Integrated Nonlinear Observer and Prescribed Performance Control for a Four In-wheel Motor-Driven Electric Vehicles Based on Phase Plane and Road Classification

To effectively improve the performance of chassis control of a four in-wheel motor (IWM)-driven electric vehicles (EVs), especially in combing nonlinear observer and chassis control for improving road handling and ride comfort, is a challenging task for the IWM-driven EVs. Simultaneously, inaccurate state-based control and uncertainty with system input, are always existing, e.g., variable control boundary, varying road input or control parameters. Due to the higher fatality rate caused by variable factors, how to precisely chose and enforce the reasonable chassis prescribed performance control strategy of IWM-driven EVs become a hot topic in both academia and industry. To issue the above mentioned, the paper proposes a novel observer-based prescribed performance control to improve IWM-driven EVs chassis performance under the double lane change steering and various road input. Firstly, a nonlinear nine degree-of-freedom of full-car model is developed to describe vehicle chassis dynamics, and the method of phase plant based the proposed model is used to illustrate the stable boundary of the EVs. Also, a road identification method established using system response data based on the theory of deep neural networks (DNNs) to acquire road information. Secondly, a nonlinear observer is employed to acquire the state of slip angle and yaw rate in real time. The stability of the proposed observer is induced by using linear matrix inequalities (LMI). Based on the Lyapunov function, sliding mode theory and prescribed performance function (PPF), an observer-based prescribed performance control (PPC) strategy is proposed to constrain the controlled vehicle slip angle and yaw rate state within the prescribed performance boundaries. Finally, combing with a high-fidelity CarSim® software and a test rig platform, the proposed observer-based PPC algorithm is validated under the double lane change steering input. The research achievements develop a reasonable algorithm to apply to the improving road handling and ride comfort performance for a four IWM-driven EVs.