Neural network based feedback linearization control of a servo-hydraulic vehicle suspension system: This paper presents the design of a neural network based feedback linearization (NNFBL) controller for a two degree-of-freedom (DOF), quarter-car, servo-hydraulic vehicle suspension system. The main objective of the direct adaptive NNFBL controller is to improve the system's ride comfort and handling quality. A feedforward, multi-layer perceptron (MLP) neural network (NN) model that is well suited for control by discrete input-output linearization (NNIOL) is developed using input-output data sets obtained from mathematical model simulation. The NN model is trained using the Levenberg-Marquardt optimization algorithm. The proposed controller is compared with a constant-gain PID controller (based on the Ziegler-Nichols tuning method) during suspension travel setpoint tracking in the presence of deterministic road disturbance. Simulation results demonstrate the superior performance of the proposed direct adaptive NNFBL controller over the generic PID controller in rejecting the deterministic road disturbance. This superior performance is achieved at a much lower control cost within the stipulated constraints.

Review on hardfacing as method of improving the service life of critical components subjected to wear in service

This paper presents the design of a two-loop, force/suspension travel PID control system, for a four degree-of-freedom (DOF), nonlinear, half-car active vehicle suspension system (AVSS). The two-loop system consists of an inner PID hydraulic actuator force control loop and an outer PID suspension travel control loop. Performance of the PID based AVSS is compared to a passive, nonlinear, half-car suspension system with the same model parameters. The simulation results showed the superior performance of the AVSS in the presence of the deterministic road disturbance.