Ghazally I. Y. MUSTAFA1*, Xinian LI1, Haoping WANG2
1 School of Information and Electronic Engineering, Shandong Technology and Business University,
191 Binhai Middle Road, Yantai, 264005, China
email@example.com (*Corresponding author), firstname.lastname@example.org
2 School of Automation, Nanjing University of Science and Technology Nanjing, 210094, China
Abstract: This paper presents a new controller for a nonlinear active vehicle suspension system, based on a combination between a time-delay control and adaptive neural network control (TDANNC). The main objective is to deal with a classical conflict between enhancing the comfort of the car, and at the same time, keeping the ride safety within acceptable safety limits. Based on time-delay control, the nonlinearity of the model and the external disturbances are replaced. Besides, a radial basis function neural network is added to time-delay control in order to obtain a compelling tracking trajectory. Moreover, an adaptive law mechanism is used to achieve excellent overall performance across various road profiles, which can quickly and precisely adjust the neural network control gain online based on the control error. The advantage of TDANNC is that it is quite a simple structure and can easily be regulated due to a smaller model. Using the Lyapunov theory, the theoretical study demonstrates the stability and finite-time convergence of the system. Finally, to demonstrate the performance of the proposed TDANNC, it is compared to the performances of the conventional passive system, of TDC, of PID, and of NNC controllers under three distinct road disturbances. The simulation results are carried out to show the success and efficiency of the suggested strategy.
Keywords: Active suspension system, Time-Delay Control (TDC), Adaptive Neural Network (ANN), Time-Delay Estimation (TDE).
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Ghazally I. Y. MUSTAFA, Xinian LI, Haoping WANG, A New Neural Network-Based Adaptive Time-Delay Control for Nonlinear Car Active Suspension System, Studies in Informatics and Control, ISSN 1220-1766, vol. 31(4), pp. 13-24, 2022. https://doi.org/10.24846/v31i4y202202