Design and input-to-state practically stable analysis of the mixed
 H
 2
 /
 H
 ∞
 feedback robust model predictive control

Huang, Hua; Li, D.; Xi, Yugeng

doi:10.1049/iet-cta.2011.0187

Cited by 23 publications

(22 citation statements)

References 13 publications

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“…Now using computer MATLAB LMI-toolbox to solve LMI (66) gives the feasibility data as follows: Remark 16. This paper is inspired by the methods and conclusions of the previous literature [55][56][57][58][59]. But the sufficient conditions of Theorem 4.3 is easier to be verified than those of existing results.…”

Section: Numerical Examplesmentioning

confidence: 99%

Robust Stability of Nonlinear Diffusion Fuzzy Neural Networks with Parameter Uncertainties and Time Delays

et al. 2018

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In this paper, a class of nonlinear p-Laplace diffusion BAM Cohen-Grossberg neural networks (BAM CGNNs) with time delays is investigated. In the case of p > 1 with p ≠ 2, the authors construct novel Lyapunov functional to overcome the mathematical difficulties of nonlinear p-Laplace diffusion time-delay model with parameter uncertainties, deriving the LMI-based robust stability criterion applicable to computer MATLAB LMI toolbox and deleting the boundedness of the amplification functions. And in the case of p = 2, LMI-based sufficient conditions are also inferred for robust input-to-state stability of reaction-diffusion Markovian jumping BAM CGNNs with the event-triggered control, which is different from those of many previous related literature. In particular, the role of diffusion can be reflected in newly acquired criteria. Finally, numerical examples verify the effectiveness of the proposed methods.

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Section: Numerical Examplesmentioning

confidence: 99%

Robust Stability of Nonlinear Diffusion Fuzzy Neural Networks with Parameter Uncertainties and Time Delays

et al. 2018

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“…Model predictive control is one of the mature and commonplace control approaches which is applied in many practical and academic systems [28][29][30][31]. M PC strictly depends on the model of system which is under the control [15,23].…”

Section: Model Predictive Controlmentioning

confidence: 99%

Fuzzy Predictive Control of Step-Down DC-DC Converter Based on Hybrid System Approach

Sarailoo¹,

Rafiee²,

Rezaie³

2014

IJISA

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Abstract-In this paper, a fu zzy predict ive control scheme is proposed for controlling output voltage of a step-down DC-DC converter in presence of disturbance and uncertainty. The DC-DC converter is considered as a hybrid system and modeled by Mixed Logical Dynamical modeling approach. The main objective of the paper is to design a Fuzzy Pred ictive Control to achieve desired voltage output without increasing complexity of the hybrid model of DC-DC converter in various conditions. A model predictive control is designed based on the hybrid model and applied to the system. Although the performance of the model predictive control method is satisfactory in normal condition, it suffers from lack of robustness in presence of disturbance and uncertainty. So, to succeed in facing up to the problem a fu zzy supervisor is utilized to adjust the main predict ive controller based on the measured states of the system. In this paper it is shown that the proposed fuzzy pred ictive control scheme has advantages such as simplicity and efficiency in normal operation and robustness in presence of disturbance and uncertainty. Through simulat ions effectiveness of the proposed method is shown. Index Terms-Step-Down DC-DC Converter, Hybrid Model, Mixed Logical Dynamical Model, Model Predictive Control, Fuzzy Supervisor

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“…MPC and fuzzy predictive control for three-tank system MPC is one of the mature and commonplace control approaches which is applied to many practical systems and strictly depends on the model of controlled system. [23][24][25][26][27][28] In the previous section, an approximate model of three-tank system has been extracted. The hybrid model predictive control (HMPC) is very similar to the conventional MPC, except for its cost function in which the logical and continuous auxiliary variables are considered.…”

Section: Consequently the Matrixesmentioning

confidence: 99%

Fuzzy predictive control of three-tank system based on a modeling framework of hybrid systems

Sarailoo

Rezaie

Rafiee

2014

Proceedings of the Institution of Mechanical Engineers, Part I:

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In this article, a fuzzy predictive control scheme is proposed for controlling liquid level in a three-tank system in the presence of disturbances and uncertainties. The three-tank system is considered as a hybrid system, and a hybrid model of the system is obtained using the mixed logical dynamical modeling approach. Nonlinear parts of the system are linearized based on a piecewise affine linear method. Then, a model predictive control is designed based on the hybrid model and applied to the three-tank system. Although the performance of the model predictive control method is satisfactory in normal condition, it suffers from the problem of bias in output in the presence of disturbance and uncertainty. In this article, a fuzzy supervisor is utilized to adjust the main predictive controller such that the effects of disturbance and uncertainties are degraded by using zero-offset tracking. The proposed fuzzy predictive control scheme has advantages of simplicity and efficiency in normal operation, and strong robustness in the presence of disturbances and uncertainties. Simulation results demonstrate the effectiveness and superiority of the method.

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Design and input-to-state practically stable analysis of the mixed H ₂ / H _∞ feedback robust model predictive control

Cited by 23 publications

References 13 publications

Robust Stability of Nonlinear Diffusion Fuzzy Neural Networks with Parameter Uncertainties and Time Delays

Robust Stability of Nonlinear Diffusion Fuzzy Neural Networks with Parameter Uncertainties and Time Delays

Fuzzy Predictive Control of Step-Down DC-DC Converter Based on Hybrid System Approach

Fuzzy predictive control of three-tank system based on a modeling framework of hybrid systems

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Design and input-to-state practically stable analysis of the mixed H 2 / H ∞ feedback robust model predictive control

Cited by 23 publications

References 13 publications

Robust Stability of Nonlinear Diffusion Fuzzy Neural Networks with Parameter Uncertainties and Time Delays

Robust Stability of Nonlinear Diffusion Fuzzy Neural Networks with Parameter Uncertainties and Time Delays

Fuzzy Predictive Control of Step-Down DC-DC Converter Based on Hybrid System Approach

Fuzzy predictive control of three-tank system based on a modeling framework of hybrid systems

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Product

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Design and input-to-state practically stable analysis of the mixed H ₂ / H _∞ feedback robust model predictive control