Trajectory Tracking Control of Autonomous Ground Vehicles Using Adaptive Learning MPC

• Published in: IEEE transaction on neural networks and learning systems Vol. 32; no. 12; pp. 5554 - 5564
• Main Authors: Zhang, Kunwu, Sun, Qi, Shi, Yang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; Adaptive learning; adaptive learning model predictive control (ALMPC); Algorithms; Autonomous ground vehicles (AGVs); Autonomous vehicles; Computer applications; Feedback control; Kinematics; Learning; Optimization; Parameter estimation; parameter identification; Predictive control; Predictive models; Robustness; Robustness (mathematics); Tracking control; Trajectory control; Trajectory tracking; uncertain dynamic system; Unmanned ground vehicles; Upper bounds
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2020.3048305

In this work, an adaptive learning model predictive control (ALMPC) scheme is proposed for the trajectory tracking of perturbed autonomous ground vehicles (AGVs) subject to input constraints. In order to estimate the unknown system parameter, we propose a set-membership-based parameter estimator based on the recursive least-squares (RLS) technique with the ensured nonincreasing estimation error. Then, the estimated system parameter is employed in MPC to improve the prediction accuracy. In the proposed ALMPC scheme, a robustness constraint is introduced into the MPC optimization to handle parametric and additive uncertainties. For the designed robustness constraint, its shape is decided off-line based on the invariant set, whereas its shrinkage rate is updated online according to the estimated upper bound of the estimation error, leading to further reduced conservatism and slightly increased computational complexity compared with the robust MPC methods. Furthermore, it is theoretically shown that the proposed ALMPC algorithm is recursively feasible under some derived conditions, and the closed-loop system is input-to-state stable (ISS). Finally, a numerical example and comparison study are conducted to illustrate the efficacy of the proposed method.