On Iterative Closed-Loop Identification Using Affine Takagi–Sugeno Models and Controllers

Schrodt, Alexander; Kroll, Andreas

doi:10.1007/s40815-016-0290-x

Cited by 5 publications

(2 citation statements)

References 32 publications

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“…The three-tank system [35] consists of three tanks that are serially connected. The system input is the volumetric flow rate q fed into the first tank by a pump.…”

Section: Three-tank System (3tk)mentioning

confidence: 99%

On Optimal Test Signal Design and Parameter Identification Schemes for Dynamic Takagi-Sugeno Fuzzy Models Using the Fisher Information Matrix

Himmelsbach

Kroll

2021

Int. J. Fuzzy Syst.

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This paper is concerned with the analysis of optimization procedures for optimal experiment design for locally affine Takagi-Sugeno (TS) fuzzy models based on the Fisher Information Matrix (FIM). The FIM is used to estimate the covariance matrix of a parameter estimate. It depends on the model parameters as well as the regression variables. Due to the dependency on the model parameters good initial models are required. Since the FIM is a matrix, a scalar measure of the FIM is optimized. Different measures and optimization goals are investigated in three case studies.

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“…The three-tank system [35] consists of three tanks that are serially connected. The system input is the volumetric flow rate q fed into the first tank by a pump.…”

Section: Three-tank System (3tk)mentioning

confidence: 99%

On Optimal Test Signal Design and Parameter Identification Schemes for Dynamic Takagi-Sugeno Fuzzy Models Using the Fisher Information Matrix

Himmelsbach

Kroll

2021

Int. J. Fuzzy Syst.

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“…The simulation results on the temperature control of a strong nonlinear continuous stirred tank reactor (CSTR) show that the proposed control scheme is feasible and effective. on the control methods based on Takagi-Sugeno (T-S) fuzzy model [9,10]. In T-S fuzzy model, a set of local linear models are weighted by nonlinear membership functions in terms of IF-THEN rules to approximate a large class of nonlinear processes well [11].…”

mentioning

confidence: 99%

Fuzzy Predictive Fault‐Tolerant Control for Time‐Delay Nonlinear Systems with Partial Actuator Failures

Shi

et al. 2019

Complexity

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A fuzzy predictive fault-tolerant control (FPFTC) scheme is proposed for a wide class of discrete-time nonlinear systems with uncertainties, interval time-varying delays, and partial actuator failures as well as unknown disturbances, in which the main opinions focus on the relevant theory of FPFTC based on Takagi-Sugeno (T-S) fuzzy model description of these systems. The T-S fuzzy model represents the discrete-time nonlinear system in the form of the discrete uncertain time-varying delay state space, which is firstly constructed by a set of local linear models and the nonlinear membership functions. The novel improved state space model can be further obtained by extending the output tracking error to the constructed model. Then the fuzzy predictive fault-tolerant control law based on this extended model is designed, which can increase more control degrees of freedom. Utilizing Lyapunov-Krasovskill theory, less conservative delay-range-dependent stable conditions in terms of linear matrix inequality (LMI) constraints are given to ensure the asymptotically robust stability of closed-loop system. Meanwhile, the optimized cost function and H-infinity performance index are introduced to the stable conditions to guarantee the robust performance and antidisturbance capability. The simulation results on the temperature control of a strong nonlinear continuous stirred tank reactor (CSTR) show that the proposed control scheme is feasible and effective. on the control methods based on Takagi-Sugeno (T-S) fuzzy model [9,10]. In T-S fuzzy model, a set of local linear models are weighted by nonlinear membership functions in terms of IF-THEN rules to approximate a large class of nonlinear processes well [11]. In virtue of this, many mature linear theories are able to be applied fully to the stability analysis and control synthesis of nonlinear processes [12][13][14][15][16], which will make great progress in the advanced control theory.Due to the increasing demands for industrial products, the scale of industrial production is growing rapidly, which makes the industrial equipment operated under more complex environments. With the long-running industrial production, the failure may occur. If a failure cannot be coped with instantaneously in such environments through the suitable corrective action, it will make the control performance deteriorate and even expose the equipment and personnel to serious damage. Thus, fault-tolerant systems [17] and advanced process control algorithms [18] were studied to deal with failure using two soft computing approaches, i.e., Hindawi Complexity

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Model Predictive Control Based on a Takagi–Sugeno Fuzzy Model for Nonlinear Systems

Kuo

Resmi

2018

Int. J. Fuzzy Syst.

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On Iterative Closed-Loop Identification Using Affine Takagi–Sugeno Models and Controllers

Cited by 5 publications

References 32 publications

On Optimal Test Signal Design and Parameter Identification Schemes for Dynamic Takagi-Sugeno Fuzzy Models Using the Fisher Information Matrix

On Optimal Test Signal Design and Parameter Identification Schemes for Dynamic Takagi-Sugeno Fuzzy Models Using the Fisher Information Matrix

Fuzzy Predictive Fault‐Tolerant Control for Time‐Delay Nonlinear Systems with Partial Actuator Failures

Model Predictive Control Based on a Takagi–Sugeno Fuzzy Model for Nonlinear Systems

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