Stable auto-tuning of hybrid adaptive fuzzy/neural controllers for nonlinear systems

Nounou, Hazem; Passino, K.M.

doi:10.1016/j.engappai.2004.09.005

Cited by 10 publications

(12 citation statements)

References 10 publications

(13 reference statements)

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“…Consider the system (1) with Assumptions 1-3 driven by the control law (14) with (15) and (20). If x.0/ 2 c x0 and O Â.0/ 2 c a , then there exist suitably large control parameters k s in (14), 1= in (15), and in (20) so that the closed-loop system has a semiglobal practical asymptotic stability in the sense that (i) the unique solution of the closed-loop system is defined 8t 2 OE0; 1/; (ii) the integrated squared errors of e and " are bounded by (27) and (28), respectively; and (iii) e converges to a small neighborhood of zero dominated by , , and k s .…”

Section: Theoremmentioning

confidence: 99%

“…with z WD col e; Q Â 2 R nCN for the closed-loop system composed of (16) and (20). Differentiating V in (23) along (16) and (20)…”

Section: Proofmentioning

confidence: 99%

“…According to the projection modification results in [42], if O Â.0/ 2 c a , then the adaptive law (20) guarantees…”

Section: Proofmentioning

confidence: 99%

“…As both tracking errors and prediction errors are applied to update parametric estimates, CAC shows an attractive property of obvious performance improvement in many applications, including robotic arms [2,[6][7][8][9][10][11][12][13][14][15], motor drivers [16][17][18], aircrafts [19][20][21][22][23][24], underwater vehicles [25], chemical processes [26,27], and chaotic systems [28]. Yet, only matched uncertainties are considered in these results.…”

Section: Introductionmentioning

confidence: 99%

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Composite adaptive dynamic surface control using online recorded data

Pan

Sun

2016

Intl J Robust & Nonlinear

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SUMMARYThis paper presents an online recorded data-based design of composite adaptive dynamic surface control for a class of uncertain parameter strict-feedback nonlinear systems, where both tracking errors and prediction errors are applied to update parametric estimates. Differing from the traditional composite adaptation that utilizes identification models and linear filters to generate filtered modeling errors as prediction errors, the proposed composite adaptation integrates closed-loop tracking error equations in a moving time window to generate modified modeling errors as prediction errors. The time-interval integral operation takes full advantage of online recorded data to improve parameter convergence such that the application of both identification models and linear filters is not necessary. Semiglobal practical asymptotic stability of the closed-loop system is rigorously established by the time-scales separation and Lyapunov synthesis. The major contribution of this study is that composite adaptation based on online recorded data is achieved at the presence of mismatched uncertainties. Simulation results have been provided to verify the effectiveness and superiority of this approach.

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Section: Theoremmentioning

confidence: 99%

“…with z WD col e; Q Â 2 R nCN for the closed-loop system composed of (16) and (20). Differentiating V in (23) along (16) and (20)…”

Section: Proofmentioning

confidence: 99%

“…According to the projection modification results in [42], if O Â.0/ 2 c a , then the adaptive law (20) guarantees…”

Section: Proofmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Composite adaptive dynamic surface control using online recorded data

Pan

Sun

2016

Intl J Robust & Nonlinear

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“…An MLP approach for designing of NNMPC controller is presented in [9].The fuzzy systems can also be used as approximators to approximate the controller in the direct case. One good candidate of fuzzy systems is the TakagiSugeno fuzzy system (TSFS), which has shown to be successful in many applications [10].…”

Section: Introductionmentioning

confidence: 99%

Design of Model Predictive Control based Direct Neural Controller for Surge Tank Application

Baweja¹,

Bhagat²

2012

IJCA

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Model predictive control based direct neural controllers represent another class of computer application in the field of non-linear controls. These controllers can also be made adaptive such that the adaptation mechanism attempts to adjust a parameterized nonlinear controller to approximate an ideal controller. Various approximators such as linear mappings, polynomials, fuzzy systems, or neural networks can be used as parameterized nonlinear controller. In this paper, we proposed a model predictive control based neural network controller to control the liquid level in a surge tank, with respect to the reference input. The neural controller works on the normalized gradient-based approximator parameter update law used for a class of nonlinear discrete-time systems in direct cases. In our proposed design, the reduction in error is reached upon between the ideal and the actual controller and the direct adaptive control scheme is tested for performance via a simple surge tank example. The proposed controller algorithm performs well and can be physically implemented. KeywordsModel predictive control, Direct neural control, Non linear systems.

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Fuzzy Logic Control Systems

Kamalasadan

Al-Olimat

2009

Wiley Encyclopedia of Computer Science and Engineering

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This article explains fuzzy controllers and general fuzzy systems. It emphasizes the basic idea of fuzzy control and the mathematics behind the concept. The article is divided into two sections. In the first part, the mathematics of fuzzy systems is discussed, which includes the definitions and basics of fuzzy expert systems, general fuzzy systems, and their properties. In the second part, the concept of fuzzy control are detailed which include the selection of inputs and outputs, enhancing control knowledge as a rule base, determining which rules to use, determining conclusions, converting decisions into actions, and graphical visual representation of fuzzy control operation. Then, various fuzzy control techniques and the essence of how fuzzy systems can be used for a wide range of control architectures are explained. The article concludes with the summary and future prospects of fuzzy logic systems.

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Stable auto-tuning of hybrid adaptive fuzzy/neural controllers for nonlinear systems

Cited by 10 publications

References 10 publications

Composite adaptive dynamic surface control using online recorded data

Composite adaptive dynamic surface control using online recorded data

Design of Model Predictive Control based Direct Neural Controller for Surge Tank Application

Fuzzy Logic Control Systems

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