in planar path tracking control is verified by simulation.In practical engineering,there are many uncertainties and nonlinear factors in themobile robot system model,which make it impossible to get the control law matching the model exactly when the path tracking controller is designed.Aiming at these unstable factors,the neural network approach to nonlinear function strategy is adopted to realize the neural network adaptive sliding mode control of nonlinear system,and the influence of imprecise mobile robot model on the path tracking control system is solved.At the same time,in view of the weights of radial basis function neural network is difficult to quickly adjust the defects,the smallest parameter learning method,the design of a single parameter,instead of the weight values of neural network,the implementation is based on single parameter estimation of RBF neural network adaptive sliding mode control,in order to simplify the complexity of the algorithm,improve the real-time performance of path tracking control system.