Model predictive path following control for autonomous cars considering a measurable disturbance: Implementation, testing, and verification

Guo, Hongyan; Cao, Dongpu; Chen, Hong; Sun, Zhenping; Hu, Yunfeng

doi:10.1016/j.ymssp.2018.08.028

Cited by 158 publications

(70 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Refs. [16,17] set the initial values as a zeros vector, but that is appropriate only if the controller starts when the vehicle is standstill. Other approaches include numerical iteration methods [18,19] to work out an appropriate initial solution; however, they are not real-time applicable.…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Nonlinear Model Predictive Controller for Yaw Motion Optimization of Distributed Drive Electric Vehicles

Guo

Lenzo

Zhang

et al. 2020

IEEE Trans. Veh. Technol.

134

View full text Add to dashboard Cite

This paper proposes a real-time nonlinear model predictive control (NMPC) strategy for direct yaw moment control (DYC) of distributed drive electric vehicles (DDEVs). The NMPC strategy is based on a control-oriented model built by integrating a single track vehicle model with the Magic Formula (MF) tire model. To mitigate the NMPC computational cost, the continuation/generalized minimal residual (C/GMRES) algorithm is employed and modified for real-time optimization. Since the traditional C/GMRES algorithm cannot directly solve the inequality constraint problem, the external penalty method is introduced to transform inequality constraints into an equivalently unconstrained optimization problem. Based on the Pontryagin's minimum principle (PMP), the existence and uniqueness for solution of the proposed C/GMRES algorithm are proven. Additionally, to achieve fast initialization in C/GMRES algorithm, the varying predictive duration is adopted so that the analytic expressions of optimally initial solutions in C/GMRES algorithm can be derived and gained. A Karush-Kuhn-Tucker (KKT) condition based control allocation method distributes the desired traction and yaw moment among four independent motors. Numerical simulations are carried out by combining CarSim and Matlab/Simulink to evaluate the effectiveness of the proposed strategy. Results demonstrate that the real-time NMPC strategy can achieve superior vehicle stability performance, guarantee the given safety constraints, and significantly reduce the computational efforts. Index Terms-continuation/generalized minimal residual algorithm; direct yaw moment control; distributed drive electric vehicle; nonlinear model predictive control.

show abstract

Section: Introductionmentioning

confidence: 99%

A Real-Time Nonlinear Model Predictive Controller for Yaw Motion Optimization of Distributed Drive Electric Vehicles

Guo

Lenzo

Zhang

et al. 2020

IEEE Trans. Veh. Technol.

134

View full text Add to dashboard Cite

show abstract

“…y 2,max = β max V x (t 0 ) and u max = δ max are the constraint upper bounds. u TS is the auxiliary http://engine.scichina.com/doi/10.1007/s11432-018-9790-3 triple-step control law (5) and V (•) is the corresponding Lyapunov function. With the contraction constraint (9f), TS-MPC is enforced to follow the default stability property of the triple-step control u TS (•).…”

Section: Wang Y L Et Al Sci China Inf Scimentioning

confidence: 99%

“…Moreover, Guo et al [4] proposed a simultaneous trajectory planning and tracking controller for use under cruise conditions to address obstacle avoidance for an intelligent vehicle. A generalized path-following control considering a measurable disturbance was developed for implementation, testing, and verification in [5]. In addition, fault-tolerant control in discrete time and differential steering solutions for intelligent electric vehicles were investigated in [6,7].…”

mentioning

confidence: 99%

Path-following control of autonomous ground vehicles using triple-step model predictive control

Wang

Zheng

Zong

et al. 2020

Sci. China Inf. Sci.

Self Cite

View full text Add to dashboard Cite

“…Yaw rate, lateral acceleration, and steering wheel angle can be considered as constraints in trajectory tracking control strategy to prevent the sudden change of steering angle from causing the negative impact on driving stability [16][17][18]. A nonlinear MPC that combines tire model with model prediction aiming at minimizing the tracking error and stabilizing the vehicle is also illustrated [19,20].…”

Section: Introductionmentioning

confidence: 99%

Coordinated Stability Control Strategy for Intelligent Electric Vehicles Using Vague Set Theory

Zhao

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Aiming at improving the tracking stability performance for intelligent electric vehicles, a novel stability coordinated control strategy based on preview characteristics is proposed in this paper. Firstly, the traditional stability control target is introduced with the two degrees of freedom model, which is realized by the sliding mode control strategy. Secondly, an auxiliary control target further amending the former one with the innovation formulation of the preview characteristics is established. At last, a multiple purpose Vague set leverages the contribution of the traditional target and the auxiliary preview target in various vehicle states. The proposed coordinated control strategy is analyzed on the MATLAB/CarSim simulation platform and verified on an intelligent electric vehicle established with A&D5435 rapid prototyping experiment platform. Simulation and experimental results indicate that the proposed control strategy based on preview characteristics can effectively improve the tracking stability performance of intelligent electric vehicles. In the double lane change simulation, the peak value of sideslip angle, yaw rate, and lateral acceleration of the vehicle is reduced by 13.2%, 11.4%, and 8.9% compared with traditional control strategy. The average deviations between the experimental and simulation results of yaw rate, lateral acceleration, and steering wheel angle are less than 10% at different speeds, which demonstrates the consistency between the experimental and the simulation results.

show abstract

Model predictive path following control for autonomous cars considering a measurable disturbance: Implementation, testing, and verification

Cited by 158 publications

References 19 publications

A Real-Time Nonlinear Model Predictive Controller for Yaw Motion Optimization of Distributed Drive Electric Vehicles

A Real-Time Nonlinear Model Predictive Controller for Yaw Motion Optimization of Distributed Drive Electric Vehicles

Path-following control of autonomous ground vehicles using triple-step model predictive control

Coordinated Stability Control Strategy for Intelligent Electric Vehicles Using Vague Set Theory

Contact Info

Product

Resources

About