Study on Impedance Control of Active Suspension

To address the challenges of parameter tuning in impedance controllers and their adaptability under varying operating conditions, this paper proposes a control method that combines the Beetle Antennae Search (BAS) algorithm with the BP neural network algorithm. A seven-degree-of-freedom full-vehicle suspension model is established, with the impedance control strategy as the core of the active suspension control. First, the BAS algorithm is used to optimize and tune the impedance parameters under different operating conditions to create a dataset. This dataset is then used to train a BP neural network, enabling the network to predict and adjust the impedance controller parameters in real time based on the current vehicle speed and road conditions. The simulation results of three typical working conditions show that, compared with the BP impedance controller with fixed parameters, the RMS of the vertical acceleration of the body is reduced by 43.08%, the RMS of the pitch acceleration of the body is reduced by 43.79%, and the RMS of the dynamic displacement of the front tire is reduced by 27.29% and 25.90%, respectively.