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An optimization-based control strategy for energy efficiency of discrete manufacturing systems

ISA TRANSACTIONS 2019年 93卷 399-409页

作者： Diaz, Jenny L. Bermeo, Miguel Diaz-Rozo, Javier Ocampo-Martinez, Carlos Univ Politecn Cataluna Inst Robat & Informat Ind CSIC Automat Control Dept Llorens & Artigas 4-6Planta 2 E-08028 Barcelona Spain Ikergune AIE San Antolin 3 Elgoibar 20870 Spain

In order to reduce the global energy consumption and avoid highest power peaks during operation of manufacturing systems, an optimization-based controller for selective switching on/off of peripheral devices in a test bench that emulates the energy consumption of a periodic system is proposed. First, energy consumption models for the test-bench devices are obtained based on data and subspace identification methods. Next, a control strategy is designed based on both optimization and receding horizon approach, considering the energy consumption models, operating constraints, and the real processes performed by peripheral devices. Thus, a control policy based on dynamical models of peripheral devices is proposed to reduce the energy consumption of the manufacturing systems without sacrificing the productivity. Afterward, the proposed strategy is validated in the test bench and comparing to a typical rule-based control scheme commonly used for these manufacturing systems. based on the obtained results, reductions near 7% could be achieved allowing improvements in energy efficiency via minimization of the energy costs related to nominal power purchased. (C) 2019 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Energy management Discrete manufacturing systems optimization-based control Model predictive control

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Investigating amplitude amplification in optimization-based control for a continuous stirred tank reactor

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DIGITAL CHEMICAL ENGINEERING 2024年 13卷

作者： Nieman, Kip Durand, Helen Patel, Saahil Koch, Daniel Alsing, Paul M. Wayne State Univ 5050 Anthony Wayne Dr Detroit MI 48202 USA Res Lab Informat Directorate Rome NY 13441 USA

Quantum computers, which utilize quantum states called 'qubits' to process information, are becoming of increased interest in a variety of fields because they have the potential to outperform classical computers in certain situations. However, many challenges and hurdles remain, including the development of quantum algorithms that offer a speedup and can be applied to practical problems (some quantum algorithms that offer a speedup may only be applicable in specific and sometimes contrived circumstances). Our previous works have studied the use of quantum computers and quantum algorithms for process control applications. Our prior work evaluated the use of a gate-based quantum amplitude amplification algorithm when applied to a model predictive control optimization problem, specifically evaluating a linear systems example. The results suggested that the algorithm may be suited for the specific linear problem studied (meaning that there is a high probability of obtaining the desired result when measuring the quantum state after the algorithm has been applied). However, the results cannot be generalized to a wider class of systems or to systems containing nonlinearities. To begin to gain an understanding of how the amplitude amplification algorithm might be generalized, this work evaluates the use of the algorithm for the optimization-based control of a nonlinear dynamic system (specifically, a continuous stirred tank reactor). We seek to understand aspects of the problem, such as the development of a solution space, the effects of changing process and control parameters, and the operation of the amplitude amplification algorithm itself. The results demonstrate the challenges that still exist, and demonstrate a method involving inverse sampling transformations to potentially aid in addressing these issues.

关键词： Quantum computing optimization-based control

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optimization-based predictive control of a simulated moving bed process using an identified model

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CHEMICAL ENGINEERING SCIENCE 2006年第18期61卷 6165-6179页

作者： Song, In-Hyoup Lee, Sang-Beom Rhee, Hyun-Ku Mazzotti, Marco Seoul Natl Univ Sch Chem & Biol Engn Seoul 151744 South Korea Seoul Natl Univ Inst Chem Proc Seoul 151744 South Korea ETH Swiss Fed Inst Technol Inst Proc Engn CH-8092 Zurich Switzerland

For optimization-based dynamic control of simulated moving bed (SMB) process, a novel control strategy based on process identification, which is an extension of the earlier work (Song et al., 2006a. Identification and predictive control of a simulated moving bed process: purity control. Chemical Engineering Science 61, 1973-1986), is proposed. A linear output prediction model is obtained by the method of subspace identification and used for the dynamic control. The controller is designed for optimizing the production cost while maintaining the specified product purities. For all of these, the average purities over one switching period of the target components in extract and raffinate streams, the reciprocal productivity and the solvent consumption are selected as output variables, while the flow rates in Sections 1-4 are chosen as the manipulated variables. The realization of this concept is discussed and assessed on a virtual eight column SMB unit for a system following a bi-Langmuir isotherm. The identified prediction model is proven to be in good agreement with the first principles model considered as the actual SMB process. For typical control objectives encountered in actual operation, i.e., disturbance rejection and set-point tracking, it is shown that the proposed controller exhibits excellent performance, hence it is an effective tool for optimization-based control of SMB process. (c) 2006 Elsevier Ltd. All rights reserved.

