Trajectory Tracking Control of Autonomous Ground Vehicles Using Adaptive Learning MPC

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.

Automated summary

An adaptive learning model predictive control (ALMPC) scheme is proposed for trajectory tracking of autonomous ground vehicles (AGVs), with a focus on estimating unknown system parameters and introducing robustness constraints for handling uncertainties. The proposed method shows improved prediction accuracy and reduced conservatism compared to robust MPC methods, with theoretical analysis confirming recursive feasibility and input-to-state stability of the closed-loop system.