Adaptive Control of Robot Manipulators in Varying Environments

This paper studies the multiple-model based adaptive control of a robot manipulator moving in varying environments. The research problem is divided into two parts: the modeling of the system consisting of the robot manipulator and the varying environment, and the multiple-model based adaptive control of the robot manipulator. This paper considers the added mass, added moment of inertia, drag, and buoyancy as the environmental factors. In these dynamic models, the environmental factors and the mass, moment of inertia, and gravity of the robot are unknown parameters. By the linearity in the parameters property, we can write these parameters independently of the robot joint variables and thus can be estimated using an adaptive control law. After obtaining the system model, we adopt a multiple-model based adaptive control scheme. When the model of the robot changes, the parameter estimates can rapidly convert to a relatively closer one for the new true values. With a multiple-model based adaptive controller, the asymptotic tracking of the robot and the parameter boundedness are achieved, and the tracking is not disturbed by the variance of the environment parameters in the multiple model control case, which has better performance than the single model case.