Robust self-triggered min-max model predictive control for linear discrete-time systems

Shi, Yang; Gao, Jian; Li, Huiping; Xu, Dehong

doi:10.23919/chicc.2017.8028068

Cited by 3 publications

(6 citation statements)

References 20 publications

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“…Substituting (40) into (36), combining (15) with 𝜇 = t 𝜂 + T and utilizing the state constraints ||x(t 𝜂 + T; t 𝜂+1 )|| ∈ Ω(𝜁 ), ||x * (t 𝜂 + T; t 𝜂+1 )|| ∈ Ω(𝜀), we have…”

Section: Stability Analysismentioning

confidence: 99%

“…However, as the control signal of MPC is generated via solving an optimization problem online, the complexity of calculation is quite high. Combined with the widely used event-triggered control strategy [6][7][8], there exist two typical results to save communication resources and online computing resources, that is, event-triggered MPC (ET-MPC) [9][10][11][12][13] and self-triggered MPC (ST-MPC) [14][15][16][17] which solve the optimization problem only at selected sampling instants.…”

Section: Introductionmentioning

confidence: 99%

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An integral‐differential combined type event‐triggered model predictive control for perturbed linear systems

Bai

et al. 2023

Asian Journal of Control

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An integral‐differential combined (IDC) type event‐triggered model predictive control (ET‐MPC) strategy is constructed for linear time‐invariant (LTI) systems in this paper. Compared with the standard ET‐MPC method, the proposed MPC algorithm is developed based on an IDC type event‐triggering mechanism (ETM), which takes both the integral and change rate of the error between the optimal prediction and the real state into the consideration for the construction of the triggering condition. In this way, the dynamic performance can be considered more comprehensively, for effectively reducing the calculation and communication burdens of the ET‐MPC method. In addition, an improved robust constraint is used to deal with additive disturbances, and the dual‐mode control method is employed in the development of the strategy. Then the parameter conditions guaranteeing the iterative feasibility of the proposed algorithm, the closed‐loop stability of the LTI system, and the avoidance of Zeno behavior are given. Finally, two simulation examples demonstrate the effectiveness of the algorithm.

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Section: Stability Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An integral‐differential combined type event‐triggered model predictive control for perturbed linear systems

Bai

et al. 2023

Asian Journal of Control

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“…2) If 𝑣(𝑡) ≠ 0: we propose a robust MPC by solving the optimization problem based on a robust 𝐿 2 -𝐿 2 technique which has no special computational and analytical complexity. As a result, the robust MPC synthesized by ISMC has a very low online computational burden compared with the fuzzy-based min-max MPC [26], tube-based MPC [27], and min-max MPC [31].…”

Section: ) Qlf-based Mpc Designmentioning

confidence: 99%

Robustness Improvement of Computationally Efficient Cooperative Fuzzy Model Predictive-Integral Sliding Mode Control of Nonlinear Systems

et al. 2021

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This paper introduces a systematic and comprehensive method to design a constrained fuzzy model predictive control (MPC) cooperated with integral sliding mode control (ISMC) based on the Takagi-Sugeno (T-S) fuzzy model for uncertain continuous-time nonlinear systems subject to external disturbances. The proposed controller benefits from the robustness, optimality, and practical constraints considerations. The robustness against the uncertainties and matched external disturbances is achieved by the proposed ISMC without iterative calculation for obtaining the robust invariant set. The MPC schemes are designed separately based on the both quadratic and non-quadratic Lyapunov functions. By the proposed MPC, the states of the system reach the desired values in the optimal, constrained, and robust manner against the unmatched external disturbances. New linear matrix inequalities (LMIs) conditions are proposed to design both the proposed MPC schemes. Also, the practical constraints on the control signals are guaranteed in the design procedure based on the invariant ellipsoid set. To evaluate the effectiveness of the suggested strategy, some simulation and experimental tests were run. INDEX TERMST-S fuzzy models (TSFMs), Model predictive control (MPC), Integral sliding mode control (ISMC), DC-DC buck converter, Flexible joint robot.

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“…A promising accepted approach to overcome this detriment is to design a robust MPC controller. Robust MPC has received a great deal of attention of the researchers in past years (Ažman and Kocijan, 2008; Cannon and Kouvaritakis, 2005; Ding, 2010; Hu et al, 2004; Kothare et al, 1996; Poursafar et al, 2010), and we can also find further development in the recent papers (Koeln and Alleyne, 2018; Liu et al, 2018; ; Moradi et al, 2019; Shamaghdari and Haeri, 2020; Shi and Mao, 2019; Xu et al, 2019; Zhang et al, 2018).…”

Section: Introductionmentioning

confidence: 98%

“…Model Predictive Control (MPC) has become the predominant advanced control strategy in recent years and a methodology of choice in industrial applications due to its capability of optimally controlling multivariable constrained systems (Mayne et al, 2000). It is well known that MPC is currently widely employed in the industrial control processes and has increased benefit in comparison with Proportional -Integral -Derivative (PID) control as well as academic interest (Liu et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Robust constrained model predictive control design for piecewise non-linear systems with multiple operating points

Shokrollahi

Shamaghdari

2019

Transactions of the Institute of Measurement and Control

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In this paper, a robust model predictive control (MPC) scheme is developed for non-linear systems. We propose a new modeling approach, entitled piecewise non-linear, for plants with multiple operating points and with unstructured uncertainties. The systems, in each subregion, are composed of an affine model perturbed by an additive non-linear term which is locally Lipschitz. Considering a non-linear term in the model changes the control problem from a convex program to a non-convex one, which is much more challenging to solve. A standard dual-mode control strategy is introduced by parameterizing the infinite horizon control moves into a number of free control moves followed by a single state feedback law. The designed controller is robust against model uncertainty and guarantees system stability under switching between subregions. Numerical examples on a highly non-linear chemical process and another non-linear system are used to evaluate the applicability of the proposed method. Simulation results show a better performance in terms of speed of convergence and feasibility compared with the conventional robust MPC designs.

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Robust self-triggered min-max model predictive control for linear discrete-time systems

Cited by 3 publications

References 20 publications

An integral‐differential combined type event‐triggered model predictive control for perturbed linear systems

An integral‐differential combined type event‐triggered model predictive control for perturbed linear systems

Robustness Improvement of Computationally Efficient Cooperative Fuzzy Model Predictive-Integral Sliding Mode Control of Nonlinear Systems

Robust constrained model predictive control design for piecewise non-linear systems with multiple operating points

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