Adaptive Fuzzy Control for Stochastic High-Order Nonlinear Systems With Output Constraints

Liandi Fang,Shihong Ding,Li Ma,Ju H. Park

doi:10.1109/tfuzz.2020.3005350

Abstract

This article investigates the adaptive fuzzy control design for p-norm stochastic high-order lower triangular nonlinear systems with output constraints and unknown nonlinearities. First of all, a tan-type barrier Lyapunov function (BLF) is constructed to deal with the output constraint issue. Subsequently, an adaptive fuzzy control algorithm is developed by combining the constructed BLF with adding a power integrator technique. Simultaneously, the Lyapunov analysis shows that the designed controller can guarantee the boundness of all the variables in the closed-loop system in probability without violating the given output constraint. Finally, some comparative simulation results are provided to demonstrate the effectiveness of the proposed method.

Highlights

D UE TO its important applications in the industry, the control design problem of stochastic nonlinear systems has drawn great attention in the past years
The stability analysis and a variety of control design methods have been well studied by common Lyapunov functions (CLFs) and the backstepping technique [1]–[7]
To overcome the obstacle arises from the unknown nonlinearities, the fuzzy logic systems (FLSs) or neural networks (NNs) have been usually employed to deal with unknown functions

Summary

Introduction

D UE TO its important applications in the industry, the control design problem of stochastic nonlinear systems has drawn great attention in the past years. To overcome the obstacle arises from the unknown nonlinearities, the fuzzy logic systems (FLSs) or neural networks (NNs) have been usually employed to deal with unknown functions.

Objectives

Results

Conclusion