Position/Force Tracking Impedance Control for Robotic Systems with Uncertainties Based on Adaptive Jacobian and Neural Network

Abstract

In this paper, an adaptive Jacobian and neural network based position/force tracking impedance control scheme is proposed for controlling robotic systems with uncertainties and external disturbances. To achieve precise force control performance indirectly by using the position tracking, the control scheme is divided into two parts: the outer-loop force impedance control and the inner-loop position tracking control. In the outer-loop, an improved impedance controller, which combines the traditional impedance relationship with the PID-like scheme, is designed to eliminate the force tracking error quickly and to reduce the force overshoot effectively. In this way, the satisfied force tracking performance can be achieved when the manipulator contacts with environment. In the inner-loop, an adaptive Jacobian method is proposed to estimate the velocities and interaction torques of the end-effector due to the system kinematical uncertainties, and the system dynamical uncertainties and the uncertain term of adaptive Jacobian are compensated by an adaptive radial basis function neural network (RBFNN). Then, a robust term is designed to compensate the external disturbances and the approximation errors of RBFNN. In this way, the command position trajectories generated from the outer-loop force impedance controller can be then tracked so that the contact force tracking performance can be achieved indirectly in the forced direction. Based on the Lyapunov stability theorem, it is proved that all the signals in closed-loop system are bounded and the position and velocity errors are asymptotic convergence to zero. Finally, the validity of the control scheme is shown by computer simulation on a two-link robotic manipulator.

Adaptive Variable Parameter Impedance Control for Apple Harvesting Robot Compliant Picking

“…e force/position control system in the grasping process is modeled as an impedance model of the second-order differential equation of mass-damp-spring [24].…”

Inverse Jacobian Adaptive Tracking Control of Robot Manipulators with Kinematic, Dynamic, and Actuator Uncertainties

“…To address this challenging problem, much results have been achieved in [21][22][23][24][25][26][27][28][29].…”

“…In [23], Cheah et al designed an adaptive Jacobian control method to handle the control problem of robot manipulators with both kinematic and dynamic uncertainties. In [22,23,26] and [27], the design of the feedback controllers was based on the transposed Jacobian matrix, and all showed excellent stability characteristics. However, when the manipulator moves in a wide range, using the transposed Jacobian feedback method does not make the manipulator tracking maintain good performance.…”

Disturbance Observer-Based Robot End Constant Contact Force-Tracking Control

“…For example, in the case of too large external disturbance, the convergence speed is slow, especially for the constant force control in the applications such as grinding processing, which may lead to large uctuations of the contact force. In recent years, scholars have proposed many force control methods that integrate both the classic methods and the intelligent algorithms, such as fuzzy control and neural network control [3][4][5][6][7][8][9]. Panwar and Sukavanam et al [4] used a feedforward neural network to compensate the uncertainty of the robot model and presented an optimized force/position hybrid control method.…”