关键词： simulated moving beds bi-Langmuir isotherm subspace identification model predictive control optimization-based control

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Regularity properties of optimization-based controllers

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EUROPEAN JOURNAL OF control 2025年 81卷

作者： Mestres, Pol Allibhoy, Ahmed Cortes, Jorge Univ Calif San Diego Dept Mech & Aerosp Engn 9500 Gilman Dr La Jolla CA 92093 USA Univ Calif Riverside Dept Mech Engn 900 Univ Ave Riverside CA 92521 USA

This paper studies regularity properties of optimization-based controllers, which are obtained by solving optimization problems where the parameter is the system state and the optimization variable is the input to the system. Under a wide range of assumptions on the optimization problem data, we provide an exhaustive collection of results about their regularity, and examine their implications on the existence and uniqueness of solutions and the forward invariance guarantees for the resulting closed-loop systems. We discuss the broad relevance of the results in different areas of systems and controls.

关键词： Parametric optimization optimization-based control Existence and uniqueness of solutions

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Adaptive learning-based model predictive control for uncertain interconnected systems: A set membership identification approach☆

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AUTOMATICA 2025年 171卷

作者： Aboudonia, Ahmed Lygeros, John Swiss Fed Inst Technol Automat Control Lab Zurich Switzerland

We propose a novel adaptive learning-based model predictive control (MPC) scheme for interconnected systems which can be decomposed into several smaller dynamically coupled subsystems with uncertain coupling. The proposed scheme is mainly divided into two main online phases;a learning phase and an adaptation phase. Set membership identification is used in the learning phase to learn an uncertainty set that contains the coupling strength using online data. In the adaptation phase, rigid tube-based robust MPC is used to compute the optimal predicted states and inputs. Besides computing the optimal trajectories, the MPC ingredients are adapted in the adaptation phase taking the learnt uncertainty set into account. These MPC ingredients include the prestabilizing controller, the rigid tube, the tightened constraints and the terminal ingredients. The recursive feasibility of the proposed scheme as well as the stability of the corresponding closed-loop system are discussed. The developed scheme is compared in simulations to existing schemes including robust, adaptive and learning-based MPC. (c) 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Model predictive control optimization-based control Learning-based control

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Stability and performance verification of optimization-based controllers

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AUTOMATICA 2017年 78卷 34-45页

作者： Korda, Milan Jones, Colin N. Univ Calif Santa Barbara Dept Mech Engn Engn 2 Bldg Santa Barbara CA 93105 USA Ecole Polytech Fed Lausanne Lab Automat Stn 9 CH-1015 Lausanne Switzerland

This paper presents a method to verify closed-loop properties of optimization-based controllers for deterministic and stochastic constrained polynomial discrete-time dynamical systems. The closed-loop properties amenable to the proposed technique include global and local stability, performance with respect to a given cost function (both in a deterministic and stochastic setting) and the 2 gain. The method applies to a wide range of practical control problems: For instance, a dynamical controller (e.g., a PID) plus input saturation, model predictive control with state estimation, inexact model and soft constraints, or a general optimization-based controller where the underlying problem is solved with a fixed number of iterations of a first-order method are all amenable to the proposed approach. The approach is based on the observation that the control input generated by an optimization-based controller satisfies the associated Karush-Kuhn-Tucker (KKT) conditions which, provided all data is polynomial, are a system of polynomial equalities and inequalities. The closed-loop properties can then be analyzed using sum-of-squares (SOS) programming. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： optimization-based control Sum-of-squares Model predictive control Output feedback Nonlinear control Stochastic control Robust control Discounted cost L-2 gain

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Fast Evaluation of Quadratic control-Lyapunov Policy

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IEEE TRANSACTIONS ON control SYSTEMS TECHNOLOGY 2011年第4期19卷 939-946页

