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

Kuo, Yong-Lin; Resmi, Ilmiyah Elrosa Citra

doi:10.1007/s40815-018-0574-4

Cited by 16 publications

(6 citation statements)

References 49 publications

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“…where y i denotes the COR and x denotes the RPCRF. In order to identify the intrinsic parameters p, the experimental data are fitted according to the model of Eq (11). The objective function used in the optimal fitting process is the minimization of squared between the fitting and experimental observation curves, which is given in Eq (8).…”

Section: Parameter Identification Processmentioning

confidence: 99%

“…Hence the optimization algorithm based on the least squares principle is more appropriate to minimize such function. The application of the AFLM for solving the assessment of COR is presented in this section, and the parameter identification is presented step by step in Fig 4. The data in Table 1 indicate intrinsic parameters p. There are seven rules in this case, and 14 parameters need to be optimized in the consequent as indicated in Eq (11). It is clear from Fig 4 that the 14 parameters are optimized at the same time.…”

Section: Parameter Identification Processmentioning

confidence: 99%

“…In this study, the Takagi-Sugeno (T-S) fuzzy system was chosen, because it can solve complex and high-dimensional problems relying only on a few rules. T-S fuzzy systems proposed by Takagi and Sugeno in 1985 are an effective tool for approximation of uncertain nonlinear systems based on fuzzy if-then implication rules, and each rule refers to a local linear system, so the T-S fuzzy model approximates the original nonlinear system [ 10 , 11 ]. T-S model is very powerful tool which is implemented in many prediction or assessment fields, like transport [ 12 ], energy [ 13 ], chemical industry [ 14 , 15 ], aerospace [ 16 ] etc.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Rule-based expert system to assess caving output ratio in top coal caving

et al. 2020

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Coal mining professionals in coal mining have recognized that the assessment of top coal release rate can not only improve the recovery rate of top coal, but also improve the quality of coal. But the process was often performed using a manual-based operation mode, which intensifies workload and difficulty, and is at risk of human errors. The study designs a assessment system to give the caving output ratio in top coal caving as accurately as possible based on the parameters adaptive Takagi-Sugeno (T-S) fuzzy system and the Levenberg-Marquardt (LM) algorithm. The main goal of the adaptive parameters based on LM algorithm is to construct its damping factor in the light of lowering of the objective function which is as taken as the index of termination iteration. The performance of the system is evaluated by Pearson correlation coefficient, Coefficient of Determination and relative error where the results of the Takagi-Sugeno method and the parameters adaptive Takagi-Sugeno method are compared to make the evaluation more robust and comprehensive.

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Section: Parameter Identification Processmentioning

confidence: 99%

Section: Parameter Identification Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Rule-based expert system to assess caving output ratio in top coal caving

et al. 2020

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“…Furthermore, the MPC method predicts the performances of the plant under a series of presumed control inputs in the control horizon. However, due to the online solution implementation of a receding horizon optimization problem, computational efficiency becomes critical in practical applications [18,19]. On the other hand, FLC methods incorporate the desired system's behaviour knowledge, as linguistic rules into the control structure, where the system's model is not accessible or is too complex.…”

Section: Introductionmentioning

confidence: 99%

Adaptive dynamic programming discrete‐time LQR control on electromagnetic levitation system

Abdollahzadeh

2023

IET Control Theory & Appl

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In this paper, an adaptive dynamic programming discrete‐time linear quadratic Riccati (ADP‐DTLQR) control is designed for an electromagnetic levitation system (EMS) to track the reference trajectories and preserve stability in severe situations efficiently. The EMS system has a strongly nonlinear and unstable dynamic in nature. The ADP‐DTLQR approach is a novel method based on the Q‐learning algorithm for unknown discrete‐time systems in a causal manner. The ADP‐DTLQR can determine the online gains of the controller in different conditions as a model‐free structure, which can minimize the combination of state errors and control efforts of the systems. Moreover, the DTLQR controller and linear matrix inequality controller on the EMS system are designed and compared with the present strategy in different scenarios in the simulation section to verify the robustness and strongness of the proposed method. The evaluation of simulation results demonstrates that the proposed control scheme is suitable not only for preserving the levitated object stability but also for compensating the disturbances and uncertainties in the EMS structure.

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“…In addition, the T-S fuzzy model as a typical model of complex dynamic systems simplifies the complex nonlinear system by establishing “IF THEN” rules, so that complex nonlinear systems can be analyzed more easily (see Bessa et al, 2020; Ghorbel and Braiek, 2022; Guo et al, 2018; Kuo and Citra Resmi, 2019). Liu and Zhang (2003) proposed a new H ∞ controller design method using linear matrix inequalities for fuzzy systems.…”

Section: Introductionmentioning

confidence: 99%

Networked fault detection for fuzzy systems using a quantizer with event-triggered strategy

Chen,

Cui,

Sun

et al. 2023

Transactions of the Institute of Measurement and Control

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This paper concerns with the problem of fault detection for networked fuzzy systems. Considering the digital communication channel, quantization is adopted to make the data transmission feasible and the induced data distortion is converted into a bounded uncertain term. For the purpose of saving network resources, a new quantizer with an event-triggered strategy is designed to reduce the length and the number of the transmitted data packets, simultaneously. In the process of stability analysis, the T-S fuzzy model approach is employed to approximate the nonlinear networked system, and a more flexible fuzzy design method is presented to get the fault detection filter which can detect the sensor fault in the presence of external disturbance. And sufficient conditions are constructed to guarantee the stability of the filter error system in terms of linear matrix inequalities. Finally, the simulation example is given to verify the feasibility of the method.

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

Cited by 16 publications

References 49 publications

Rule-based expert system to assess caving output ratio in top coal caving

Rule-based expert system to assess caving output ratio in top coal caving

Adaptive dynamic programming discrete‐time LQR control on electromagnetic levitation system

Networked fault detection for fuzzy systems using a quantizer with event-triggered strategy

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