Multiplexed model predictive control

Ling, Keck Voon; Maciejowski, Jan M.; Richards, Arthur; Wu, Bing

doi:10.1016/j.automatica.2011.11.001

Cited by 38 publications

(24 citation statements)

References 30 publications

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“…The constraints are

({, \begin{matrix} array \\ array - 4 ⩽ z_{1} ⩽ 4, - 4 ⩽ z_{3} ⩽ 4, - 4 ⩽ z_{5} ⩽ 4, \\ array - 4 ⩽ z_{7} ⩽ 4, - 0.5 ⩽ u_{1} ⩽ 0.5, - 0.5 ⩽ u_{2} ⩽ 0.5 . \end{matrix})

A possible approach to control the mass spring system is to perform multi‐variable MPC design, but this may lead to a large number of decision variables. Here, we propose MPC with asynchronous inputs .…”

Section: Examplesmentioning

confidence: 99%

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Fast model predictive control for linear periodic systems with state and control constraints

Nguyen

Bourdais

Gutman

2017

Intl J Robust & Nonlinear

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International audienceThe design of stabilizing model predictive control laws for discrete-time linear periodic systems with state and control constraints is considered. Two algorithms are presented. The first one is based on interpolation between several unconstrained periodic controllers. Among them, one controller is chosen for the performance while the rest are used to extend the domain of attraction. The second algorithm aims to improve the performance by combining model predictive control and interpolating control. The proposed approaches not only guarantee recursive feasibility and asymptotic stability but also are optimal for states near the origin

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“…The constraints are

({, \begin{matrix} array \\ array - 4 ⩽ z_{1} ⩽ 4, - 4 ⩽ z_{3} ⩽ 4, - 4 ⩽ z_{5} ⩽ 4, \\ array - 4 ⩽ z_{7} ⩽ 4, - 0.5 ⩽ u_{1} ⩽ 0.5, - 0.5 ⩽ u_{2} ⩽ 0.5 . \end{matrix})

Section: Examplesmentioning

confidence: 99%

“…A possible approach to control the mass spring system is to perform multi-variable MPC design, but this may lead to a large number of decision variables. Here, we propose MPC with asynchronous inputs [31].…”

Section: Examplementioning

confidence: 99%

Fast model predictive control for linear periodic systems with state and control constraints

Nguyen

Bourdais

Gutman

2017

Intl J Robust & Nonlinear

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“…ii) Otherwise, exclude the stability constraint (9) from the MPC optimization formulation. b) The MPC optimization is subsequently solved for the optimal control sequenceû * ( ).…”

Section: E Quadratically-constrained Mpc (Qc-mpc)mentioning

confidence: 99%

“…The following LMIs are derived from (11), (9), and the dissipation inequality for of the form + + − ≤ ∑ 1 ( , ). They are obtained by substituting the model of or S into the corresponding inequalities, rearranging them, and adequately applying the Schur complement: ⎡…”

Section: Decentralized Constrained-state Feedbackmentioning

confidence: 99%

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Application of quadratically-constrained model predictive control in power systems

Tran

Foo

Ling

et al. 2014

2014 13th International Conference on Control Automation Robotics &Amp; Vision (ICARCV)

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Simulations for the quadratically-constrained model predictive control (qc-MPC) with power system linear models are studied in this work. In qc-MPC, the optimization is imposed with two additional constraints to achieve the closed-loop system stability and the recursive-feasibility simultaneously. Instead of engaging the traditional terminal constraint for MPC, both constraints in qc-MPC are imposed on the first control vector of the MPC control sequence. As a result, qc-MPC has the potential for further extension to the control of network centric power systems. The algorithm of qc-MPC has been developed in a previous paper. Here, simulation studies with small-signal linear models of three typical power systems are presented to demonstrate its efficacy. We also develop a computational strategy for the decentralized static state-feedback control using the same quadratic dissipativity constraint as of the qc-MPC. Only state constraints are considered in the state feedback design. A comparison is then provided in the simulation study of qc-MPC relatively to the constrained-state feedback control. 193We36.5

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A Novel Multi‐Step Model Predictive Control for Multi‐Input Systems

Zhang¹,

Wang²

2014

Asian Journal of Control

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In this paper, a novel control scheme, known as multi-step model predictive control (MPC), is presented for multi-input systems. To reduce computational complexity, only one or several control inputs are optimized at each time interval. A multi-step MPC scheme for nominal multi-input systems is presented. A set invariance condition for polytopic uncertain systems is identified, and the invariant set is determined by solving a linear matrix inequality optimisation problem. Based on the set invariance condition, a min-max multi-step robust MPC algorithm is proposed for the polytopic uncertain systems. Two examples are carried out to demonstrate the effectiveness of the proposed algorithms.

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Multiplexed model predictive control

Cited by 38 publications

References 30 publications

Fast model predictive control for linear periodic systems with state and control constraints

Fast model predictive control for linear periodic systems with state and control constraints

Application of quadratically-constrained model predictive control in power systems

A Novel Multi‐Step Model Predictive Control for Multi‐Input Systems

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