Force Control of the Robot Manipulator with Collision Phenomena by Learning Control

Impact/collision is a fast and non-linear phenomenon, so it is difficult to control a robotic manipulator undergoing collision phenomena. Therefore, in the past, manipulators were moved slowly in order to avoid collision. But with the recent increase in the amount of high-speed tasks, control of the manipulator undergoing collision has become indispensable. In such a situation, it is effective to use learning control in the forward manner. In this paper, we propose a new learning control method to optimize the weighted least-squares criterion of learning errors. This method can be applied to obtain a unique control gain, and it is shown here that the convergence of learning error can be readily assured. Using this learning control method, we carried out experiments on force control with collision phenomena, and proved the convergence of the output error. The robotic manipulator was made of an air-driven rubber actuator with no reduction gears to avoid damage due to impact.