Efficient Model-Predictive Control for Nonlinear Systems in Interval Type-2 T-S Fuzzy Form Under Round-Robin Protocol

Dong, Yuying; Song, Yan; Wei, Guoliang

doi:10.1109/tfuzz.2020.3031394

Cited by 30 publications

(6 citation statements)

References 43 publications

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“…The neural fuzzy models have been widely used in the control system. Examples include the MPC with finite horizon based on the fuzzy discrete systems [7], the output feedback predictive control based on the Takagi-Sugeno (T-S) fuzzy model [8] [9], the NMPC based on self-feedback fuzzy network [10], and the adaptive T-S fuzzy model-based predictive controller [11]. The neural fuzzy network is also used in the intelligent control [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Structurally Optimized Neural Fuzzy Modeling for Model Predictive Control

Gong

Zhao

et al. 2023

IEEE Trans. Ind. Inf.

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This paper investigates the local linear model tree (LOLIMOT), a typical neural fuzzy model, in the multipleinput-multiple-output model predictive control (MPC). In the conventional LOLIMOT, the structural parameters including centres and variances of its Gaussian kernels are set based on equally dividing the input data space. In this paper, after the structural parameters are initially obtained from the input space partition, they are optimized by the gradient descent search, from which the space partitions are further adjusted. This makes it better for the model structure to fit the input data statistics, leading to improved modelling performance with small model size. The MPC based on the proposed structurally optimized LOLIMOT is then implemented and verified with both numerical and diesel engine plants. Validation results show that the proposed MPC has significantly better controlling performance than the MPC based on the conventional LOLIMOT, making it an attractive solution in practice.

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Section: Introductionmentioning

confidence: 99%

Structurally Optimized Neural Fuzzy Modeling for Model Predictive Control

Gong

Zhao

et al. 2023

IEEE Trans. Ind. Inf.

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“…However, a key obstacle of the networked communication is the limited networked bandwidth, which may induce data collision and network congestion, and also can make significant degradation for system performance and even result in system instability. 10,11 In view of this, it is of vital importance to make rational and efficient use of limited network resources. As such, various communication protocols have been used to manage information transmission.…”

Section: Introductionmentioning

confidence: 99%

Outlier‐resistant quantized control for T‐S fuzzy systems under multi‐channel‐enabled round‐robin protocol and deception attacks

Zha

Huang

Liu

et al. 2023

Intl J Robust & Nonlinear

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In this study, the security quantized control problem is investigated for discrete‐time Takagi‐Sugeno (T‐S) fuzzy systems with deception attacks based on the multi‐channel‐enabled round‐robin (MCERR) protocol. The MCERR protocol breaks through the limitation in the well‐known RR protocol where only one signal can be transmitted at every turn. Under MCERR protocol, multiple transmission channels are put to use and a set of sensor nodes can gain access to network. Considering the random deception attacks and the measurement outliers, a new system model is established and a sufficient condition is derived to ensure the stability of the discussed system. Furthermore, an outlier resistant observer‐based controller which can mitigate the impact of deception attacks is developed. Besides, the concrete expressions of observer gains and controller gains are obtained. Finally, the effectiveness of the proposed method can be validated by simulation results.

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“…Afterward, a novel dual‐mode MPC strategy has been proposed 14 to guarantee a fairly large feasibility region with adding less additional on‐line computational load. Up to date, efficient MPC has gained a special concern in the control community and some promising research results on this point have been reported in the literature 15‐17 . Nonetheless, when it comes to the complicated stochastic system, most published results on MPC including the aforementioned ones will be invalid, not even mention the efficient MPC strategies, due primarily to the analytical uncertain system dynamics induced by randomness.…”

Section: Introductionmentioning

confidence: 99%

Efficient model predictive control for Markovian jump systems with Lur'e nonlinear term: A dual‐mode control scheme

Zhang

Song

2021

Intl J Robust & Nonlinear

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In this article, the efficient model predictive control (MPC) problem is investigated for a class of Markovian jump Lur'e systems (MJLSs) subject to parameter uncertainties and input constraints, where each subsystem is described by the combination of a linear part and a nonlinear Lur'e term. The main purpose of the addressed problem is to design a set of dual‐mode feedback controllers in the framework of efficient MPC to make a nice tradeoff among the online computation burden, the initial feasible region, and the control performance. To this aim, the following two tasks need to be fulfilled: 1) for the terminal constraint set, the corresponding fixed feedback gain that is composed of a linear part and a nonlinear part is designed with aid of the Lur'e‐type Lyapunov‐like function related to the modes of subsystems; and 2) a fairly large initial feasible region is obtained off‐line by adjusting the dimension of the control perturbation sequence. Then, an online optimization problem is put forward to design this perturbation sequence with the determined dimension to steer the system state into the terminal constraint set within the pre‐determined steps. Sufficient conditions are presented to guarantee the recursive feasibility of the proposed efficient MPC algorithm and the mean‐square stability of the underlying MJLSs. Finally, a simulation example with regards to the DC motor device system is provided to demonstrate the effectiveness of our proposed MPC strategy.

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Efficient Model-Predictive Control for Nonlinear Systems in Interval Type-2 T-S Fuzzy Form Under Round-Robin Protocol

Cited by 30 publications

References 43 publications

Structurally Optimized Neural Fuzzy Modeling for Model Predictive Control

Structurally Optimized Neural Fuzzy Modeling for Model Predictive Control

Outlier‐resistant quantized control for T‐S fuzzy systems under multi‐channel‐enabled round‐robin protocol and deception attacks

Efficient model predictive control for Markovian jump systems with Lur'e nonlinear term: A dual‐mode control scheme

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