Neural network-based model predictive control of a servo-hydraulic vehicle suspension system

Abstract

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.

Modelling and control of tinning line entry section using neural networks

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Proportional-integral-derivative control of nonlinear half-car electro-hydraulic suspension systems

“…These include optimal control (Hassanzadeh et al, 2010), H 2 (Pedro, 2007), H ∞ (Chen et al, 2005;Du and Zhang, 2007;Ryu et al, 2008), H 2 /H ∞ (Akcay and Turkay, 2009), linear parameter varying (LPV) (Fialho and Balas, 2002;Szaszi et al, 2002), sliding mode control (SMC) (Yoshimura et al, 2001), fuzzy logic control (FLC) (Du and Zhang, 2009a), backstepping control (Yagiz and Hacioglu, 2008), feedback linearization (FBL) (Chien et al, 2008;Fateh and Alavi, 2009), and various neural network (NN)-based control methods (Buckner et al, 2000;Dahunsi et al, 2009;Pedro and Dahunsi, 2011).…”

“…This is because the main focus was on calculation of the required control force, excluding the dynamics of the force generating actuators (Chantranuwathana and Peng, 2004;Sam and Hudha, 2006). Although many other types of actuators have been proposed in the literature, hydraulic actuators are the most common in AVSS owing to their rapid response time, high stiffness, superior power-to-weight ratio, low cost, and low heat dissipation during periods of sustained force generation (Dahunsi et al, 2009;Pedro and Dahunsi, 2011).…”