Decomposition of the min-max multi-model problem via integral sliding mode

Poznyak, Alexander S.; Bejarano, Francisco Javier

doi:10.1002/rnc.1009

Cited by 21 publications

(13 citation statements)

References 8 publications

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“…This numerical method practically makes workable the realization of the robust optimal control suggested in [7,8] and complements the results given in [12][13][14].…”

Section: Introductionsupporting

confidence: 57%

Numerical method for weights adjustment in minimax multi‐model LQ‐control

Poznyak

Bejarano

2007

Optim Control Appl Methods

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SUMMARYThe minimax linear quadratic problem, where 'max' is taken over a finite set of indices (models) and 'min' is taken over the set of admissible controls, is considered. The solution is obtained by the robust optimal control application. The control turns out to be a linear combination of the controls optimal for each individual model. This paper develops a numerical method for the optimal weights adjustment. An example shows a quick convergence of the proposed procedure.

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“…This numerical method practically makes workable the realization of the robust optimal control suggested in [7,8] and complements the results given in [12][13][14].…”

Section: Introductionsupporting

confidence: 57%

Numerical method for weights adjustment in minimax multi‐model LQ‐control

Poznyak

Bejarano

2007

Optim Control Appl Methods

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“…The following fuzzy surface is offered to facilitate the controller design stage via the use of an integral type sliding surface (Fridman et al, 2005)…”

Section: Resultsmentioning

confidence: 99%

Constrained integral fuzzy sliding mode control for an electric vehicle: An LMI-based approach

Amiri

Moosavi

Forouzanfar

et al. 2022

Transactions of the Institute of Measurement and Control

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This study investigates the issue of stability analysis and controller synthesis for an electric vehicle based on the Takagi–Sugeno fuzzy control approach. The proposed adaptive fuzzy integral sliding mode controller ensures that the electric vehicle system performs satisfactorily in the presence of uncertainties and exogenous disturbances, comprising low computational load, small control input amplitude, and reduced energy consumption. For efficient performance of the electric vehicle, the battery voltage (control input) and the vehicle’s speed (system output) are considered restricted. Simulations of an electric vehicle powered by a brushed direct current motor are utilized to evaluate the efficacy of the proposed controller. Ultimately, the design procedure validates the robust performance and fast stabilization of the electric vehicle speed in the presence of exogenous disturbances and parametric uncertainties.

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“…7 For systems with parametric uncertainties or unmeasured disturbances, sliding mode control (SMC) is a well known and attractive control strategy, due to its robustness, good transient performance and disturbance rejection capability. [8][9][10] It is also a natural choice for systems with switched actuators (such as power converters). 11 When the state vector is not measurable, the use state observers, as originally proposed by Bondarev et al, 12 for linear systems with known parameters, is an effective solution whereby chattering, the foremost flaw of SMC, can be avoided even in the presence of unmodeled dynamics.…”

Section: Introductionmentioning

confidence: 99%

Sliding mode control for disturbance rejection and estimation under measurement delay with PDE‐backstepping predictor

Pinto,

Oliveira,

Rodrigues

et al. 2023

Intl J Robust & Nonlinear

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In this article, we address the disturbance rejection problem for linear plants with delayed measured output. The output delay is allowed to be time‐varying and of arbitrary duration. The disturbances that may represent actuator faults are generated by an exogenous dynamic system and can be matched or unmatched. A predictor–based observer is designed via the PDE–backstepping (Partial Differential Equation–backstepping) method so that an estimate of the current state vector, as well as that of unmeasured disturbance, can be obtained. These estimates are used in to regulate the output and reject the disturbance by output feedback sliding mode control. In addition, a solution for finite‐time disturbance rejection (rather than exponential only) is also introduced when the sensor delay is constant. Numerical examples illustrate the performance and robustness of the both proposed predictor‐feedback control systems.

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Decomposition of the min-max multi-model problem via integral sliding mode

Cited by 21 publications

References 8 publications

Numerical method for weights adjustment in minimax multi‐model LQ‐control

Numerical method for weights adjustment in minimax multi‐model LQ‐control

Constrained integral fuzzy sliding mode control for an electric vehicle: An LMI-based approach

Sliding mode control for disturbance rejection and estimation under measurement delay with PDE‐backstepping predictor

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