A Fuzzy Approach for Robust Reference-Tracking-Control Design of Nonlinear Distributed Parameter Time-Delayed Systems and Its Application

Chang, Young Chul; Chen, Bor‐Sen

doi:10.1109/tfuzz.2010.2058809

Cited by 90 publications

(49 citation statements)

References 55 publications

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“…2 Remark 3. (i) Based on Theorem 1, the MOP of multiobjective H 2 /H ∞ tracking control design problem (9) can be replaced by the MOP (10) with the inequality constraints (11)- (12), which are much easier to solve. Therefore, we focus on the MOP (10) in the sequel.…”

Section: Multiobjective H 2 /H ∞ Tracking Control Designmentioning

confidence: 99%

“…Fuzzy control has been employed successfully to solve many nonlinear control problems [3,4]. Recently, many robust fuzzy tracking control schemes have been developed for nonlinear tracking control design problem [5][6][7][8][9][10][11][12][13][14]. In general, tracking control design problems are more difficult than fuzzy stabilization control design problem because they need to track a prescribed reference signal.…”

Section: Introductionmentioning

confidence: 99%

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Multiobjective tracking control design of T–S fuzzy systems: Fuzzy Pareto optimal approach

Chen

2016

Fuzzy Sets and Systems

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Section: Multiobjective H 2 /H ∞ Tracking Control Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multiobjective tracking control design of T–S fuzzy systems: Fuzzy Pareto optimal approach

Chen

2016

Fuzzy Sets and Systems

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“…For a more precise approximation, the influence of the fuzzy approximation errors in (18) can be incorporated in the design of the robust reference tracking control, or it can be merged with the external disturbance d(x, t), whose effect could be attenuated by the proposed robust reference tracking strategy. The details of how to deal with the effect of fuzzy approximation errors can be found in our previous studies (Tseng et al, 2001;Chang and Chen, 2010). In this study, we estimated the fuzzy approximation errors based on the following assumption:…”

Section: Robust Fuzzy Tracking Control Design Approachmentioning

confidence: 99%

“…A fuzzy infinite-dimensional state space model based on the Galerkin method has been proposed to represent a nonlinear PDEs system with some truncation error (Chen and Chang, 2009). An alternative method, the finite difference scheme (Strikwerda, 2004), has also been widely applied to obtain the numerical solutions of PDEs, and a fuzzy tracking control for nonlinear PDEs systems with environmental disturbances has been proposed based on this scheme (Chang and Chen, 2010). However, many control methods remain theoretical, as it is very difficult and probably unrealistic to design a controller for the entire PDEs system.…”

mentioning

confidence: 99%

Natural Resource Management for Nonlinear Stochastic Biotic-Abiotic Ecosystems: Robust Reference Tracking Control Strategy Using Limited Set of Controllers

Lin¹,

Chen²

2015

J ENV INFORM

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ABSTRACT. The management of natural resources has become an important subject because of the increasing population. To meet human requirements for resources such as food and water, many control strategies have been proposed to improve biomass production and nutrient supply. However, implementing these strategies may be limited by the expense of large numbers of control devices and by the effects of stochastic perturbations. In this study, a robust reference tracking control strategy for the natural resource management of nonlinear stochastic biotic-abiotic ecosystems is proposed, using a limited set of controllers, to regulate the systematic dynamics achieving a desired reference trajectory under the influence of intrinsic fluctuations and environmental disturbances. To simplify the design procedure and make the robust reference tracking control strategy more feasible, we propose a fuzzy stochastic partial differrential equations system to represent the ecosystem, itself approximated by a fuzzy stochastic spatial state space model, based on a finite difference scheme. This allows replacement of a complex Hamilton-Jacobi integral inequality by an equivalent set of local linear matrix inequalities which can be easily solved. We verify the efficiency of the proposed approach using a nonlinear stochastic biomass-nutrient control example and compare the robust tracking control performance for different arrangements of control devices. Managers may select appropriate tracking control schemes based on this comparison. The robust reference tracking control strategy can be applied not only to agricultural systems but also to biophysical systems in ecological conservation, ecosystem restoration or engineering.

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“…The problems dealing with the effect of fuzzy approximation error can be acquisited in our previous researches [31][32][33].…”

Section: Remarkmentioning

confidence: 99%

On the Information Transmission Ability of Nonlinear Stochastic Dynamic Networks

Chen

Lin

2012

Entropy

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Abstract:The major function of dynamic networks is to sense information from the environment and process the information to the downstream. Therefore how to measure the information transmission ability of a dynamic network is an important topic to evaluate network performance. However, the dynamic behavior of a dynamic network is complex and, despite knowledge of network components, interactions and noises, it is a challenge to measure the information transmission ability of a dynamic network, especially a nonlinear stochastic dynamic network. Based on nonlinear stochastic dynamic system theory, the information transmission ability can be investigated by solving a Hamilton-Jacobi inequality (HJI)-constrained optimization problem. To avoid difficulties associated with solving a complex HJI-constrained optimization problem for information transmission ability, the Takagi-Sugeno (T-S) fuzzy model is introduced to approximate the nonlinear stochastic dynamic network by interpolating several local linear stochastic dynamic networks so that a HJI-constrained optimization problem can be replaced by the linear matrix inequalities (LMIs)-constrained optimization problem. The LMI problem can then be efficiently solved for measuring information transmission ability. We found that a more stable (robust) dynamic network has less information transmission ability, and vice versa. Finally, an example of a biochemical network in cellular communication is given to illustrate the measurement of information transmission ability and to confirm the results by using Monte Carlo simulations.

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A Fuzzy Approach for Robust Reference-Tracking-Control Design of Nonlinear Distributed Parameter Time-Delayed Systems and Its Application

Cited by 90 publications

References 55 publications

Multiobjective tracking control design of T–S fuzzy systems: Fuzzy Pareto optimal approach

Multiobjective tracking control design of T–S fuzzy systems: Fuzzy Pareto optimal approach

Natural Resource Management for Nonlinear Stochastic Biotic-Abiotic Ecosystems: Robust Reference Tracking Control Strategy Using Limited Set of Controllers

On the Information Transmission Ability of Nonlinear Stochastic Dynamic Networks

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