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A Decentralized Adaptive Neural Controller for Active Vehicle Suspension Systems

[+] Author Affiliations
Kuo-Ching Chiou, Shiuh-Jer Huang

National Taiwan University of Science and Technology, Taipei, Taiwan

Paper No. ESDA2006-95083, pp. 63-68; 6 pages
doi:10.1115/ESDA2006-95083
From:
  • ASME 8th Biennial Conference on Engineering Systems Design and Analysis
  • Volume 3: Dynamic Systems and Controls, Symposium on Design and Analysis of Advanced Structures, and Tribology
  • Torino, Italy, July 4–7, 2006
  • ISBN: 0-7918-4250-9 | eISBN: 0-7918-3779-3
  • Copyright © 2006 by ASME

abstract

A decentralized adaptive neural controller (DANC) for active vehicle suspension systems using singular perturbation method is developed in this paper. In order to reduce the neural network (NN) weight’s number, the DANC is designed based on the decentralized single input single output (SISO) NN. The active suspension system SIMO characteristic is decoupled into two time-scale separated SISO systems via the singular perturbation technique for achieving a compromise between riding comfort and handing performance. The structure of this MIMO model-free controller is derived from the Lyapunov stability theory to monitor the system for tracking a user-defined reference model. The experimental results are discussed and compared with that of the passive suspension.

Copyright © 2006 by ASME

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