Vibration protection of the hydraulic suspension system of mobile vehicles using active regulation by a neural network controller

Most of the time of operation, wheeled agricultural tractors work in fields with different soil back-grounds and in transport on dirt roads or even off-road. Under these conditions, a suspension system is needed to ensure the smoothness ride and effective vibration protection of the operator. Purpose of the study. To develop a new sliding mode controller based on radial basis functions in a neural network (RBFNN), implemented for an active suspension system of ¼ of a part of a mobile energy facility (MF). Materials and methods. The control algorithm is based on radial basis functions and combines the advantage of an adaptive control system and slip mode control. The adaptation rule is used to regulate basic functions based on information about a given sliding surface in real time. Since this approach has the ability to learn, its implementation can be started without any initial RBFNN values. It is proposed to use the neural network to control the parameters of the sliding mode. Results and discussion. To control the active MF suspension system, an adaptive RBFNN sliding mode controller is proposed. The adjustable parameter for the RBFSS controller is selected as gs = 8,5 to cover the range of Gaussian functions. Conclusions. A new controller for the sliding mode of a hydraulically active suspension system based on a neural network is proposed. Simulation results show that due to the use of this neural network controller, the suspension system quite effectively reduces the vibrational activity of the oscillatory system of the studied MF from road irregularities.

comparative evaluation, hydraulic suspension, active suspension system, neural network