Time Delay Strong Tracking Kalman Estimation and Self-Learning Control of Manipulator Contact Force
In order to improve the accuracy of contact force estimation and joint control in the interaction between manipulator and environment,a contact force estimation method based on time-delay strong tracking Kalman filter and a joint angle position control method based on entropy self-learning control network are proposed.The joint driving model and dynamic model of the manipulator system are established.Based on the theory of time delay estimation,the time delay model of 4-DOF manipulator system is established.The fading factor is introduced into the Kalman filter to force the estimation residual to be orthogonal,soa contact force estimation method based on time-delay strong tracking Kalman is proposed.Taking the contact force error and joint position error as the input and joint torque as the output,the self-learning control network is constructed,and the entropy cluster-ing is proposed to determine the network structure and parameters,so as to design the entropy self-learning control network.The simulation results show that the estimation error interval distribution based on time-delay strong tracking Kalman is smaller than that of standard Kalman and reference[12],and the distribution density is very high at the error of 0.The absolute tracking error and convergence time of entropy self-learning control network for the desired trajectory are much smaller than that of self-learning control network and variable impedance damping control.The simulation results verify the superiority of the contact force estimation method and angular position control method in this paper.
Contact Force EstimationJoint ControlStrong Tracking Kalman FilteringEntropy Self-Learning ControlManipulator System
NETL
NSTL
万方数据
维普
