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Efficient and integration stable nonlinear model predictive controller for autonomous vehicles based on the stabilized explicit integration method
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DOI：10.1007/s11071-022-08081-3.png)
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There has been a growing interest in nonlinear model predictive control (NMPC) for the motion control of autonomous driving. However, it is odd that integration stability has not been considered enough when developing these applications for autonomous driving, which results in inefficient performance of NMPC-based motion controllers. The stabilized explicit Runge-Kutta (RK) integration method is adopted in this paper to solve the integration stability problem in motion control of autonomous vehicles (AVs). In comparison with other explicit integration methods, this method provides a wider stable region and is more efficient than implicit integration methods. This integration method is integrated into the framework of offset free nonlinear model predictive control (OF-NMPC) solver based on gradient-based MPC (GRAMPC) by us. As a result, the problem of computational stability at low speeds of motion control can be resolved. And, a larger integration step size can be adopted, NMPC becomes more computationally efficient. The results of simulation and real vehicle experiment show that the problem of low-speed integration stability when using nonlinear dynamic model as a prediction model is successfully solved. At the same time, since the offset free NMPC is adopted, the longitudinal and lateral steady-state errors are eliminated.
Keyword:
Path tracking
Integration stability
Nonlinear offset free MPC
Runge-Kutta-Chebyshev method
期刊
IF:
6
论文数:
1.4W
被引数:
4.1W
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