A model reaching learning control scheme for a class of nonlinear systems

In the convolutional learning control design, a desired output trajectory is specified and an iterative algorithm is implemented to improve the tracking performance as the action is repeated. This limits the applications of the learning controller because in some important tasks like impedance control of a robotic manipulator, a desired model is given rather than the trajectories. In this paper, a model reaching learning control scheme (MRLC) is proposed for a class of nonlinear systems. A desired model specifying the desired output is given and an iterative algorithm is designed so that the learning system will eventually follow the desired response specified by the desired model as the action is repeated. This allows extra freedom in learning controller design and represents many important applications. The proposed learning controller is applied to impedance control of robotic manipulators. Simulation results of a cylindrical robot are presented to illustrate the performances of the learning controllers.