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

Pedro J. O., Dahunsi O. A.

International Journal of Applied Mathematics and Computer Science

2011

Vol. 21, no 1

137-147

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 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.

Decentralized direct adaptive fuzzy control of non-linear interconnected MIMO system class

Tlemcani A., Chekireb H., Boucherit M. S., Labiod S.

Archives of Control Sciences

2007

Vol. 17, no. 4

409-425

In this paper, we propose a decentralized direct adaptive fuzzy control method for a class interconnected MIMO non linear plant encountered mainly in robotics. The establishment of the control law introduces very simplest assumptions. Indeed, the functions incorporating the plant dynamic must be continuous and the interconnection terms are bounded by unknown bounds. The fuzzy direct adaptive law is designed to compensate for the interconnections effect and to ensure the closed-loop stability, convergence of the controlled outputs and `boundedness' of adaptation parameters. The proposed method is tested by simulation on the robot Puma 560. In this test the robot is controlled in the operational space as that the robot tip follows a prescribed curve on the sphere where the orientation of the last link (sixth) is maintained radial related to the center of this sphere.