This paper presents the design of a neural network based feedback linearization (NNFBL) controller for a two degree-offreedom (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 LevenbergMarquardt 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.

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This paper presents the design of a multi-layer feedforward neural network-based model predictive controller (NNMPC) for a two degree-of-freedom (DOF), quarter-car servohydraulic vehicle suspension system. The nonlinear dynamics of the servo-hydraulic actuator is incorporated in the suspension model and thus a suspension travel controller is developed to indirectly improve the ride comfort and handling quality of the suspension system. A SISO feedforward multi-layer perceptron (MLP) neural network (NN) model is developed using input-output data sets obtained from the mathematical model simulation. Levenberg-Marquandt algorithm was employed in training the NN model. The NNMPC was used to predict the future responses that are optimized in a sub-loop of the plant for cost minimization. The proposed controller is compared with an optimally tuned constant-gain PID controller (based on Ziegler-Nichols tuning method) during suspension travel setpoint tracking in the presence of deterministic road input disturbance. Simulation results demonstrate the superior performance of the NNMPC over the generic PID -based in adapting to the deterministic road disturbance.

This paper presents a differential-evolution- (DE-) optimized, independent multiloop proportional-integral-derivative (PID) controller design for full-car nonlinear, electrohydraulic suspension systems. The multiloop PID control stabilises the actuator via force feedback and also improves the system performance. Controller gains are computed using manual tuning and through DE optimization to minimise a performance index, which addresses suspension travel, road holding, vehicle handling, ride comfort, and power consumption constraints. Simulation results showed superior performance of the DE-optimized PID-controlled active vehicle suspension system (AVSS) over the manually tuned PID-controlled AVSS and the passive vehicle suspension system (PVSS).

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 suspen sion 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.

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