A model predictive control approach for angle tracking of steer‐by‐wire system with nonlinear transmission ratio

Abstract

Steer‐by‐wire for full self‐driving vehicles requires rapid angle tracking in the process of steering, making steering system stability paramount. This is particularly challenging for steering wheels accurately without handwheel, for which road feedback information is scarce. While much of the research on steer‐by‐wire has focused on improving the stability of vehicle dynamics, comparatively little is known about the angle tracking control of steer‐by‐wire system for full self‐driving vehicle. Here, we discuss a series of studies on the steering dynamics model and nonlinear transmission ratio of steering system that, collectively, design an angle tracking approach of how the model predictive control algorithm steers the front wheels precisely. The designed approach has been downloaded into a steering control unit and tested by some steering cycles in a steering test bench to fully realize angle tracking control of steer‐by‐wire system in practical engineering.

Angle demand control of steer-by-wire system by disturbance estimation–based generalized predictive control approach

“…Based on the open-loop optimization of a cost function, it can be considered as a kind of optimal control method regarding the difference between the output and the trajectory to be tracked at each sampling time. 15,16 The characteristic of the MPC algorithm is consistent with that of the research problem in this article. However, a nonlinear optimization problem has to be solved online to determine the optimal control, which limits its applications to nonlinear systems for a long time.…”

“…Controller for comparison: the MPC controller with integral action (IMPC) 16 and the GPC controller (doGPC without considering the disturbances) are selected as the comparative controllers and the settings of controller parameters are given as follows. IMPC: 16 The predictive horizon is N p =10 and the control horizon is N c = 1 . GPC: The parameter of the GPC controller is T θ = 0 . 06 .…”

“…Disturbance estimation–based robust GPC. Compared with the existing MPC methods in the SBW system, 8,16,26 the designed disturbance estimation–based GPC controller has the following merits: (1) the output prediction accuracy is corrected via an NDO in the receding optimization process; (2) an explicit analytical form of the continuous-time predictive controller is derived, hence avoiding a large computational quantity; (3) The disturbance rejection performance is achieved without sacrificing the nominal tracking performance of the GPC controller.…”

State‐dependent indirect hp‐pseudospectral method for rigid spacecraft attitude optimal control problem

“…To improve the robustness of the controller to handle the environment and uncertainties of external disturbances and rotational inertia, the receding horizon control [41,42] is introduced to SDIHP method.…”