Neural network based adaptive event trigger control for a class of electromagnetic suspension systems

In this paper, a neural network (NN) based event trigger control problem of electromagnetic active suspension system is solved. Due to the limitation of vehicle communication resources, the control schemes utilizing fixed threshold and relative threshold are presented respectively to reduce the communication burden between actuator and controller. Firstly, the fixed threshold-based trigger mechanism is developed while the algebraic loop problem is addressed using the special characteristics of NN basis function. Second, to further avoid a large measurement error, the time-varying threshold-based event trigger approach is built. The designed event trigger controllers can make the vertical displacement and speed of the electromagnetic suspension system near zero. In the design process, the radial basis function neural networks (RBFNNs) are employed to approximate unknown terms. Then, all signals in the resulted system are proved to be bounded, and the Zeno behavior is avoided successfully. Finally, the feasibility and rationality of the two methods are proved by the simulation analysis base on the electromagnetic suspension system.