Optimal selection of weighting functions by genetic algorithms to design H∞ Anti-roll bar controllers for heavy vehicles
Résumé
Multi-criterion optimization is so far popular for many complex engineering problems. The objective of active anti-roll bar of heavy vehicles is to maximize roll stability to prevent rollover in dangerous cases. However, such a performance objective must be balanced with the energy consumption of the anti-roll bar system, which is not a trivial task. In a previous work, the authors proposed an H∞ active anti-roll bar controller for which the weighting functions were chosen by trials and errors during the design step. In this paper, Genetic Algorithms (GAs) are proposed to find optimal weighting functions for the H∞ control synthesis. Such a general procedure is applied to the case of active anti-roll bar control in heavy vehicles. Thanks to GAs, the conflicting objectives between roll stability and torques generated are handled using one high level parameter only. The multi-criterion optimization solution is illustrated via the Pareto frontier. Simulations, performed in the frequency and time domains, emphasize the efficiency of the proposed method.
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