Particle Swarm Optimized Intelligent Control of Nonlinear Full-Car Electrohydraulic Suspensions
نویسندگان
چکیده
This paper presents the design of an indirect adaptive feedback linearization (FBL)based control using dynamic neural networks (DNN) for full-car nonlinear electrohydraulic suspensions. Particle swarm optimization (PSO) algorithm is used in training the DNN to learn the dynamics of the system. A multi-loop, PSO-optimized proportional-integral-derivative (PID) control is implemented for the feedback-linearized DNN model to improve system performance. The proposed control scheme outperformed the passive vehicle suspension system (PVSS) and the benchmark PSO-optimized PID controller.
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