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Simple Tuning of Arbitrary controllers using Governors

IFAC-PapersOnLine 2023年第2期56卷 8439-8444页

作者： Miroslav Fikar Karol Kiš Martin Klaučo Martin Mönnigmann Institute of Information Engineering Automation and Mathematics Faculty of Chemical and Food Technology Slovak University of Technology in Bratislava Radlinskeho 9 SK-812 37 Bratislava Automatic Control and Systems Theory Ruhr-Universität Bochum Germany

The paper proposes a preliminary idea to an intuitive and straightforward mechanism for tuning arbitrary controllers and changing the closed-loop performance. While the structure and parameters of the original controller are kept unchanged, the inputs to the nominal controller are modified such that the closed-loop response becomes slower or faster. Such a governor setup implementation is advantageous, especially when re-tuning the original controller is impractical or impossible. The practicability and versatility of this approach is presented using several examples spanning from simple loops with PID controllers to complex nonlinear closed-loop systems with optimal and approximated explicit MPC.

关键词： Tuning constrained control predictive control controller structure governor

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Robust co-design framework for buildings operated by predictive control

arXiv

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arXiv 2024年

作者： Falugi, P. O’Dwyer, E. Zagorowska, M.A. Kerrigan, E.C. Nie, Y. Strbac, G. Shah, N. Engineering and Construction Department University of East London London United Kingdom Department of Chemical Engineering Imperial College London London United Kingdom Mechanical Engineering Department Delft University of Technology Delft Netherlands Department of Aeronautics Imperial College London London United Kingdom Department of Electrical and Electronic Engineering Imperial College London London United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield United Kingdom

Cost-effective decarbonisation of the built environment is a stepping stone to achieving net-zero carbon emissions since buildings are globally responsible for more than a quarter of global energy-related CO2 emissions. Improving energy utilization and decreasing costs naturally requires considering multiple domain-specific performance criteria. The resulting problem is often computationally infeasible. The paper proposes an approach based on decomposition and selection of significant operating conditions to achieve a formulation with reduced computational complexity. We present a robust framework to optimise the physical design, the controller, and the operation of residential buildings in an integrated fashion, considering external weather conditions and time-varying electricity prices. The framework explicitly includes operational constraints and increases the utilization of the energy generated by intermittent resources. A case study illustrates the potential of co-design in enhancing the reliability, flexibility and self-sufficiency of a system operating under different conditions. Specifically, numerical results demonstrate reductions in costs up to 30% compared to a deterministic formulation. Furthermore, the proposed approach achieves a computational time reduction at least 10 times lower compared to the original problem with a deterioration in the performance of only 0.6%. © 2024, CC BY.

关键词： Zero-carbon

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Optimization of Rice Bran Protein Extraction Using Choline Chloride-Glycerol Deep Eutectic Solvent Using Response Surface Methodology (RSM) 2nd

Optimization of Rice Bran Protein Extraction Using Choline C...

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2nd International Conference on Biomass Utilization and Sustainable Energy, ICoBiomasSE 2022

作者： Kamal Ramlee, Kamalea Amaleena Farhana Muhammad Nor, Intan Nurfazliyana Md Sarip, Mohd Sharizan Mohd Zainudin, Mohd Asraf Abu Bakar, Amirul Ridzuan Nawawi, Mohd Azizi Nik Daud, Nik Muhammad Azhar UniCITI Alam Campus Sungai Chuchuh Perlis Padang Besar02100 Malaysia Kompleks Pusat Pengajian Jejawi 3 Perlis Arau02600 Malaysia Division of Process Control and Process Systems Engineering Department of Chemical Engineering Kyoto University Kyotodaigau-Katsura Nishikyo-ku Kyoto-shi Kyoto615-8510 Japan Faculty of Applied Sciences Universiti Teknologi MARA Shah Alam40450 Malaysia Kompleks Pusat Pengajian Jejawi 3 Perlis Arau02600 Malaysia

ISBN: (纸本)9789819916948

A by-product of the rice milling process, rice bran contains four different types of protein: albumin, globulin, prolamin, and glutelin. These proteins are widely known for being hypoallergenic and having nutritional benefits. In order to increase the value of rice bran, the protein was extracted via deep eutectic solvent (DES). Response Surface Methodology (RSM) was used to assess the impact of three operational conditions, including temperature, extraction duration, and the ratio of rice bran to DES, on the yield of rice bran protein after the precipitates were converted into powder form by freeze drying. At optimal working conditions, which were 60 °C, 2 h, and a 1:9 ratio of rice bran to DES, the method’s results showed that the highest extracted protein of rice bran was 16.254%. Several techniques, including Fourier transform infrared spectroscopy (FTIR) and Kjeldahl studies, have been used to demonstrate the presence of protein in rice bran powder. In the rice bran protein (RBP), amine (1640.70 cm−1), alcohol (3229.74 cm−1), and alkane (2925.37 cm−1) were all detected using FTIR analysis. Furthermore, Kjeldahl analysis revealed that 15.61% of the rice bran powder’s protein content is present. In conclusion, rice bran’s value as a functional meal can be increased by adding protein through the use of a deep eutectic solvent called green solvent. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Proteins

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Development of Block Oriented Recursive and Constrained Parameter Estimation Schemes for ARMAX Models

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IFAC-PapersOnLine 2022年第7期55卷 7-12页

作者： Shrita Singh Kunal Kumar Sachin C. Patwardhan Department of Energy Science and Engineering Indian Institute of Technology Bombay Mumbai-400076 India Department of Systems and Control Engineering Indian Institute of Technology Bombay Mumbai-400076 India Department of Chemical Engineering Indian Institute of Technology Bombay Mumbai-400076 India

