Robust output feedback model predictive control for constrained linear systems via interval observers

Souza, Alex dos Reis de; Efimov, Denis; Raïssi, Tarek; Ping, Xubin

doi:10.1016/j.automatica.2021.109951

Cited by 12 publications

(12 citation statements)

References 32 publications

(57 reference statements)

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“…To illustrate the 6) for a Lotka-Volterra model: x(t), x(t), x(t) versus time t efficiency of the proposed interval observer, it was applied to a Lotka-Volterra model. Development of these results to interval prediction and model predictive control design as in [41], [42] can be considered as a direction for future research.…”

Section: Discussionmentioning

confidence: 99%

An Interval Observer for Continuous-Time Persidskii Systems

Efimov

Polyakov

Ping

2022

2022 IEEE 61st Conference on Decision and Control (CDC)

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The paper deals with design of interval observers for a class of generalized Persidskii systems. The conditions of stability for the suggested nonlinear interval observer are formulated using linear matrix inequalities. The nonnegativity of this class of models is investigated for nonlinearities satisfying the incremental passivity conditions. The efficiency of the proposed observer is demonstrated on a Lotka-Volterra model.

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Section: Discussionmentioning

confidence: 99%

An Interval Observer for Continuous-Time Persidskii Systems

Efimov

Polyakov

Ping

2022

2022 IEEE 61st Conference on Decision and Control (CDC)

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“…To avoid these drawbacks, the MPC design is considered combined with LMI as a tool to take into account the multi-modeling approach. The MPC seems to be an appropriate choice due to its robustness and efficiency [21], [22]. One of the reasons for its popularity is that it is practically the only technique that provides a methodology that systematically considers constraints in control law synthesis.…”

Section: Mpc Design To Control Greenhouse Indoor Climate With Lmi App...mentioning

confidence: 99%

“…In the case of multi-modeling approach, the standard formulation is insufficient to solve the optimization problem. Here, the LMI tool is adopted to transform the MPC problem into a convex optimization problem [21], [22]. First, we recall the main of the MPC method and then extend it to multi-modeling case, which would ultimately lead to a convex optimization problem solved by the LMI tool.…”

Section: Mpc Design To Control Greenhouse Indoor Climate With Lmi App...mentioning

confidence: 99%

“…The problem is therefore the following: calculate the future increments of the control signal which minimize the criterion 𝐽 given by the relation (12) while satisfying constraints given by (22). This approach leads to the quadratic programming problem.…”

Section: Mpc Design To Control Greenhouse Indoor Climate With Lmi App...mentioning

confidence: 99%

“…The constraints (22) are initially non-symmetric matrix; thus, it is restructured in a diagonal symmetric form compatible with LMI formalism. Using the LMI solver of MATLAB, we obtain at each iteration the optimization objective defined as: ℎ = [𝛥𝑈, 𝛾] 𝑇 .…”

Section: Lmi Based Multi-model Predictive Control Designmentioning

confidence: 99%

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Linear matrix inequalities tool to design predictive model control for greenhouse climate

Moufid

Boutchich²,

Bennis³

2023

IJECE

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<span lang="EN-US">Modeling and regulating the internal climate of a greenhouse have been a challenge as it is a complex and time variant system. The main goal is to regulate the internal climate considering the difference between nighttime and diurnal phases of the day. To depict the comportment of the greenhouse, a multi model approach based on two multivariable black box models have been proposed representing the diurnal and nighttime phases of the day. The least-squares method is utilized to identify the parameters of these two models based on an experimental collected data. We have shown that these two models are more representative than one model to describe the dynamic behavior of the greenhouse. The second contribution is to control the internal temperature and hygrometry respecting constraints on actuators and controlled variables. For this purpose, a constrained model predictive control scheme based on the multi-modeling approach have been developed. The optimization problem of the control law is transformed to a convex optimization problem includes linear matrix inequalities (LMI). The simulation results show that the adopted control method of indoor climate allows rapid and precise tracking of set points and rejects effectively the external disturbances affecting the greenhouse.</span>

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Less‐conservative reduced‐order interval estimation: A new two‐step approach

Wang,

Zhang,

Meslem

et al. 2024

Intl J Robust & Nonlinear

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This article proposes a two‐step interval estimation method for continuous‐time linear time‐invariant systems with unknown but bounded disturbances and measurement noise. In the first step, method is used to design a robust reduced‐order observer that attenuates the impact of the system uncertainties on the estimation error. In the second step, a non‐conservative interval prediction of the estimation error is achieved by applying a symmetric set‐integration approach. The resulting tight set‐valued state estimation is obtained by combining the point estimation from the reduced‐order observer with the predicted estimation error set provided by the set‐integration method. Compared to the conventional methods, the proposed method is built on less‐restrictive design conditions, that allow one to deal with a large range of applications. On the other hand, numerical simulations show that the proposed method offers more precise interval estimation compared to an optimal reduced‐order interval observer selected from recent literature.

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Robust output feedback model predictive control for constrained linear systems via interval observers

Cited by 12 publications

References 32 publications

An Interval Observer for Continuous-Time Persidskii Systems

An Interval Observer for Continuous-Time Persidskii Systems

Linear matrix inequalities tool to design predictive model control for greenhouse climate

Less‐conservative reduced‐order interval estimation: A new two‐step approach

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