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It is of great significance to obtain road preview information using sensors to improve the ride comfort of intelligent vehicles. The explicit model predictive control (EMPC) algorithm offers advantages under a single road condition by integrating the road elevation with suspension state. However, EMPC requires adjusting the weighting coefficient to achieve optimal control effectiveness across varying road conditions. Therefore, an active suspension preview controller with a knowledge-guided weight optimization method is proposed. This article introduces an offline method to enhance particle swarm optimization (PSO) through the augmented Lagrangian method (ALM) to mitigate the ill-conditioned problem. Meanwhile, the analytic hierarchy process (AHP) is employed to determine initial particles based on artificial knowledge, thus improving the efficiency of the PSO. During online implementation of the preview EMPC, a weight switch strategy is deployed to decouple the complex road condition by identifying the road class. Subsequently, the preview control experiment using actual sensing data is conducted on an electronic control unit (ECU) in the loop test platform, demonstrating a significant improvement over EMPC without optimization.
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Zhao et al. (2026) studied this question.
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