Integral Sliding Mode Control for Nonlinear Systems With Matched and Unmatched Perturbations

Rubagotti, Matteo; Estrada, Antonio; Castaños, Fernando; Ferrara, Antonella; Fridman, Leonid

doi:10.1109/tac.2011.2159420

Cited by 251 publications

(174 citation statements)

References 20 publications

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“…It is possible to prove that the equivalent control, that is the control obtained by posing the first time derivative of the auxiliary sliding manifold equal to zero, i.e.,Σ = 0, is such that u ISMeq = −w m , so as to have a rejection of the matched uncertainty in sliding mode from t 0 . Moreover it is possible to show that the choice of the sliding manifold such that G = B T allows one to maintain the unmatched uncertainty without amplifying them [Rubagotti et al, 2011b].…”

Section: Integral Sliding Mode Controlmentioning

confidence: 99%

“…where only the unmatched uncertainty is present, that, thanks to a suitable choice of the sliding manifold, is not affected by the ISM control action [Rubagotti et al, 2011b]. The MPC is then computed on the basis of the model relying on system (22), according to the FHOCP described in the previous section.…”

Section: Robust Mpc/ism In Continuous-time For Sampled-data Nonlinearmentioning

confidence: 99%

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Hierarchical Model Predictive/Sliding Mode Control of Nonlinear Constrained Uncertain Systems

2015

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Abstract-This paper presents an overview of some hierarchical control schemes composed by a high level Model Predictive Control (MPC) and a low level Sliding Mode Control (SMC). The latter is realized by using the so-called Integral Sliding Mode (ISM) control approach and is meant to reject the matched disturbances affecting the plant, thus providing a system with reduced uncertainty for the MPC design. Both continuous and discrete-time solutions are discussed in the paper. Moreover, assuming the presence of a network in the control loop, a networked version of the control scheme is presented. It is a model-based event-triggered MPC/ISM control scheme aimed at minimizing the packets transmission. The input-to-state (practical) stability properties of the proposed approaches are also addressed in the paper.

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Section: Integral Sliding Mode Controlmentioning

confidence: 99%

Section: Robust Mpc/ism In Continuous-time For Sampled-data Nonlinearmentioning

confidence: 99%

Hierarchical Model Predictive/Sliding Mode Control of Nonlinear Constrained Uncertain Systems

2015

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“…The motivation for using ISM control, apart from its property of providing robustness versus matched uncertain terms, is also given by its capability of enforcing sliding modes of the controlled system since the initial time instant, without amplifying the remaining unmatched uncertain terms [27], [28]. The presence of the ISM local controller allows MPC controller to solve the optimization problem relying on a system with reduced uncertainties, while fulfilling the state and control constraints.…”

Section: Introductionmentioning

confidence: 99%

Asynchronous Networked MPC With ISM for Uncertain Nonlinear Systems

Incremona

Ferrara

Magni

2017

IEEE Trans. Automat. Contr.

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Abstract-A model-based event-triggered control scheme for nonlinear constrained continuous-time uncertain systems in networked configuration is presented in this paper. It is based on the combined use of Model Predictive Control (MPC) and Integral Sliding Mode (ISM) control, and it is oriented to reduce the packets transmission over the network both in the direct path and in the feedback path, in order to avoid network congestion. The key elements of the proposed control scheme are the ISM local control law, the MPC remote controller, a smart sensor and a smart actuator, both containing a copy of the nominal model of the plant. The role of the ISM control law is to compensate matched uncertainties, without amplifying the unmatched ones. The MPC controller with tightened constraints generates the control component oriented to comply with state and control requirements, and is asynchronous since the underlying constrained optimization problem is solved only when a triggering event occurs. In the paper, the robustness properties of the controlled system are theoretically analyzed, proving the regional input-tostate practical stability of the overall control scheme.

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“…In (Incremona et al, 2015;Benattia et al, 2015) hierarchical control schemes, consisting of a high level MPC and a low level SMC (Rubagotti et al, 2011), are considered. The role of the SMC component is to reject the matched disturbances acting the plant, and to reduce uncertainty for the MPC design in that way.…”

Section: Introductionmentioning

confidence: 99%

Tube Model Predictive Control with an Auxiliary Sliding Mode Controller

Spasić¹,

Hovd²,

Mitic³

et al. 2016

MIC

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This paper studies Tube Model Predictive Control (MPC) with a Sliding Mode Controller (SMC) as an auxiliary controller. It is shown how to calculate the tube widths under SMC control, and thus how much the constraints of the nominal MPC have to be tightened in order to achieve robust stability and constraint fulfillment. The analysis avoids the assumption of infinitely fast switching in the SMC controller.

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Integral Sliding Mode Control for Nonlinear Systems With Matched and Unmatched Perturbations

Cited by 251 publications

References 20 publications

Hierarchical Model Predictive/Sliding Mode Control of Nonlinear Constrained Uncertain Systems

Hierarchical Model Predictive/Sliding Mode Control of Nonlinear Constrained Uncertain Systems

Asynchronous Networked MPC With ISM for Uncertain Nonlinear Systems

Tube Model Predictive Control with an Auxiliary Sliding Mode Controller

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