Self-optimizing control – A survey

Jäschke, Johannes; Cao, Yi; Kariwala, Vinay

doi:10.1016/j.arcontrol.2017.03.001

Cited by 69 publications

(33 citation statements)

References 112 publications

(120 reference statements)

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“…[5,6] As an example, Wenzel et al [7] proposed an application of this hierarchical optimization system to coordinate the optimization of a large scale central energy facility relating to the energy consumption and participation in demand response programs. Jäschke and Skogestad [8] classified the approaches for optimal process into three categories: (a) model used online (eg, real-time optimization (RTO) [9] ; (b) model used offline (eg, self-optimizing control (SOC)) [10] ; and, (c) explicit model not used, but in this case a necessary conditions of optimality (eg, NCO-tracking). [11] All of these categories are measure dependent and/or model dependent, that is, they depend on online measurements and accurate plant models to estimate the best economic profit and send targets/set-points to the plant.…”

Section: Introductionmentioning

confidence: 99%

Economic performance tracking for nonsquare MPCs based on a two‐layer approach

2021

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Range tracking for nonsquare systems is frequently adopted in process industries and its implementation is developed by model predictive control technologies. However, these approaches differ from those most considered in academia, with set-point tracking and the same number of controlled and manipulated variables. In this scope, real-time optimization (RTO) emerges as a diffused technology to improve the economic performance considering process, safety, and environmental constraints. In this work, a way to integrate economic aspects in industrial model predictive controllers (MPCs) by treating the nonlinear economic function as an output of the process model is proposed. The tracking error of the cost function is monitored, and its real value is estimated by a state estimator. The approach was applied to a nonsquare range system, exemplifying a continuous stirred-tank reactor (CSTR) with Van de Vusse kinetics, and showed that it is capable of tracking the minimum cost operation robustly. The paper also compares the proposed strategy to the traditional RTO implementation, which provides optimized targets, and presents slight improvement in the steady-state operation regarding the optimal cost seeking.

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Section: Introductionmentioning

confidence: 99%

Economic performance tracking for nonsquare MPCs based on a two‐layer approach

2021

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“…Some artificial intelligence techniques are also employed for the global SOC method [30,31]. For more detailed information about the SOC method, readers are referred to an overview of the state-of-the-art and open issues in the development of SOC by Jaschke [32].…”

Section: Introductionmentioning

confidence: 99%

A Retrofit Hierarchical Architecture for Real-Time Optimization and Control Integration

Xie

et al. 2020

Processes

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To achieve the optimal operation of chemical processes in the presence of disturbances and uncertainty, a retrofit hierarchical architecture (HA) integrating real-time optimization (RTO) and control was proposed. The proposed architecture features two main components. The first is a fast extremum-seeking control (ESC) approach using transient measurements that is employed in the upper RTO layer. The fast ESC approach can effectively suppress the impact of plant-model mismatch and steady-state wait time. The second is a global self-optimizing control (SOC) scheme that is introduced to integrate the RTO and control layers. The proposed SOC scheme minimizes the global average loss based on the approximation of necessary conditions of optimality (NCO) over the entire operating region. A least-squares regression technique was adopted to select the controlled variables (CVs) as linear combinations of measurements. The proposed method does not require the second order derivative information, therefore, it is numerically more reliable and robust. An exothermic reaction process is presented to illustrate the effectiveness of the proposed method.

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“…This approach could be successfully used in the self-optimizing control context, where the aim is to systematically select the controlled variables (CVs) such that, by controlling them at constant setpoints, the impact of uncertain and varying disturbances on the economic optimality is minimized. 18 In this scenario, the optimal input can be computed as a combination of individually optimal inputs after a proper choice of CVs has been made. If the CVs might change, the proposed modular approach allows to update the optimal controller by simply replacing the related output components.…”

Section: Introductionmentioning

confidence: 99%

Optimality principles and decomposition of tracking controllers for weakly dual redundant systems

Corona

Cristofaro

2018

Optim Control Appl Methods

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Summary In this paper, the problem of optimizing the output regulation of a weakly dual redundant plant is addressed. When the system is underactuated, only a subset of the outputs can be arbitrarily controlled and the remaining ones are constrained. With a specific focus on the asymptotic output tracking problem for single‐input systems, we investigate the connection between the overall optimal input and the individually optimal controllers, which leads to the perfect tracking of each output reference. Such relationship is analyzed both for open‐loop and closed‐loop controllers for different cost functions and types of references, and the optimality principles are established, provided that suitable structural conditions are met by the plant matrices.

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Self-optimizing control – A survey

Cited by 69 publications

References 112 publications

Economic performance tracking for nonsquare MPCs based on a two‐layer approach

Economic performance tracking for nonsquare MPCs based on a two‐layer approach

A Retrofit Hierarchical Architecture for Real-Time Optimization and Control Integration

Optimality principles and decomposition of tracking controllers for weakly dual redundant systems

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