作者： Wang, Yang Boyd, Stephen Stanford Univ Dept Elect Engn Stanford CA 94305 USA

The evaluation of a control-Lyapunov policy, with quadratic Lyapunov function, requires the solution of a quadratic program (QP) at each time step. For small problems this QP can be solved explicitly;for larger problems an online optimization method can be used. For this reason the control-Lyapunov control policy is considered a computationally intensive control law, as opposed to an "analytical" control law, such as conventional linear state feedback, linear quadratic Gaussian control, or H-infinity, too complex or slow to be used in high speed control applications. In this note we show that by precomputing certain quantities, the control-Lyapunov policy can be evaluated extremely efficiently. We will show that when the number of inputs is on the order of the square-root of the state dimension, the cost of evaluating a control-Lyapunov policy is on the same order as the cost of evaluating a simple linear state feedback policy, and less (in order) than the cost of updating a Kalman filter state estimate. To give an idea of the speeds involved, for a problem with 100 states and 10 inputs, the control-Lyapunov policy can be evaluated in around 67 mu s, on a 2 GHz AMD processor;the same processor requires 40 mu s to carry out a Kalman filter update.

关键词： Approximate dynamic programming model predictive control (MPC) optimization-based control real-time convex optimization stochastic control

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A flexible optimization framework for district systems based on topological graph and hybrid models

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JOURNAL OF PROCESS control 2021年 107卷 27-36页

作者： Manganini, Giorgio Riverso, Stefano Kouramas, Kostantinos Gran Sasso Sci Inst GSSI Laquila Italy United Technol Res Ctr Ireland Cork Ireland

This paper proposes a scalable and widely replicable framework for the energy management of district systems. The framework is built on a graph-based energy characterization of the system, whose supply and demand dynamics are numerically represented by hybrid mathematical models involving the available control inputs and measurable outputs. Following the graph topology connecting supply and demand nodes, a suitable optimization problem is formulated to take into consideration the actual availability of the involved control input and measurements, resulting in a computationally tractable mixed-integer linear control problem which can be implemented in a receding-horizon fashion. As an illustrative case, the framework is applied to a real-case district heating system, and the validity of the approach is showed through model-in-the-loop and system-in-the-loop numerical results. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： District energy management optimization-based control Topological graph Hybrid systems

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Constrained minimum variance and covariance steering based on affine disturbance feedback control parameterization

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2024年第11期34卷 7332-7370页

作者： Balci, Isin M. Bakolas, Efstathios Univ Texas Austin Dept Aerosp Engn & Engn Mech Austin TX USA Univ Texas Austin Dept Aerosp Engn & Engn Mech Austin TX 78712 USA

This paper deals with finite-horizon minimum-variance and covariance steering problems subject to constraints. The goal of the minimum variance problem is to steer the state mean of an uncertain system to a prescribed vector while minimizing the trace of its terminal state covariance whereas the goal in the covariance steering problem is to steer the covariance matrix of the terminal state to a prescribed positive definite matrix. The paper proposes a solution approach that relies on a stochastic version of the affine disturbance feedback control parametrization. In this control policy parametrization, the control input at each stage is expressed as an affine function of the history of disturbances that have acted upon the system. It is shown that this particular parametrization reduces the stochastic optimal control problems considered in this paper into tractable convex programs or difference of convex functions programs with essentially the same decision variables. In addition, the paper proposes a variation of this control parametrization that relies on truncated histories of past disturbances, which allows for sub-optimal controllers to be designed that strike a balance between performance and computational cost. The suboptimality of the truncated policies is formally analyzed and closed form expressions are provided for the performance loss due to the use of the truncation scheme. Finally, the paper concludes with a comparative analysis of the truncated versions of the proposed policy parametrization and other standard policy parametrizations through numerical simulations.

关键词： convex optimization optimization-based control stochastic optimal control

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Nonlinear predictive control on a heterogeneous computing platform

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control ENGINEERING PRACTICE 2018年 78卷 105-115页

作者： Khusainov, Bulat Kerrigan, Eric Suardi, Andrea Constantinides, George Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England Imperial Coll London Dept Aeronaut London SW7 2AZ England

We propose an implementation of an interior-point-based nonlinear predictive controller on a heterogeneous processor. The workload can be split between a general-purpose CPU and a field-programmable gate array to trade off the contradicting design objectives of control performance and computational resource usage. A new way of exploiting the structure of the KKT matrix yields significant memory savings. We report an 18x memory saving, compared to existing approaches, and a 6x speedup over a software implementation with an ARM Cortex-A9 processor. We also introduce a new release of Protoip, which abstracts low-level details of heterogeneous programming and allows processor-in-the-loop verification.

关键词： Predictive control Hardware software co-design Scheduling FPGA optimization-based control

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