An interval type-2 fuzzy controller for vehicle active suspension systems

An interval type-2 fuzzy controller for vehicle active suspension systems

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A novel interval type-2 fuzzy controller architecture is proposed for resolving nonlinear control problems of vehicle active suspension systems. It integrates Takagi-Sugeno (T-S) fuzzy model, interval type-2 fuzzy reasoning, the Wu-Mendel uncertainty bounds method, and selected optimization algorithms in order to construct the switching routes between generated linear model control surfaces. The stability analysis of the proposed approach is presented. The proposed method is implemented into a numerical example and a case study on a nonlinear half-vehicle active suspension system. The simulation results demonstrate the effectiveness and efficiency of the proposed approach.

Chapter Contents:

  • Abstract
  • 4.1 Introduction
  • 4.2 A nonlinear active suspension system
  • 4.3 The interval type-2 T-S fuzzy control system
  • 4.3.1 The general T-S fuzzy model and fuzzy control system
  • 4.3.2 The interval type-2 T-S fuzzy control system
  • 4.3.3 The proposed IT2 T-S fuzzy control system
  • 4.4 Stability analysis of the IT2 T-S fuzzy control system
  • 4.5 Simulation examples
  • 4.5.1 A numerical example
  • 4.5.2 A half-vehicle active suspension system
  • 4.6 Concluding remarks
  • References

Inspec keywords: suspensions (mechanical components); fuzzy control; road vehicles; uncertainty handling; stability; nonlinear control systems; optimisation; fuzzy reasoning

Other keywords: Wu-Mendel uncertainty bounds method; vehicle active suspension systems; switching routes; nonlinear control problems; nonlinear half-vehicle active suspension system; Takagi-Sugeno model; generated linear model control surfaces; optimization algorithms; T-S fuzzy model; interval type-2 fuzzy reasoning; interval type-2 fuzzy controller architecture; stability analysis

Subjects: Stability in control theory; Nonlinear control systems; Road-traffic system control; Control technology and theory; Optimisation techniques; Optimisation; Reasoning and inference in AI; Mechanical components; Fuzzy control; Vehicle mechanics

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