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

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

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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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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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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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Impact-aware task-space quadratic-programming control

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INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH 2023年第14期42卷 1265-1282页

作者： Wang, Yuquan Dehio, Niels Tanguy, Arnaud Kheddar, Abderrahmane Maastricht Univ Dept Adv Comp Sci Maastricht Netherlands KUKA Deutschland GmbH Augsburg Germany Univ Montpellier CNRS LIRMM Montpellier France Joint Robot Lab AIST CNRS JRL Tsukuba Japan Maastricht Univ Dept Adv Comp Sci Paul-Henri Spaaklaan 1 NL-6229 EN Maastricht Netherlands

Robots usually establish contacts at rigid surfaces with near-zero relative velocities. Otherwise, impact-induced energy propagates in the robot's linkage and may cause irreversible damage to the hardware. Moreover, abrupt changes in task-space contact velocity and peak impact forces also result in abrupt changes in robot joint velocities and torques;which can compromise controllers' stability, especially for those based on smooth models. In reality, several tasks would require establishing contact with moderately high velocity. We propose to enhance task-space multi-objective controllers formulated as a quadratic program to be resilient to frictional impacts in three dimensions. We devise new constraints and reformulate the usual ones to be robust to the abrupt joint state changes mentioned earlier. The impact event becomes a controlled process once the optimal control search space is aware of: (1) the hardware-affordable impact bounds and (2) analytically computed feasible set (polyhedra) that constrain post-impact critical states. Prior to and nearby the targeted contact spot, we assume, at each control cycle, that the impact will occur at the next iteration. This somewhat one-step preview makes our controller robust to impact time and location. To assess our approach, we experimented its resilience to moderate impacts with the Panda manipulator and achieved swift grabbing tasks with the HRP-4 humanoid robot.

关键词： Intentional impact tasks Impact-aware control optimization-based control

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optimization-based Trajectory Planning for Heat Conduction 25

Optimization-based Trajectory Planning for Heat Conduction

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25th International Carpathian control Conference (ICCC)

作者： Scholz, Stephan Berger, Lothar Univ Appl Sci Ravensburg Weingarten Control & Proc Engn Weingarten Germany

ISBN: (纸本)9798350350708;9798350350715

Heating and cooling procedures are crucial tasks in several industrial processes, and they have to be steered exactly and energy-efficiently to ensure a high-quality production. Hence, the design of optimal (open-loop) control strategies for heat conduction problems is an active research field. Flatness-based control is a popular open-loop control approach to steer parabolic partial differential equations like the heat equation. The control input signal is computed by a power series including the derivatives of a specified reference signal. However, this method has some limitations regarding, e.g. expensive computation due to a high number of derivatives and a weak capability to handle input constraints or nonlinear expressions in the original equations. In this contribution, we present a procedure to derive a parametrized input signal from flatness-based control for a simplified model. This parametrized input function is adjusted such that it fits to our original complex model. In detail, we compute a prototype input function with flatness-based control for a one-dimensional heat equation and design a parametrized input signal which is based on this prototype. We explain how to find suitable parameters for a well tracking of the measured and mean temperature. Moreover, we show that our proposed input signal is also able to handle input constraints and nonlinear boundary conditions. Finally, we discuss how to decrease the maximum input value while ensuring a suitable open-loop dynamics.

关键词： Heat Equation Flatness-based control optimization-based control

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Economic Model Predictive control for Cryogenic Air Separation Unit Startup 12

Economic Model Predictive Control for Cryogenic Air Separati...

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12th IFAC Symposium on Advanced control of Chemical Processes (ADCHEM)

作者： Quarshie, Anthony W. K. Matias, Jose Swartz, Christopher L. E. McMaster Univ Dept Chem Engn 1280 Main St West Hamilton ON L8S 4L7 Canada

Current energy market trends incentivize frequent optimal load changes, including the startup of cryogenic air separation units (ASUs), which are large electricity consumers. In this study, we assess the potential benefits of using an economic nonlinear model predictive control (ENMPC) framework for the optimal startup of ASUs in the presence of a measured process disturbance. We also considered strategies for improving the solution computational speed of the ENMPC problem. Our case study shows substantial profit recovery by the control strategy relative to offline pre-computed optimal inputs in response to the disturbance. Copyright (c) 2024 The Authors.

