Elegant anti-disturbance control for stochastic systems with multiple heterogeneous disturbances based on fuzzy logic systems

There are a large number of non-harmonic disturbances generated by nonlinear exogenous systems in realistic engineering. The current disturbance observer is not applicable for estimating the non-harmonic disturbance with unknown nonlinear dynamics, thus greatly reducing the accuracy of the controller. This paper addresses a class of stochastic systems with multiple heterogeneous disturbances including white noise and non-harmonic disturbance with unknown smooth nonlinear function, which can be approximated by fuzzy logic systems. Based on the approximation of the unknown nonlinear function, an adaptive disturbance observer (ADO) is constructed to estimate non-harmonic disturbance. Combining disturbance observer-based control with fuzzy control, an elegant anti-disturbance control (EADC) scheme is proposed such that the composite system achieves asymptotically bounded in mean square. Simulation examples show that the state responses of the system gradually approache [Formula: see text] from divergence, indicating that the effectiveness of the controller is satisfactory. In addition, the anti-disturbance control accuracy of EADC approximately improves [Formula: see text] times compared with [Formula: see text] control. The simulation results demonstrate the feasibility and effectiveness of the proposed scheme.

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Universal Approximation of a Class of Interval Type-2 Fuzzy Neural Networks in Nonlinear Identification

Advances in Fuzzy Systems ◽

10.1155/2013/136214 ◽

2013 ◽

Vol 2013 ◽

pp. 1-16 ◽

Cited By ~ 16

Author(s):

Oscar Castillo ◽

Juan R. Castro ◽

Patricia Melin ◽

Antonio Rodriguez-Diaz

Keyword(s):

Neural Networks ◽

Fuzzy Logic ◽

Nonlinear Function ◽

Fuzzy Neural Networks ◽

Universal Approximation ◽

Fuzzy Logic Systems ◽

Fuzzy Neural ◽

Logic Systems ◽

Interval Type

Neural networks (NNs), type-1 fuzzy logic systems (T1FLSs), and interval type-2 fuzzy logic systems (IT2FLSs) have been shown to be universal approximators, which means that they can approximate any nonlinear continuous function. Recent research shows that embedding an IT2FLS on an NN can be very effective for a wide number of nonlinear complex systems, especially when handling imperfect or incomplete information. In this paper we show, based on the Stone-Weierstrass theorem, that an interval type-2 fuzzy neural network (IT2FNN) is a universal approximator, which uses a set of rules and interval type-2 membership functions (IT2MFs) for this purpose. Simulation results of nonlinear function identification using the IT2FNN for one and three variables and for the Mackey-Glass chaotic time series prediction are presented to illustrate the concept of universal approximation.

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Adaptive nonlinear disturbance observer-based control for stochastic systems with multiple heterogeneous disturbances

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331220904573 ◽

2020 ◽

Vol 42 (11) ◽

pp. 2020-2030

Author(s):

Xinqing Li ◽

Xinjiang Wei ◽

Huifeng Zhang ◽

Jian Han ◽

Xin Hu ◽

...

Keyword(s):

Disturbance Observer ◽

Composite System ◽

Stochastic Systems ◽

Nonlinear Function ◽

Control Input ◽

System State ◽

Nonlinear Disturbance Observer ◽

Nonlinear Disturbance ◽

Nonlinear Signal ◽

And Control

The problem of anti-disturbance control is studied for a class of stochastic systems with multiple heterogeneous disturbances, which include three kinds of disturbance. One is the non-harmonic disturbance coupled with system state and control input. The other one is an unexpected nonlinear signal described as a nonlinear function. The third one is white noise. An adaptive nonlinear disturbance observer (ANDO) is constructed to estimate non-harmonic disturbance. Based on which, a new adaptive nonlinear disturbance observer-based control (ANDOBC) strategy is developed such that the composite system is asymptotically bounded in mean square. Simulation results are given to show its effectiveness of the proposed method.

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Composite hierarchical anti-disturbance control for stochastic systems with multiple heterogeneous disturbances

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331219858786 ◽

2019 ◽

Vol 41 (15) ◽

pp. 4398-4408

Author(s):

Yongli Wei ◽

Xinjiang Wei ◽

Huifeng Zhang ◽

Jian Han

Keyword(s):

Neural Network ◽

Lyapunov Function ◽

Linear Matrix Inequality ◽

Disturbance Observer ◽

Composite System ◽

Stochastic Systems ◽

Dynamic Performance ◽

Nonlinear Function ◽

Linear Matrix ◽

Matrix Inequality

This paper studies the problem of anti-disturbance control for a class of stochastic systems with multiple heterogeneous disturbances, which include the white noise and the non-harmonic disturbance with unknown nonlinear function. An adaptive disturbance observer is constructed to estimate the non-harmonic disturbances with unknown nonlinear function, which is approximated by neural network. A composite hierarchical anti-disturbance control (CHADC) scheme is designed by integrated Lyapunov function and linear matrix inequality (LMI), such that the expected dynamic performance of the composite system is achieved. Finally, simulations show that the approach is proper and effective.

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Special Issue: Fuzzy logic systems for transportation engineering

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189957 ◽

2021 ◽

pp. 1-8

Author(s):

Dalin Zhang ◽

Sabah Mohammed ◽

Alessandro Calvi

Keyword(s):

Fuzzy Logic ◽

Transportation Engineering ◽

Special Issue ◽

Fuzzy Logic Systems ◽

Logic Systems

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Design of back propagation optimized Nagar-Bardini structure-based interval type-2 fuzzy logic systems for fuzzy identification

Transactions of the Institute of Measurement and Control ◽

10.1177/01423312211006635 ◽

2021 ◽

pp. 014233122110066

Author(s):

Yang Chen ◽

Jiaxiu Yang

Keyword(s):

Fuzzy Logic ◽

Back Propagation ◽

Membership Functions ◽

Identification Problems ◽

Fuzzy Logic Systems ◽

Fuzzy Identification ◽

Logic Systems ◽

Academic Topic ◽

Interval Type

In recent years, fuzzy identification based on system identification theory has become a hot academic topic. Interval type-2 fuzzy logic systems (IT2 FLSs) have become a rising technology. This paper designs a type of Nagar-Bardini (NB) structure-based singleton IT2 FLSs for fuzzy identification problems. The antecedents of primary membership functions of IT2 FLSs are chosen as Gaussian type-2 primary membership functions with uncertain standard deviations. Then, the back propagation algorithms are used to tune the parameters of IT2 FLSs according to the chain rule of derivation. Compared with the type-1 fuzzy logic systems, simulation studies show that the proposed IT2 FLSs can obtain better abilities of generalization for fuzzy identification problems.

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