Variable universe stable adaptive fuzzy control of a nonlinear system

Li, Hongxing; Man, Zhihong; Lee, E.S.

doi:10.1016/s0898-1221(02)00192-x

Cited by 54 publications

(43 citation statements)

References 4 publications

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“…Linguistic description is the virtue of fuzzy inference, but fuzzy partition sometimes is not suitable, need optimising by some methods on-line. FNN has the linguistic description and learning ability, so we adjust the system universes by FNN in place of function model, to avoid selecting the function models and parameters of models in [1]- [3] and solve the problem of fuzzy partition of contraction-expansion factor fuzzy controller in [4]. The structure of variable universe fuzzy control based on fuzzy neural network is given in Fig.1.…”

Section: Controllermentioning

confidence: 99%

“…In this paper, we use the model of the quadruple inverted pendulum given in [3] to verify the proposed algorithm. Linear model of the quadruple inverted pendulum is given as (16).…”

Section: A the Model Of The Inverted Pendulum Systemmentioning

confidence: 99%

“…But the design of fuzzy controller is complicated work, especially choosing the range of universe and the form of membership function. To solve this question, the variable universe fuzzy controller was presented in [1]- [3]. The socalled variable universe is that the universe contracts or expands with the size of the error on the condition that the number of control rules is fixed.…”

Section: Introductionmentioning

confidence: 99%

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A variable universe fuzzy control algorithm based on fuzzy neural network

Liu

Chen

2008

2008 7th World Congress on Intelligent Control and Automation

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In the conventional variable universe fuzzy control system, it was hard to select the function models of the contraction-expansion factors and parameters of the models. A new variable universe fuzzy control algorithm, to achieve the contraction-expansion of universes by fuzzy neural network in place of the function models, was proposed in this paper. The algorithm, with the semantic characteristics of fuzzy inference and learning ability of neural network, can reasonably adjust universes. Finally, the control algorithm is applied to a quadruple inverted pendulum system. The experiment results show that the proposed algorithm can avoid choosing the function models of contraction-expansion factors and parameters of models, and improve response speed and control precision of the controlled system.

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Section: Controllermentioning

confidence: 99%

“…In this paper, we use the model of the quadruple inverted pendulum given in [3] to verify the proposed algorithm. Linear model of the quadruple inverted pendulum is given as (16).…”

Section: A the Model Of The Inverted Pendulum Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A variable universe fuzzy control algorithm based on fuzzy neural network

Liu

Chen

2008

2008 7th World Congress on Intelligent Control and Automation

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show abstract

“…Due to their approximation property, FL systems can be tuned to estimate the unknown functions in dynamical systems and to reject disturbances. By now, proofs of the stability and performance of FL systems have been provided by a variety of researchers ( [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11] and others). However, the cognitive behaviour of FL controllers has yet to be investigated.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Focusing of Awareness in Fuzzy Control Systems

Kuljača¹,

Lewis²,

Gadewadikar³

et al. 2011

IJAIA

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“…@, AB CD01EFG HIJKL0156, M NO/NO:, P OQRSTa7D/01E DP01EUVW HY5"Z#$@X#?8#$! ?YZ [2] Q 6FG7 +, [3,4][234#$%&56*\ %]^_<`aa %& 7 >H234Q 6, +, [5] %&, +, [6] %&, +, [7] 1 [8] ?A#@;34X]+A#$@, N # BF@,…”

Section: (Introduction)mentioning

confidence: 99%