关键词： Disturbance rejection optimization-based control discontinuity smoothing

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Stabilization of Complementarity Systems via Contact-Aware controllers

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IEEE TRANSACTIONS ON ROBOTICS 2022年第3期38卷 1735-1754页

作者： Aydinoglu, Alp Sieg, Philip Preciado, Victor M. Posa, Michael Univ Penn Gen Robot Automat Sensing & Percept GRASP Lab Philadelphia PA 19104 USA Univ Penn Dept Elect & Syst Engn Philadelphia PA 19104 USA

We propose a control framework, which can utilize tactile information by exploiting the complementarity structure of contact dynamics. Since many robotic tasks, like manipulation and locomotion, are fundamentally based in making and breaking contact with the environment, state-of-the-art control policies struggle to deal with the hybrid nature of multicontact motion. Such controllers often rely heavily upon heuristics or, due to the combinatorial structure in the dynamics, are unsuitable for real-time control. Principled deployment of tactile sensors offers a promising mechanism for stable and robust control, but modern approaches often use this data in an ad hoc manner, for instance to guide guarded moves. This framework can close the loop on tactile sensors and it is noncombinatorial, enabling optimization algorithms to automatically synthesize provably stable control policies. We demonstrate this approach on multiple numerical examples, including quasi-static friction problems and a high dimensional problem with 10 contacts. We also validate our results on an experimental setup and show the effectiveness of the proposed method on an underactuated multicontact system.

关键词： Force Robots Tactile sensors Dynamics Task analysis Sensors optimization Bilinear matrix inequalities (BMIs) force control optimization-based control tactile feedback

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Risk-Aware Energy Management for Drive Mode control in Plug-in Hybrid Electric Vehicles

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IEEE ACCESS 2022年 10卷 103619-103631页

作者： Watanabe, Ryunosuke Ibuki, Tatsuya Sakayanagi, Yoshihiro Funada, Riku Sampei, Mitsuji Tokyo Inst Technol Tokyo 1528550 Japan Meiji Univ Sch Sci & Technol Kawasaki Kanagawa 2148571 Japan Toyota Motor Co Ltd Susono City Shizuoka 4101193 Japan

This paper proposes risk-aware energy management for drive mode control in plug-in hybrid electric vehicles (PHEVs) to reduce fuel consumption. This paper focuses on reducing fuel usage in scenarios in which energy demands along a planned route are stochastically estimated using historical driving data. In this scenario, the proposed method evaluates the risk of high energy consumption based on the conditional value at risk (CVaR) and entropic value at risk (EVaR) derived from Chernoff's inequality. The CVaR quantifies the high fuel consumption expected in the tail of the probability distributions as a cost function. In contrast, the EVaR bound constraint provides stochastic constraints of electricity capacity based on the property of the cumulant-generating function. Each risk evaluation is formulated as a mixed-integer exponential cone programming problem by expressing the drive modes of a PHEV as binary variables. The proposed method was demonstrated using a detailed vehicle simulator with real-world driving cycles. The designed controller achieved, on two selected routes, 8.60% and 16.09% improvements on average, and 10.75% and 12.85% reductions at the 75th percentile compared to a commercial method. The simulations indicated that we can design the controller characteristics by adjusting the risk-awareness of energy loss. The conservativeness of the risk evaluation is also discussed based on the simulation results.

关键词： Energy management Reactive power Tail Random variables Probability distribution Plug-in hybrid electric vehicles Switches Energy management optimization-based control plug-in hybrid electric vehicles risk measurement

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