Center of gravity position prediction and trajectory tracking control for omnidirectional robots
A tracking control method based on the long short-term memory(LSTM)neural network for on-line pre-diction of the position of the center of gravity of an omnidirectional mobile robot is presented to solve the problems of nonlinear dynamic strong coupling,real-time center of gravity offset and difficulty in achieving high-precision tracking control.Firstly,a dynamic model considering gravity center deviation is established and its contrast model is built based on the LSTM neural network training.Secondly,the center of gravity offset parameters are estimated in real time based on the model comparison method,and then the center of gravity offset parameters are predicted based on the Zhang neural network(ZNN)to reduce the lag caused by parameter estimation.Finally,a numerical acceleration control algorithm is designed based on the dynamic feedback decoupling method,and the stability of the system is analyzed based on the pole assignment method of discrete system.The simulation results verify that the proposed method can improve the control accuracy by high-precision dynamic decoupling compared with the numerical acceleration controller and the adaptive con-troller because of the ability to predict the center of gravity offset parameters online.In actual experiments,the tracking accuracy of the proposed control algorithm is significantly higher than that of numerical acceleration control and model predictive control,which indicates that the proposed control algorithm can significantly reduce the impact of center of gravity offset on tracking control accuracy.
tracking controlinverse time-varying matrixcenter of gravity offsetparameter estimationlong short-term memory
万方数据
维普
