Novel semiactive suspension using a magnetorheological elastomer (MRE)-based absorber and adaptive neural network controller for systems with input constraints

Abstract. For most existing semiactive systems, it is commonly known that the stability and tracking performance will deteriorate in a real application due to the input constraints and nonlinearity in the system. In this study, in order to overcome the above shortcomings, a novel bench-scale suspension plant using a magnetorheological elastomer (MRE)-based absorber accompanied with an adaptive and global neural-network-based tracking controller is introduced. The adaptive neural network (ANN) is used to estimate the uncertain dynamics of the quarter-car model. The novel scheme consists of three parts, including a conventional ANN controller dominating the active region of neurons, a robust controller serving as a temporary controller to pull back the state into the active region when the neural approximation falls outside, and a switch to be used to monitor the activation of the neural part and switch the control authority between the above two controllers. The controller ensures that a globally uniform ultimate boundedness can be achieved. Furthermore, an auxiliary design system was added to the controller in order to deal with the effects of input constraints, and the state was analyzed for the tracking of the stabilization. The control scheme ensures that the output of the system converges to the vicinity of a reference trajectory and all the signals are globally, uniformly, and ultimately bounded. The simulation and experimental results demonstrate that the proposed controller can effectively suppress the vibrations of the semiactive quarter car.