Iterative Learning Control and Model Predictive Control for Trajectory Tracking with Model Mismatch

Abstract

Model predictive control (MPC) is a powerful technique that can be used for various applications, including trajectory tracking. However, MPC requires an accurate model of the system and a long enough prediction horizon to achieve good performance. Although variations of MPC like stochastic MPC and robust MPC can alleviate the problem to a certain degree, they still more or less rely on the knowledge of system models, disturbance and noise. MPC can also suffer from high computational cost or loss of recursive feasibility, particularly in the case of the existence of model mismatch (actual system model is different from the model used by the controller). Iterative learning control (ILC) is another technique that can be used for improving system performance (reducing tracking error) for repetitive tasks. ILC is known for its capability of learning from previous history (iterations of tasks) and less reliance on the model accuracy. In this paper, both MPC and different ILC algorithms are employed for a trajectory tracking problem with model mismatch. The performances of different algorithms are then analyzed and compared. A criterion on the degree of model mismatch to guarantee error convergence is also discussed for model-based ILC.