TY - JOUR
T1 - Handling-Stability Control for Distributed Drive Electric Vehicles via Lyapunov-Based Nonlinear MPC Algorithm
AU - Guo, Ningyuan
AU - Liu, Jin
AU - Li, Junqiu
AU - Chen, Weilin
AU - Zhang, Yunzhi
AU - Lu, Qinghua
AU - Chen, Zheng
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2025
Y1 - 2025
N2 - This article proposes a handling-stability control strategy for distributed drive electric vehicles (EVs) to improve motion performance. A motion supervisor, using only front steering angle feedback, is developed to evaluate the driving state and generate a unified yaw rate reference for handling-stability coordination. To ensure tracking convergence, a Lyapunov-based nonlinear model predictive control (LNMPC) strategy is proposed for direct yaw moment control (DYC), incorporating a contraction constraint to guarantee closed-loop stability, with rigorous proofs provided. For rapid problem-solving, a modified iterative linear quadratic regulator (iLQR) algorithm is developed, leveraging a relaxed log barrier function and double-loop iteration to handle inequality constraints, preventing violations and theoretically ensuring convergence to the original problem's solution. Additionally, an auxiliary control law is applied to generate the initial solution in iLQR, reducing sensitivity. Using a Karush-Kuhn-Tucker (KKT) conditions-based approach, the virtual control distribution is optimized efficiently, and the torque command of in-wheel motors (IWMs) can be gained. Simulations and hardware-in-the-loop (HIL) experiments demonstrate superior handling-stability performance and high computational efficiency with the proposed strategy.
AB - This article proposes a handling-stability control strategy for distributed drive electric vehicles (EVs) to improve motion performance. A motion supervisor, using only front steering angle feedback, is developed to evaluate the driving state and generate a unified yaw rate reference for handling-stability coordination. To ensure tracking convergence, a Lyapunov-based nonlinear model predictive control (LNMPC) strategy is proposed for direct yaw moment control (DYC), incorporating a contraction constraint to guarantee closed-loop stability, with rigorous proofs provided. For rapid problem-solving, a modified iterative linear quadratic regulator (iLQR) algorithm is developed, leveraging a relaxed log barrier function and double-loop iteration to handle inequality constraints, preventing violations and theoretically ensuring convergence to the original problem's solution. Additionally, an auxiliary control law is applied to generate the initial solution in iLQR, reducing sensitivity. Using a Karush-Kuhn-Tucker (KKT) conditions-based approach, the virtual control distribution is optimized efficiently, and the torque command of in-wheel motors (IWMs) can be gained. Simulations and hardware-in-the-loop (HIL) experiments demonstrate superior handling-stability performance and high computational efficiency with the proposed strategy.
KW - Direct yaw moment control (DYC)
KW - distributed drive electric vehicles (EVs)
KW - handling-stability control
KW - iterative linear quadratic regulator (iLQR)
KW - Lyapunov-based nonlinear model predictive control (LNMPC)
UR - http://www.scopus.com/inward/record.url?scp=105001481555&partnerID=8YFLogxK
U2 - 10.1109/TTE.2024.3513438
DO - 10.1109/TTE.2024.3513438
M3 - Article
AN - SCOPUS:105001481555
SN - 2332-7782
VL - 11
SP - 6615
EP - 6628
JO - IEEE Transactions on Transportation Electrification
JF - IEEE Transactions on Transportation Electrification
IS - 2
ER -