In this paper, we present an online adaptive model predictive control (MPC) method for the robot driver speed control problem. The control structure contains three essential components: a regularized least square method to identify a time varying state space model for the vehicle system; a Kalman Filter (KF) to identify the internal states of the model; and a model predictive controller (MPC) to generate the control input based on properly constructed optimization objective and constraints. We cast the robot driver speed tracking problem as an acceleration tracking problem which we solve as an optimization problem with time varying constraints that are dependent on vehicle speed and acceleration. Simulation results on the FTP city drive cycle shows that the proposed approach has good speed tracking capability with minimal calibration efforts. and the controller is adaptive to variation on vehicle weight without degrading control performance.
- Dynamic Systems and Control Division
An Online Model Predictive Control Framework for Robot Driver Speed Control
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Wang, J, Wang, Y, & Filev, D. "An Online Model Predictive Control Framework for Robot Driver Speed Control." Proceedings of the ASME 2018 Dynamic Systems and Control Conference. Volume 1: Advances in Control Design Methods; Advances in Nonlinear Control; Advances in Robotics; Assistive and Rehabilitation Robotics; Automotive Dynamics and Emerging Powertrain Technologies; Automotive Systems; Bio Engineering Applications; Bio-Mechatronics and Physical Human Robot Interaction; Biomedical and Neural Systems; Biomedical and Neural Systems Modeling, Diagnostics, and Healthcare. Atlanta, Georgia, USA. September 30–October 3, 2018. V001T10A002. ASME. https://doi.org/10.1115/DSCC2018-8957
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