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

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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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Slip Angle Estimation; Electric Vehicles; Kalman Filter; Local Models; LMI Design

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Larbi, B., Alimi, W., Chouikh, R., Guizani, A., Geometrical parameters influence on PEM fuel cell performance, (2013) International Review on Modelling and Simulations (IREMOS), 6 (4), pp. 1363-1370.

Ibrahim, H.E.A., Elnady, M.A., A comparative study of PID, fuzzy, fuzzy-PID, PSO-PID, PSO-fuzzy, and PSO-fuzzy-PID controllers for speed control of DC motor drive, (2013) International Review of Automatic Control (IREACO), 6 (4), pp. 393-403.

Allirani, S., Jagannathan, V., Field programmable gate array based direct torque control of induction motor drive, (2013) International Review of Automatic Control (IREACO), 6 (4), pp. 373-380.

Yoichi Hori, “Future Vehicle Driven by Electricity and Control Research on 4 Wheel Motored “UOT March II” ”, in AMC2002 Proc. (7th International Workshop on Advanced Motion Control Proceedings), pp.1-14, 2002.

M. Canale, L. Fagiano, A. Ferrara, C. Vecchio, "Vehicle Yaw Control via Second-Order Sliding-Mode Technique," IEEE Trans. on Industrial Electronics, vol. 55, no. 11, pp. 3908-3916, Nov 2008.

Cong Geng, Lotfi Mostefai, Mouloud Denai, and Yoichi Hori, “ Direct Yaw-Moment Control of an In-Wheel-Motored Electric Vehicle Based on Body Slip Angle Fuzzy Observer ”. IEEE Trans. on Industrial Electronics, Vol.56, No.5, pp.1411-1419, May 2009.

P. Gahinet, A. Nemirovski, A. J. Laub, and M. Chilali, LMI Control Toolobox for Use With Matlab, The Mathworks Inc. 1995.

P. T. Takagi and M. Sugeno, ‘‘Fuzzy identification of systems and its applications to modeling and control,’’ IEEE Trans. Syst. Man. Cyber., Vol. 15, pp. 116_132,1985.

R. Babuska and H. Verbruggen, “An Overview of Fuzzy Modeling for Control”, Control Engineering Practice, vol. 4, no. 11, pp. 1593 – 1606, 1996.

J. Th. Paul, Venhovens, K. Naab, “Vehicle Dynamics Estimation Using Kalman Filters”, Vehicle System Dynamics, Vol.32, pp. 171-184, 1999.


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