<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

Neural network based feedback linearization control of a servo-hydraulic vehicle suspension system

School of Mechanical, Aeronautical and Industrial Engineering, University of the Witwatersrand, 1 Jan Smuts Ave, Private Bag 03, WITS 2050, Johannesburg, South Africa

International Journal of Applied Mathematics and Computer Science

This paper presents the design of a neural network based feedback linearization (NNFBL) controller for a two degree-of-freedom (DOF), quarter-car,...

Neural network based feedback linearization control of a servo-hydraulic vehicle suspension system

Online veröffentlicht: 28. März 2011

Seitenbereich: 137 - 147

DOI: https://doi.org/10.2478/v10006-011-0010-5

Schlüsselwörter
neural networks, direct adaptive control, feedback linearization control, PID control, ride comfort, suspension system, servo-hydraulics

This content is open access.

Neural network based feedback linearization control of a servo-hydraulic vehicle suspension system

Online veröffentlicht: 28. März 2011

Seitenbereich: 137 - 147

DOI: https://doi.org/10.2478/v10006-011-0010-5

Schlüsselwörterneural networks, direct adaptive control, feedback linearization control, PID control, ride comfort, suspension system, servo-hydraulics

This content is open access.

Schlüsselwörter
neural networks, direct adaptive control, feedback linearization control, PID control, ride comfort, suspension system, servo-hydraulics