Model predictive control: Recent developments and future promise

Mayne, D. Q.

doi:10.1016/j.automatica.2014.10.128

Cited by 1,683 publications

(967 citation statements)

References 126 publications

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“…Remark 4: Note that the proposed approach based on the combination of a robust MPC with ISM has a general validity, in the sense that it could be used not only with the constraint tightening method here adopted, but also with any other robust MPC based approach [36], [37]. Yet, the combined use of MPC with ISM provides an advantage over the use of a robust MPC standalone.…”

Section: Definition 1 (Fhocp)mentioning

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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Section: Definition 1 (Fhocp)mentioning

confidence: 99%

Asynchronous Networked MPC With ISM for Uncertain Nonlinear Systems

Incremona

Ferrara

Magni

2017

IEEE Trans. Automat. Contr.

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“…Because of the improvements in algorithm efficiency, model predictive control can now be implemented on embedded hardware such as MCUs [7], [8], programmable logic controllers (PLC) [9]- [11], or field programmable gate arrays (FPGA) [4], [12], [13], etc. Efficiency improvements in nominal or deterministic MPC can be divided into two main categories [14]. To the first belong online MPC algorithms that attempt to minimize the real-time computational requirements by a context-oriented reformulation of the optimization problem, or by the use of advanced, often hardware-targeted optimization solvers [15]- [17]; sometimes referred to as implicit MPC.…”

Section: Introductionmentioning

confidence: 99%

Efficient Embedded Model Predictive Vibration Control via Convex Lifting

Gulan

Takács

Nguyễn

et al. 2019

IEEE Trans. Contr. Syst. Technol.

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Abstract-This paper presents an efficient real-time implementation of embedded model predictive control, adopted in the context of active vibration control with the objective of minimizing the tip deflection of lightly damped cantilever beams. In particular, we focus on memory and time-efficient explicit solutions to the associated constrained optimal control problem that are easily implementable on low-end embedded hardware. To this end, we exploit the concept of convex lifting and show how it can be used to devise low-complexity, regionless piecewise affine controllers without any loss of optimality and performance. Efficiency of this constructive procedure is quantified via an extensive complexity analysis, evidenced by a successful practical deployment and optimal vibration control performance using a family of 32-bit ARM Cortex-M based microcontroller platforms.

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“…Recent research in the Model Predictive Control (MPC) field has paid attention to the presence of unavoidable modeling uncertainties or external disturbances affecting the plants, so as to motivate the introduction of robust control strategies [Maciejowski, 2002;Rawlings and Mayne, 2009;Magni et al, 2009;Mayne, 2014]. Two main approaches have been followed: the first one, called openloop nominal approach, introduces tightened constraints in order to guarantee robust constraint satisfaction and recursive feasibility [Limon Marruedo et al, 2002;Lazar and Heemels, 2009;Pin et al, 2009], while the second one is based on the solution of a closed-loop min-max optimization problem that explicitly takes into account model uncertainty [Scokaert and Mayne, 1998;Fontes and Magni, 2003;Bemporad et al, 2003;Magni et al, 2003;Limon Marruedo et al, 2006].…”

Section: Introductionmentioning

confidence: 99%

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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Model predictive control: Recent developments and future promise

Cited by 1,683 publications

References 126 publications

Asynchronous Networked MPC With ISM for Uncertain Nonlinear Systems

Asynchronous Networked MPC With ISM for Uncertain Nonlinear Systems

Efficient Embedded Model Predictive Vibration Control via Convex Lifting

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

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