Fuzzy Model Predictive Control With Enhanced Robustness for Nonlinear System via a Discrete Disturbance Observer

Zhu, Jianzhong; Nguang, Sing Kiong

doi:10.1109/access.2020.3043359

Cited by 6 publications

(2 citation statements)

References 48 publications

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“…The first category of such approaches is based on feedforward compensation. The disturbance observer (DOB) 13,14 or extended state observer (ESO) 15,16 is used to estimate the lumped disturbance and the feedforward control law is designed separately to compensate the effects of disturbance. The second category integrates the disturbance estimation into predictive model.…”

Section: Introductionmentioning

confidence: 99%

Model predictive control for gas turbine shaft speed based on model compensation using extended state observer

Zhang

Xue

Wei

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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Gas turbine engines have offered advanced power generation with relatively small size on marine vessels. The operational flexibility has high control requirements for shaft speed regulation. Model predictive control provides a methodology for safe and efficient operation of gas turbines. However, when working at partial load, the controller degrades due to the mismatch between predictive model in model predictive control and dynamics of gas turbine. To deal with the issue of model adaptability in model predictive control, model compensation–based model predictive control is proposed for the gas turbine operation. The dynamics of the plant is modified by the extended state observer to get close to the pure integrator. The model predictive control is devised based on the modified plant. The wide-range load regulation and disturbance rejection of gas turbine have been improved by leveraging the benefits of model compensation–based model predictive control.

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Section: Introductionmentioning

confidence: 99%

Model predictive control for gas turbine shaft speed based on model compensation using extended state observer

Zhang

Xue

Wei

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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show abstract

“…Authors in [31] proposed a secondary voltage control based on the combination of fuzzy systems and MPC; however, only fixed delays and low disturbances are considered. The authors in [32] proposed a predictive controller for time‐delayed T‐S fuzzy systems by taking advantage of the linear matrix inequality (LMI) techniques. Although the non‐linear characteristics of μ G have been studied in the above‐mentioned papers, the appearance of disturbances and model uncertainties, which are ubiquitous in the industrial process, can cause severe degradation of the control performance.…”

Section: Introductionmentioning

confidence: 99%

Robust fuzzy model predictive control for voltage regulation in islanded microgrids

Mottaghizadeh

Aminifar

Amraee

et al. 2021

IET Generation Trans & Dist

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An effective robust fuzzy model predictive control (RFMPC) method for secondary voltage control in islanded microgrids (μGs) is presented here. In contrast to the existing techniques, which require a detailed model of μG and an ideal communication network between μG central controller and primary local controllers, RFMPC is synthesized for a non-linear model of the μG with various time delays, uncertainties, and bounded disturbances. The famous Takagi-Sugeno fuzzy approach is adopted to approximate the inherently non-linear model of μG by locally linear dynamics. The Lyapunov-Razumikhin functional method is exploited to deal with time delays. In this regard, sufficient conditions are provided in the form of linear matrix inequalities (LMIs). Then, a sequence of control laws corresponding to a set of terminal constraints is computed offline. Doing so, the online stage is reduced to solving a convex problem with LMI constraints considering the sequence of constraint sets obtained in the offline stage, thereby reducing the computational burden significantly. Robust positive invariance and input-to-state stability property concerning communication network deficiency are then speculated. The effectiveness of the proposed RFMPC is verified via a comprehensive suite of simulations in the MATLAB/SimPowerSystems environment.

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Model predictive control based control strategy for battery energy storage system integrated power plant meeting deep load peak shaving demand

Zhu

Cui

Weidong

2022

Journal of Energy Storage

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Fuzzy Model Predictive Control With Enhanced Robustness for Nonlinear System via a Discrete Disturbance Observer

Cited by 6 publications

References 48 publications

Model predictive control for gas turbine shaft speed based on model compensation using extended state observer

Model predictive control for gas turbine shaft speed based on model compensation using extended state observer

Robust fuzzy model predictive control for voltage regulation in islanded microgrids

Model predictive control based control strategy for battery energy storage system integrated power plant meeting deep load peak shaving demand

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