Optimization-based iterative learning control scheme for point-to-point tracking of nonlinear systems

For a robotic manipulator executing a repetitive carriage task, the primary target is to pick up an object from one position and place it at the intended position. In this circumstance, the manipulator is required to only pass certain key positions. This forms a multi-point-to-point trajectory tracking problem for which the controller only stimulates the manipulator at the predetermined instants. This paper considers an iterative learning control scheme for a nonlinear system with unknown parameters under the premise of minimizing the performance index, which is composed of the output tracking error and the rated input increment. For the optimized control scheme, the unknown system parameters are iteratively updated to obey two criteria: to minimize the output estimation error and the rated parameter estimation increment and to minimize only the output estimation error. A theoretical proof shows that the output tracking error is convergent along the iteration axis. Two numerical simulations are provided to demonstrate the effectiveness of the proposed learning scheme.