Robust H∞ control of multiple time-delay uncertain nonlinear system using fuzzy model and adaptive neural network

Shou-song, HU; Liu, Ya

doi:10.1016/j.fss.2003.09.009

Cited by 26 publications

(21 citation statements)

References 25 publications

(28 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The stability property derived in Theorem 2 is not affected when using the on-line tuning laws (27) and (28). This result should confirm the following conditions: (i) n R i=1w i >0; (ii) m i j , i j ∈ L ∞ ; and (iii) i j >0.…”

Section: Remarkmentioning

confidence: 60%

“…If both uncertainties and control force are zero, the nonlinear system is chaotic (cf. [27]). Assumptions 1-3 are easily satisfied for the above system.…”

Section: X(t) = (A + A(t))x(t)+ Bg −1 (X(t))(u(t)+ (X(t)))mentioning

confidence: 99%

“…In comparison, the fuzzy membership adjustment leads to smoother control force, i.e. the effect of (27) and (28) has been shown.…”

Section: Examplementioning

confidence: 99%

See 2 more Smart Citations

Adaptive TS‐FNN control for a class of uncertain multi‐time‐delay systems: The exponentially stable sliding mode‐based approach

Chiang

Chiu

Liu

2008

Adaptive Control & Signal

View full text Add to dashboard Cite

This paper presents an adaptive Takagi-Sugeno fuzzy neural network (TS-FNN) control for a class of multiple time-delay uncertain nonlinear systems. First, we develop a sliding surface guaranteed to achieve exponential stability while considering mismatched uncertainty and unknown delays. This exponential stability result based on a novel Lyapunov-Krasovskii method is an improvement when compared with traditional schemes where only asymptotic stability is achieved. The stability analysis is transformed into a linear matrix inequalities problem independent of time delays. Then, a sliding mode control-based TS-FNN control scheme is proposed to achieve asymptotic stability for the controlled system. Since the TS-FNN combines TS fuzzy rules and a neural network structure, fewer numbers of fuzzy rules and tuning parameters are used compared with the traditional pure TS fuzzy approach. Moreover, all the fuzzy membership functions are tuned on-line even in the presence of input uncertainty. Finally, simulation results show the control performance of the proposed scheme.

show abstract

Section: Remarkmentioning

confidence: 60%

“…If both uncertainties and control force are zero, the nonlinear system is chaotic (cf. [27]). Assumptions 1-3 are easily satisfied for the above system.…”

Section: X(t) = (A + A(t))x(t)+ Bg −1 (X(t))(u(t)+ (X(t)))mentioning

confidence: 99%

See 1 more Smart Citation

Adaptive TS‐FNN control for a class of uncertain multi‐time‐delay systems: The exponentially stable sliding mode‐based approach

Chiang

Chiu

Liu

2008

Adaptive Control & Signal

View full text Add to dashboard Cite

show abstract

“…To the best of our knowledge, such research has resulted in only few findings [3][4][5][6][7][8]. Mukaidanif et al investigated the application of neural networks for guaranteed cost control of the discrete time uncertain system [6].…”

Section: Introductionmentioning

confidence: 99%

“…Mukaidanif et al investigated the application of neural networks for guaranteed cost control of the discrete time uncertain system [6]. Hu and Liu presented robust H ∞ control of multiple time-delay uncertain non-linear system using fuzzy model and adaptive neural network, where known time delay was assumed [7]. Lin et al developed a complete approach to design adaptive neural network-based H ∞ control for non-linear uncertainty systems [8].…”

Section: Introductionmentioning

confidence: 99%

H ∞ Control Design Using Dynamic Neural Networks

Shen

2007

Neural Process Lett

View full text Add to dashboard Cite

In this paper, a new approach is investigated for adaptive dynamic neural network-based H ∞ control, which is designed for a class of non-linear systems with unknown uncertainties. Currently, non-linear systems with unknown uncertainties are commonly used to efficiently and accurately express the real practical control process. Therefore, it is of critical importance but a great challenge and still at its early age to design a stable and robust controller for such a process. In the proposed research, dynamic neural networks were constructed to precisely approximate the non-linear system with unknown uncertainties first, a non-linear state feedback H ∞ control law was designed next, then an adaptive weighting adjustment mechanism for dynamic neural networks was developed to achieve H ∞ regulation performance, and last a recurrent neural network was employed as a neuro-solver to efficiently and numerically solve the standard LMI problem so as to obtain the appropriate control gains. Finally, case studies further verify the feasibility and efficiency of the proposed research.

show abstract

Polynomial Fuzzy Control of an Underwater Robot Based on Gaussian Processes

Zhao

et al. 2022

Proceedings of 2021 5th Chinese Conference on Swarm Intelligence and Cooperative Control

View full text Add to dashboard Cite

Robust H∞ control of multiple time-delay uncertain nonlinear system using fuzzy model and adaptive neural network

Cited by 26 publications

References 25 publications

Adaptive TS‐FNN control for a class of uncertain multi‐time‐delay systems: The exponentially stable sliding mode‐based approach

Adaptive TS‐FNN control for a class of uncertain multi‐time‐delay systems: The exponentially stable sliding mode‐based approach

H ∞ Control Design Using Dynamic Neural Networks

Polynomial Fuzzy Control of an Underwater Robot Based on Gaussian Processes

Contact Info

Product

Resources

About