Model predictive control (MPC) is a model-based optimal control strategy widely used in robot systems.In this work, the MPC controller tuning problem for the path tracking of the wheeled mobile robot is studied and a novel self-tuning approach is developed. First, two novel path tracking performance indices, i.e., steadystate time ratio and steady-state distance ratio are proposed to more accurately reflect the control performance.Second, the mapping relationship between the proposed indices and the MPC parameters is established based on machine learning technique, and then a novel controller structure which can automatically tune the control parameters online is further designed. Finally, experimental verification with an actual wheeled mobile robot is conducted, which shows that the proposed method could outperform the existing method via achieving significant improvement in the rapidity, accuracy and adaptability of the robot path tracking.
LIU Yuesheng (刘月笙)
,
HE Ning? (贺宁)
,
HE Lile (贺利乐)
,
ZHANG Yiwen (张译文)
,
XI Kun (习坤)
,
ZHANG Mengrui (张梦芮)
. Self-Tuning of MPC Controller for Mobile Robot Path Tracking Based on Machine Learning[J]. Journal of Shanghai Jiaotong University(Science), 2024
, 29(6)
: 1028
-1036
.
DOI: 10.1007/s12204-022-2545-z
[1] XI Y G, LI D W, LIN S. Model predictive control—status and challenges [J]. Acta Automatica Sinica,2013, 39(3): 222-236.
[2] ZHANG R D, XUE A K, GAO F R. Model predictive control : Approaches based on the extended state space model and extended non-minimal state space model [M]. Singapore: Springer, 2018.
[3] CHEN Y, JIANG W L, SHI X H, et al. Tuning parameters for model predictive controller based on CSA-SQP[J]. Journal of Ningbo University (Natural Science & Engineering Edition), 2017, 30(4): 51-57 (in Chinese).
[4] IRA A S, MANZIE C, SHAMES I, et al. Tuning of multivariable model predictive controllers through expert bandit feedback [J]. International Journal of Control,2021, 94(10): 2650-2658.
[5] MOUMOUH H, LANGLOIS N, HADDAD M. A novel tuning approach for MPC parameters based on artificial neural network [C]//2019 IEEE 15th International Conference on Control and Automation. Edinburgh:IEEE, 2019: 1638-1643.
[6] JARDINE P T, GIVIGI S, YOUSEFI S. Parameter tuning for prediction-based quadcopter trajectory planning using learning automata [J]. IFACPapersOnLine, 2017, 50(1): 2341-2346.
[7] LIU C, WANG D Y, ZHANG Y X, et al. Model predictive control for path following and roll stabilization of marine vessels based on neurodynamic optimization[J]. Ocean Engineering, 2020, 217: 107524.
[8] HE N, QI L P, LI R X, et al. Design of a model predictive trajectory tracking controller for mobile robot based on the event-triggering mechanism [J]. Mathematical Problems in Engineering, 2021, 2021:5573467.
[9] LUO L H. Steering control for lane keeping system based on MPC [J]. Journal of Shanghai Jiao Tong University, 2014, 48(7): 1015-1020 (in Chinese).
[10] CAO Y, HE D B, YU F, et al. Generalized predictive control based on vehicle path following strategy by using active steering system [J]. Journal of Shanghai Jiao Tong University, 2016, 50(3): 401-406 (in Chinese).
[11] ZHAO Y Q, WANG J, ZANG L G, et al. Path tracking for different wheelbase vehicles [J]. Journal of Shanghai Jiao Tong University, 2015, 49(4): 481-486 (inbChinese).
