Fuzzy Lyapunov-Based Model Predictive Sliding-Mode Control of Nonlinear Systems: An Ellipsoid Recursive Feasibility Approach

Farbood, Mohsen; Shasadeghi, Mokhtar; Niknam, Taher; Safarinejadian, Behrouz

doi:10.1109/tfuzz.2021.3070680

Cited by 20 publications

(22 citation statements)

References 40 publications

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“…MPC requires a dynamical model of the controlled system to predict the system states' future behavior. The main advantages of the MPC are optimal performance and guaranteed constrained performance of the system [37]. In order to design the TSFM-based MPC for the CF-DVM, the obtained TSFM in (41) is employed.…”

Section: B Tsfm-based Model Predictive Control Of Cf-dvmmentioning

confidence: 99%

Dynamics and Model Predictive Control of Current-Fed Dickson Voltage Multiplier: TS Fuzzy Approach

et al. 2022

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This study presents a new approach to modeling and control the current-fed Dickson voltage multiplier (CF-DVM). The capacitor voltage relation and the input current are obtained. As all switching intervals are considered in detail, a highly accurate dynamic model is obtained, which can be easily extended for a CF-DVM with an arbitrary number of stages. Using the precise extracted model, the Takagi-Sugeno fuzzy model (TSFM) of the CF-DVM is provided, which is an exact equivalent representation of the CF-DVM nonlinear model. Then, a highly accurate and responsive model predictive controller (MPC) is designed based on an obtained TSFM of the CF-DVM to control the output voltage in an optimal and constrained manner. To obtain the control signal, the suggested optimization problem is converted to a quadratic programming (QP)-based problem which has a low online computational burden. Moreover, the performance of the proposed MPC is compared with the PI controller and the linear MPC. Finally, the simulation and experimental results demonstrate the promising merits of the proposed model and control approaches. INDEX TERMSDC-DC boost converter, current-fed Dickson voltage multiplier, TS fuzzy model, model predictive control.

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Section: B Tsfm-based Model Predictive Control Of Cf-dvmmentioning

confidence: 99%

Dynamics and Model Predictive Control of Current-Fed Dickson Voltage Multiplier: TS Fuzzy Approach

et al. 2022

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“…However, the proposed control is not constrained and not optimal. A cooperative ISMC with model predictive control (MPC) has been proposed in Farbood et al (2021a) for nonlinear systems based on the fuzzy approach. The suggested controller was obtained by solving a combination of an online and an offline optimization problem to reach the optimal and constrained performances in the presence of the parameter uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Optimal constrained integral sliding mode control design for fuzzy-based nonlinear systems

Daneshian

Forouzanfar

Moosavi

et al. 2023

Transactions of the Institute of Measurement and Control

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This study introduces a novel H∞ optimal constrained integral sliding mode control (OCISMC) for nonlinear systems due to the matched/unmatched external disturbances based on Takagi–Sugeno (TS) fuzzy models. Based on the Lyapunov function, the appropriate conditions are provided to reach the sliding mode from any initial condition and robustness against the matched disturbances is guaranteed. The optimal nominal part of the proposed OCISMC is designed based on an online optimization problem. To reach robustness against the unmatched disturbances and constrained controller, the H∞ performance and the limitations of the control signal are included in the design procedure of the suggested OCISMC. Furthermore, by considering a non-quadratic Lyapunov function (NQLF), a new linear matrix inequality problem with less conservatism is derived. Finally, two examples are simulated and compared with the existing works to illustrate the capability of the proposed OCISMC.

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“…It is also worth noting that the class of optimal control considered here differs from the fuzzy model optimal control case where only the model under control is fuzzy whereas the objective function is the standard one. For this, latest research on stability has reached a relatively a mature stage, see e.g., [18]- [22].…”

Section: Introductionmentioning

confidence: 99%

Infinite horizon fuzzy optimal control: optimality does not imply asymptotic stability

Teniou

Belarbi²

2023

IJECE

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In this paper, novel stability result for discrete-time infinite horizon optimal control using fuzzy objective functions is presented. For this class of control, the fuzzy goals and the fuzzy constraints introduced in the fuzzy objective function handle the constraints placed on both the state and the control vectors. We analyze the asymptotic stability of the equilibrium for the infinite horizon fuzzy optimal control law using the minimum aggregation operator. We show that the infinite horizon control with the minimum aggregation operator does not guarantee the asymptotic stability of the equilibrium in general. This is done by deriving an analytical solution of the control law for a simple linear system using a fuzzy dynamic programming approach. An example that shows the novel asymptotic stability result of the equilibrium for discrete-time infinite horizon optimal control with fuzzy objective function problem is given.

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Fuzzy Lyapunov-Based Model Predictive Sliding-Mode Control of Nonlinear Systems: An Ellipsoid Recursive Feasibility Approach

Cited by 20 publications

References 40 publications

Dynamics and Model Predictive Control of Current-Fed Dickson Voltage Multiplier: TS Fuzzy Approach

Dynamics and Model Predictive Control of Current-Fed Dickson Voltage Multiplier: TS Fuzzy Approach

Optimal constrained integral sliding mode control design for fuzzy-based nonlinear systems

Infinite horizon fuzzy optimal control: optimality does not imply asymptotic stability

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