Adaptive feedback linearization control of chaotic systems via recurrent high-order neural networks

Lu, Zuneng; Shieh, Leang-San; Chen, Guanrong; Coleman, N.

doi:10.1016/j.ins.2005.08.002

Cited by 38 publications

(16 citation statements)

References 25 publications

(21 reference statements)

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“…. , n − 2) are related to the previously obtained bounds of eachx (i) and can be summarized as in (9). In this way, by inspection of the integrals of (12), as well as (16), (18), (19), (20) …”

Section: Theorem 2 Consider the Uncertain Nonlinear System (1) And Amentioning

confidence: 99%

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Feedback Linearization with Fuzzy Compensation for Uncertain Nonlinear Systems

Tanaka

Fernandes

Bessa

2013

INT J COMPUT COMMUN

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Abstract:This paper presents a nonlinear controller for uncertain single-input-single-output (SISO) nonlinear systems. The adopted approach is based on the feedback linearization strategy and enhanced by a fuzzy inference algorithm to cope with modeling inaccuracies and external disturbances that can arise. The boundedness and convergence properties of the tracking error vector are analytically proven. An application of the proposed control scheme to a second-order nonlinear system is also presented. The obtained numerical results demonstrate the improved control system performance.

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Section: Theorem 2 Consider the Uncertain Nonlinear System (1) And Amentioning

confidence: 99%

“…The most common strategies are based on artificial neural networks [2,4,9,10,13] or fuzzy logic [1,3,5,6]. A drawback of these approaches is that both neural networks or fuzzy logic are used to model the entire plant, which means that a large computational effort is normally required to characterize system dynamics.…”

Section: Introductionmentioning

confidence: 99%

Feedback Linearization with Fuzzy Compensation for Uncertain Nonlinear Systems

Tanaka

Fernandes

Bessa

2013

INT J COMPUT COMMUN

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“…Chaos is nonlinear in nature (Lu, Z. et al, 2006), and can help global searching by its diversity characteristic; however, it will cause much more computation time before getting convergent. To perform effectively, chaos algorithm employed in this study was a probability based function; that is, chaos function would be triggered with high probability in the beginning and decreasingly by iteration times.…”

Section: Proposed Approach Kcpsomentioning

confidence: 99%

A Particle Swarm Optimization Based Chaotic K-Means Evolutionary Approach

Cheng¹,

Huang²,

Chen³

2010

Proceedings -- The 27th International Symposium on Automation and Robotics in Construction

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The proposed approach brings up a manner of cognitiveness that inherits a paradigm in particle swarm optimization to implement a chaotic mapping and enhanced by K-means clustering algorithm. In this work, named KCPSO, chaotic mapping with ergodicity, irregularity and the stochastic properties in PSO contributes to global search while K-means with clustering properties in PSO results in rapid convergence.Numerical results indicated that PSO adopting chaotic features can more easily escape from local optima, and meanwhile PSO incorporating with K-means can evidently improve convergence speed. Unpredictability and grouping principle underlying chaotic mapping and K-means methods often imply diversity maintenance and convergence potential within the swarm which inevitably lead to a desirable optimal solution.As proven in the experiments with multidimensional search space and compared with original PSO, the conclusions reported that the proposed KCPSO algorithm could improve the search performance on the benchmark functions significantly, and show the effectiveness of solving optimization problems.

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“…At present, a lot of the chaotic phenomena control methods have been proposed by scholars both at home and abroad, such as delayed feedback [5], adaptive control [6], and intelligent control [7] and so on, some of which have been applied in the field of engineering practice. Many advanced control theories, methods and techniques have been introduced to improve grid security and stability.…”

Section: Introductionmentioning

confidence: 99%

Sliding Mode Control of Grid Chaotic Oscillation based on Immune Algorithm

Li¹,

Liu²,

Yang³

2014

Appl. Math. Inf. Sci.

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Abstract:The bifurcation and chaos phenomenon which exist in power grid are threats to the stable operation of power grid and the safety of people and equipment. In order to eliminate the chaos phenomena in power grid, a kind of non-singular terminal sliding mode (NTSM) variable structure control method based on immune algorithm is put forward. The second-order model of grid chaotic oscillation is built and to be controlled by non-singular terminal sliding mode variable structure controller. Meanwhile, the immune algorithm is introduced to optimize the sliding mode controller parameters. By means of simulation, the effectiveness of this method in the grid chaos control is verified.

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Adaptive feedback linearization control of chaotic systems via recurrent high-order neural networks

Cited by 38 publications

References 25 publications

Feedback Linearization with Fuzzy Compensation for Uncertain Nonlinear Systems

Feedback Linearization with Fuzzy Compensation for Uncertain Nonlinear Systems

A Particle Swarm Optimization Based Chaotic K-Means Evolutionary Approach

Sliding Mode Control of Grid Chaotic Oscillation based on Immune Algorithm

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