Neural Network Techniques for Robust Force Control of Robot Manipulators

In this paper a neural network force/position control scheme is proposed to compensate uncertainties in both robot dynamics and unknown environment. The proposed impedance control allows us to regulate force directly by specifying a desired force. Training signals are proposed for a feed forward neural network controller. The robustness analysis of the uncertainties in environment position is presented. Simulation results are presented to show that both position and force tracking are excellent in the presence of uncertainties in robot dynamics and unknown environment. It is well known that impedance control technique of Hogan 1] is an eeective way for controlling the contact force between a robot end eeector and an environment. The principle idea of this technique is to regulate the contact force and the end-eeector position based on a desired mechanical impedance. It is also known that the contact force can be controlled to track a desired force if the reference trajectory of the robot is speciically designed using the information of the environment position and stiiness 1]. In many applications, force tracking impedance control is of great interest. We can see that a successful implementation of the force tracking impedance control requires a high quality robot position controller and accurate knowledge of environment position and environment stii-ness. These requirements are usually diicult to fullll simultaneously. Thus, it remains to be a strong challenge for researchers to solve the problem in a meaningful way. It has been shown that adaptive techniques can be eeectively applied to deal with the problems of uncertainties in robot dynamics and environment stiiness 2, 3, 4]. Seraji and Colbaugh 2] has proposed the adaptive technique to adjust the reference trajectory using force tracking errors so that the controller can accommodate the lack of knowledge of environment position and stiiness. Seraji 4] has implemented the adaptive PID or PI controllers to deal with unknown environment stiiness. A non adaptive approach using a combination of disturbance rejection and integral control has also been proposed to solve the problem of uncertainties in robot model and environment 5]. Colbaugh and Engelmann 6] has proposed the adaptive motion control which does not require any knowledge of either robot dynamics or environment. Recently the authors have developed a solution for force tracking impedance control using a neural network controller and an environment position modii-cation technique to compensate for uncertainties in robot dynamic model and environment stiiness is assumed to be …