Discretisation of continuous-time T–S fuzzy system: global approach

Kim, Do Wan; Park, J.B.; Lee, H.J.; Joo, Young Hoon

doi:10.1049/ip-cta:20045267

Cited by 8 publications

(7 citation statements)

References 11 publications

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“…In (10), if the sample time is small enough, one can despise the high-order terms of the Taylor series, and, consequently, = I. An intuitive idea to discretize the continuous T-S fuzzy model is to directly apply the discretization method for linear time-invariant systems at the consequent parts of the fuzzy rules [11].…”

Section: Discretization Problem Descriptionmentioning

confidence: 99%

“…However, instead of implementing analogic controllers, implementation using a digital dispositive is preferable as it features greater flexibility and lower cost. In nonlinear controller's design, considering the plant described by T-S fuzzy models, several results have been published, as in [11]. The discretization of nonlinear systems is not always easy.…”

Section: Introductionmentioning

confidence: 99%

“…A technique to discretize linear systems with emulation is suggested in [12]. This technique can also be used to discretize T-S fuzzy nonlinear systems in a way less complicated than the method presented in [11]. In Theorem 1, the validity of the discretization methodology with T-S fuzzy models to design discrete-time controllers for the rehabilitation of paraplegic patients using electrical stimulation is shown in a way more simplified than in [15].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Discrete Takagi-Sugeno Fuzzy Models Applied to Control the Knee Joint Movement of Paraplegic Patients

et al. 2020

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In this manuscript, a method for designing Takagi-Sugeno (T-S) fuzzy discrete-time regulators based on linear matrix inequalities (LMIs) is proposed to control the variation of the knee joint angle movement of paraplegic patients through electrical stimulation. A simple method for discretizing nonlinear systems described by T-S fuzzy models is used. The control strategy is applied for a paraplegic volunteer and a healthy one. The results and analysis show that the controlled system attended the design specifications for small values of the sample time considered for the discretization.

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Section: Discretization Problem Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Discrete Takagi-Sugeno Fuzzy Models Applied to Control the Knee Joint Movement of Paraplegic Patients

et al. 2020

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“…It makes traditional analysis tools for a homogeneous signal system unable to be directly used. It is found in [1,2,3,4,5,6,7] that the approximate discrete-time model of (3) takes the following form:…”

Section: Preliminariesmentioning

confidence: 99%

Stabilization of Multirate Sampled-Data Fuzzy Systems Based on an Approximate Discrete-Time Model

Kim

Park

Joo

2006

Fuzzy Systems and Knowledge Discovery

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This paper studies a stabilization problem for a multirate digital control of fuzzy systems based on the approximately discretized model. In the multirate control scheme, a numerical integration scheme is used to approximately predict the current state from the state measured at the sampling points. It is shown that the multirate digital fuzzy controller stabilizing an approximate discrete-time fuzzy model would also stabilize the sampled-data fuzzy system in the sufficiently small control update time. Furthermore, some sufficient conditions for the stabilization of the approximate discrete-time fuzzy model are provided under the delta-operator frame work, which are expressed as the linear matrix inequalities (LMIs) and thereby easily tractable by the convex optimization techniques. A numerical example is demonstrated to visualize the feasibility of the developed methodology.

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“…In practice, many engineering systems, such as network control systems [11,12], robot systems [13,14], and electromechanical systems [15], can be described by the switched system models. The T-S fuzzy model provided new method for the nonlinear system problems [16][17][18][19][20][21]. The fuzzy rules describing the input-output relationship of each subsystem of nonlinear system are important components of the T-S fuzzy model.…”

Section: Introductionmentioning

confidence: 99%

Memory State Feedback Control for Time-Varying Delay Switched Fuzzy Systems

Zhang

Jia

et al. 2019

Advances in Fuzzy Systems

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Consider the problem of memoryless state feedback controller for time-delay system, which cannot consider both the memoryless and the memory items in the system. Therefore, the memoryless state feedback controller has certain limitations and is more conservative. This paper addresses the memory state feedback control for the time-varying delay switched fuzzy systems based on T-S fuzzy model to overcome the problem discussed above. The state vector and input of the time-varying delay systems contain unknown time-varying delay with known bounds. The designed controller whose parameters are solvable can introduce past state information and reduce the system conservativeness. The more general Lyapunov-Krasovskii functional is selected and the switching law is designed in order to analyze the open-loop system stability, and the memory state feedback controller is designed for the closed-loop system and the criterion for its asymptotic stability. Discuss the solvability of the above two criteria. Finally, a numerical example is given. The simulation results show that the proposed method is more feasible and effective.

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Discretisation of continuous-time T–S fuzzy system: global approach

Cited by 8 publications

References 11 publications

Discrete Takagi-Sugeno Fuzzy Models Applied to Control the Knee Joint Movement of Paraplegic Patients

Discrete Takagi-Sugeno Fuzzy Models Applied to Control the Knee Joint Movement of Paraplegic Patients

Stabilization of Multirate Sampled-Data Fuzzy Systems Based on an Approximate Discrete-Time Model

Memory State Feedback Control for Time-Varying Delay Switched Fuzzy Systems

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