Model predictive control with discrete actuators: Theory and application

Rawlings, James B.; Risbeck, Michael J.

doi:10.1016/j.automatica.2016.12.024

Cited by 176 publications

(246 citation statements)

References 20 publications

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“…As such, this feature should not be considered a primary contribution of the work. In particular, the presence of discrete inputs does not materially change stability analysis significantly in many circumstances, as pointed out in [36]. We believe extending the results in this paper to different action spaces to be straightforward.…”

Section: B Control Architecturesupporting

confidence: 52%

Robust Economic Model Predictive Control of Continuous-Time Epidemic Processes

Watkins

Nowzari

Pappas

2020

IEEE Trans. Automat. Contr.

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In this paper, we develop a robust economic model predictive controller for the containment of stochastic Susceptible-Exposed-Infected-Vigilant pSEIV q epidemic processes which drives the process to extinction quickly, while minimizing the rate at which control resources are used. The work we present here is significant in that it addresses the problem of efficiently controlling general stochastic epidemic systems without relying on mean-field approximation, which is an important issue in the theory of stochastic epidemic processes. This enables us to provide rigorous convergence guarantees on the stochastic epidemic model itself, improving over the mean-field type convergence results of most prior work. There are two primary technical difficulties addressed in treating this problem: (i) constructing a means of tractably approximating the evolution of the process, so that the designed approximation is robust to the modeling error introduced by the applied moment closure, and (ii) guaranteeing that the designed controller causes the closed-loop system to drive the SEIV process to extinction quickly. As an application, we use the developed framework for optimizing the use of quarantines in containing an SEIV epidemic outbreak.

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Section: B Control Architecturesupporting

confidence: 52%

Robust Economic Model Predictive Control of Continuous-Time Epidemic Processes

Watkins

Nowzari

Pappas

2020

IEEE Trans. Automat. Contr.

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“…A wealth of literature focuses on the closed-loop properties of the aforementioned iterative control methods, with novel and most recent results, specifically, in presence of discrete inputs, discussed in Rawlings and Risbeck (2017) [67].…”

Section: Standard Form Of State-space Modelsmentioning

confidence: 99%

A General State-Space Formulation for Online Scheduling

Gupta

Maravelias

2017

Processes

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Abstract:We present a generalized state-space model formulation particularly motivated by an online scheduling perspective, which allows modeling (1) task-delays and unit breakdowns; (2) fractional delays and unit downtimes, when using discrete-time grid; (3) variable batch-sizes; (4) robust scheduling through the use of conservative yield estimates and processing times; (5) feedback on task-yield estimates before the task finishes; (6) task termination during its execution; (7) post-production storage of material in unit; and (8) unit capacity degradation and maintenance. Through these proposed generalizations, we enable a natural way to handle routinely encountered disturbances and a rich set of corresponding counter-decisions. Thereby, greatly simplifying and extending the possible application of mathematical programming based online scheduling solutions to diverse application settings. Finally, we demonstrate the effectiveness of this model on a case study from the field of bio-manufacturing.

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“…Initially, a few industrial predictive control algorithms were proposed based on industrial demands, which are composed of model predictive heuristic control (MPHC) [10], dynamic matrix control (DMC) [11], generalized predictive control (GPC) [12], and predictive functional control (PFC) [13]. Since then, in order to obtain a better control performance, many improved methods about MPC have been exploited in various fields [14][15][16][17][18][19], especially in industrial processes [18][19][20][21][22][23][24]. But these methods need more tests to determine the control parameters in practical application.…”

Section: Introductionmentioning

confidence: 99%

Delay‐Range‐Dependent Robust Constrained Model Predictive Control for Industrial Processes with Uncertainties and Unknown Disturbances

Shi

Wang

et al. 2019

Complexity

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A delay-range-dependent robust constrained model predictive control is proposed for discrete-time system with uncertainties and unknown disturbances. The dynamic characteristic of the discrete-time system is established as a new extended state space model in which state variables and output tracking error are integrated and regulated independently. It is used as the design of control law of system, which cannot only guarantee the convergence and tracking performance but also offer more degrees of freedom for designed controller. Unlike the traditional robust model predictive control (RMPC), the novel, less conservative, and more simplified delay-range-dependent stable conditions are derived by linear matrix inequality (LMI) theory and some relaxed technologies, which make use of the information of the upper and lower bounds of the time-varying delay. Meanwhile, the H∞ performance index is introduced in the RMPC controller design, which can reject any unknown bounded disturbances. As a result, the design controller has better abilities of both tracking and disturbance rejection. The control results on the liquid level of tank system show that the proposed control method is effective and feasible.

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Model predictive control with discrete actuators: Theory and application

Cited by 176 publications

References 20 publications

Robust Economic Model Predictive Control of Continuous-Time Epidemic Processes

Robust Economic Model Predictive Control of Continuous-Time Epidemic Processes

A General State-Space Formulation for Online Scheduling

Delay‐Range‐Dependent Robust Constrained Model Predictive Control for Industrial Processes with Uncertainties and Unknown Disturbances

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