Active Adaptive Estimation and Control for Vehicle Suspensions With Prescribed Performance

Jing Na, Yingbo Huang, Xing Wu, Guanbin Gao, Guido Herrmann, Jason Zheng Jiang

Research output: Contribution to journalArticle (Academic Journal)peer-review

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Abstract

This paper proposes an adaptive control for vehicle active uspensions with unknown nonlinearities (e.g., nonlinear springs and piecewise dampers). A prescribed performance function that characterizes the convergence rate, maximum overshoot, and steady-state error is incorporated into the control design to stabilize the vertical and pitch motions, such that both the transient and steady-state suspension response are guaranteed. Moreover, a novel adaptive law is used to achieve precise estimation of essential parameters (e.g., mass of vehicle body and moment of inertia for pitch motion), where the parameter estimation error is obtained explicitly and then used as a new leakage term. Theoretical studies prove the convergence of the estimated parameters, and compare the suggested controller with generic adaptive controllers using the gradient descent and e-modification schemes. In addition to motion displacements, dynamic tire loads and suspension travel constraints are also considered. Extensive comparative simulations on a dynamic simulator consisting of commercial vehicle simulation software Carsim 8.1 and MATLAB Simulink are provided to show the efficacy of the proposed control, and to illustrate the improved performance. Index Terms—Active suspension systems, adaptive control, neural network (NN), parameter estimation, prescribed performance.
Original languageEnglish
Number of pages15
JournalIEEE Transactions on Control Systems Technology
Early online date28 Sep 2017
DOIs
Publication statusE-pub ahead of print - 28 Sep 2017

Keywords

  • Active suspension systems
  • Adaptive control
  • Parameter estimation
  • Prescribed performance
  • Neural network

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