Stable and Quadratic Optimal Control for TS Fuzzy-Model-Based Time-Delay Control Systems

Hsu, Ming-Ren; Ho, Wen-Hsien; Chou, Jyh–Horng

doi:10.1109/tsmca.2008.923067

Cited by 25 publications

(24 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, in [42], some new results on stability properties (asymptotical stability and input-to-state stability) have been investigated for T-S fuzzy Hopfield neural networks with constant timedelay. Furthermore, in [33], the delay-dependent stabilizability condition has been integrated with the shifted-Chebyshevseries approach and the hybrid Taguchi-genetic algorithm. An effective control scheme has been proposed to handle the quadratic finite-horizon optimal parallel distributed compensation (PDC) control problem of the T-S fuzzymodel-based time-delay systems.…”

Section: T-s Fuzzy Control and Filtering With Communicationmentioning

confidence: 99%

Analysis, Filtering, and Control for Takagi-Sugeno Fuzzy Models in Networked Systems

Zhang

Wang

et al. 2015

Abstract and Applied Analysis

View full text Add to dashboard Cite

The fuzzy logic theory has been proven to be effective in dealing with various nonlinear systems and has a great success in industry applications. Among different kinds of models for fuzzy systems, the so-called Takagi-Sugeno (T-S) fuzzy model has been quite popular due to its convenient and simple dynamic structure as well as its capability of approximating any smooth nonlinear function to any specified accuracy within any compact set. In terms of such a model, the performance analysis and the design of controllers and filters play important roles in the research of fuzzy systems. In this paper, we aim to survey some recent advances on the T-S fuzzy control and filtering problems with various network-induced phenomena. The network-induced phenomena under consideration mainly include communication delays, packet dropouts, signal quantization, and randomly occurring uncertainties (ROUs). With such network-induced phenomena, the developments on T-S fuzzy control and filtering issues are reviewed in detail. In addition, some latest results on this topic are highlighted. In the end, conclusions are drawn and some possible future research directions are pointed out.

show abstract

Section: T-s Fuzzy Control and Filtering With Communicationmentioning

confidence: 99%

Analysis, Filtering, and Control for Takagi-Sugeno Fuzzy Models in Networked Systems

Zhang

Wang

et al. 2015

Abstract and Applied Analysis

View full text Add to dashboard Cite

show abstract

“…which stabilizes system (1) within finite time N and minimizes the performance index function (2). Then, obviously,…”

Section: Properties Of the -Optimal Control Algorithmmentioning

confidence: 99%

“…In many theoretical discussions, controllers are generally designed to make the controlled systems stabilized or tracked within infinite time horizon [1][2][3][4][5][6][7][8][9]. That is, the system cannot really be stabilized or tracked until the time reaches infinity.…”

Section: Introductionmentioning

confidence: 99%

Finite horizon optimal control of discrete-time nonlinear systems with unfixed initial state using adaptive dynamic programming

Wei

Liu

2011

J. Control Theory Appl.

View full text Add to dashboard Cite

In this paper, we aim to solve the finite horizon optimal control problem for a class of discrete-time nonlinear systems with unfixed initial state using adaptive dynamic programming (ADP) approach. A new -optimal control algorithm based on the iterative ADP approach is proposed which makes the performance index function converge iteratively to the greatest lower bound of all performance indices within an error according to within finite time. The optimal number of control steps can also be obtained by the proposed -optimal control algorithm for the situation where the initial state of the system is unfixed. Neural networks are used to approximate the performance index function and compute the optimal control policy, respectively, for facilitating the implementation of the -optimal control algorithm. Finally, a simulation example is given to show the results of the proposed method.

show abstract

“…Factors (or individuals) are called related when the desirable experimental region of some factors (or individuals) depends on the level settings of other factors (or individuals). Two major tools used in the TSLM are: (I) signal-to-noise ratio (SNR) which measures quality, and (II) orthogonal arrays which are used to study many design parameters simultaneously [34][35][36]. In the TSBDEA, the TSLM is to provide a new systematic crossover operation to replace the original crossover operation of DEA.…”

Section: Introductionmentioning

confidence: 99%

Optimal approximation of linear systems using Taguchi-sliding-based differential evolution algorithm

Tsai

Chou

et al. 2011

Applied Soft Computing

Self Cite

View full text Add to dashboard Cite

Stable and Quadratic Optimal Control for TS Fuzzy-Model-Based Time-Delay Control Systems

Cited by 25 publications

References 15 publications

Analysis, Filtering, and Control for Takagi-Sugeno Fuzzy Models in Networked Systems

Analysis, Filtering, and Control for Takagi-Sugeno Fuzzy Models in Networked Systems

Finite horizon optimal control of discrete-time nonlinear systems with unfixed initial state using adaptive dynamic programming

Optimal approximation of linear systems using Taguchi-sliding-based differential evolution algorithm

Contact Info

Product

Resources

About