LMI Design of a Direct Yaw Moment Robust Controller Based on Adaptive Body Slip Angle Observer for Electric Vehicles

Abstract

A stabilizing observer based control algorithm for an in-wheel-motored vehicle is proposed, which generates direct yaw moment to compensate for the state deviations. The control scheme is based on a fuzzy rule-based body slip angle (β) observer. In the design strategy of the fuzzy observer, the vehicle dynamics are represented by local models. Initially, local equivalent vehicle models have been built using linear approximations of vehicle dynamics respectively for low and high lateral acceleration operating regimes. The optimal β observer is then designed for each local model using Kalman filter theory.
Finally, local observers are combined to form the overall controlled system by using fuzzy rules. These fuzzy rules consequently represent the qualitative relationships among the variables associated with the nonlinear and uncertain nature of vehicle dynamics, such as tire force saturation and the influence of road adherence. An adaptation mechanism has been introduced within the fuzzy design and incorporated to improve the accuracy and performance of the controlled system. The controller can then be robustly synthesized based on Linear Matrix Inequalities and using the deviation states model. The controller-observer pair gives good performances in term of stability and presents convincing advantages regarding the real-time implementation issues. The effectiveness of this design approach has been demonstrated in simulations and using real-time experimental data.

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