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.

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