In the process industry, black box linear perturbation models are often used for the development of model predictive controllers. Maintaining a high-quality model so as to achieve good control performance in the face of changing operating conditions is a difcult task. In adaptive control schemes, the model parameters are updated online using recursive least square schemes. These recursive schemes typically update the model parameters at every sampling instant. Since in many chemical/biological processes, the model parameters change at a relatively slow rate compared to state variables, it is beneficial to update the model parameters using blocks of data points instead of updating at each sampling instant. In this work, a constrained update scheme based on blocks of data is proposed for updating ARMAX model parameters online. The inclusion of constraints ensures that the noise model is stable and inversely stable. The constrained formulation is further simplifed to arrive at two unconstrained recursive parameter update schemes. The efficacy of the proposed schemes is demonstrated using a simulation study on an artificial system and experimental data obtained from a temperature control system and benchmark quadruple tank system.

关键词： Recursive least squares Block recursive estimation Constrained parameter estimation

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Decentralized Zeno-Free Event-Triggered control For Multiple Networks Subject to Stochastic Network Delays and Poisson Pulsing Attacks

arXiv

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arXiv 2024年

作者： Zhang, Dandan Jin, Xin Su, Hongye The State Key Laboratory of Industrial Control Technology The Institute of Cyber-Systems and Control The College of Control Science and Engineering Zhejiang University Hangzhou310027 China The Research Institute of Intelligent Complex Systems Fudan University Shanghai200433 China The Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China

By designing the decentralized time-regularized (Zeno-free) event-triggered strategies for the state-feedback control law, this paper considers the stochastic stabilization of a class of networked control systems, where two sources of randomness exist in multiple decentralized networks that operate asynchronously and independently: the communication channels are constrained by the stochastic network delays and also by Poisson pulsing denial-of-service (Pp-DoS) attacks. The time delay in the network denotes the length from a transmission instant to the corresponding update instant, and is supposed to be a continuous random variable subject to certain continuous probability distribution;while the attacks' cardinal number is a discrete random variable supposed to be subject to Poisson distribution, so the inter-attack time, i.e., the time between two consecutive attack instants, is subject to exponential distribution. The considered system is modeled as a stochastic hybrid formalism, where the randomness enters through the jump map into the reset value (the inter-attack time directly related) of each triggered strategy. By only sampling/transmitting state measurements when needed and simultaneously by taking the specific medium access protocols into account, the designed event-triggered strategies are synthesized in a state-based and decentralized form, which are robust (tolerable well) to stochastic network delays, under different tradeoff-conditions between the minimum inter-event times, maximum allowable delays (i.e., potentially tolerable delays) and the frequencies of attacks. Using stochastic hybrid tools to combine attack-active parts with attack-over parts, the designed triggered strategies, if designed well according to the actual system needs, can tolerate (be resilient to) the Pp-DoS attacks and stochastic network delays without jeopardizing the stability and Zeno-freeness, which is verified by the robust global attitude stabilization of flexible combined ro

关键词： Networked control systems

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Exploiting symmetries in active set enumeration for constrained linear-quadratic optimal control

arXiv

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arXiv 2023年

作者： Mitze, Ruth Kvasnica, Michal Mönnigmann, Martin Automatic Control and Systems Theory Ruhr-Universität Bochum Bochum Germany Institute of Information Engineering Automation and Mathematics Faculty of Chemical and Food Technology Slovak University of Technology Bratislava Slovakia

This paper studies symmetric constrained linear-quadratic optimal control problems and their parametric solutions. The parametric solution of such a problem is a piecewise-affine feedback law that can be equivalently expressed as a set of active sets. We show symmetries of the optimal control problem entail symmetries of the active sets, which can be used to simplify finding the set of active sets considerably. Specifically, we improve a recently proposed method for the dynamic-programming-based enumeration of all active sets. The achieved reduction of the computational effort is illustrated with an example. © 2023, CC BY-NC-ND.

关键词： Quadratic programming

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Constrained Optimization with Decision-Dependent Distributions

引用

IEEE Transactions on Automatic control 2025年

作者： Wang, Zifan Liu, Changxin Parisini, Thomas Zavlanos, Michael M. Johansson, Karl H. KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Sweden Digital Futures StockholmSE-10044 Sweden East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom Aalborg University Department of Electronic Systems Denmark University of Trieste Department of Engineering and Architecture Italy Duke University Department of Mechanical Engineering and Materials Science DurhamNC United States

In this paper we deal with stochastic optimization problems where the data distributions change in response to the decision variables. Traditionally, the study of optimization problems with decision-dependent distributions has assumed either the absence of constraints or fixed constraints. This work considers a more general setting where the constraints can also dynamically adjust in response to changes in the decision variables. Specifically, we consider linear constraints and analyze the effect of decision-dependent distributions in both the objective function and constraints. Firstly, we establish a sufficient condition for the existence of a constrained equilibrium point, at which the distributions remain invariant under retraining. Moreover, we propose and analyze two algorithms: repeated constrained optimization and repeated dual ascent. For each algorithm, we provide sufficient conditions for convergence to the constrained equilibrium point. Furthermore, we explore the relationship between the equilibrium point and the optimal point for the constrained decision-dependent optimization problem. Notably, our results encompass previous findings as special cases when the constraints remain fixed. To show the effectiveness of our theoretical analysis, we provide numerical experiments on both a market problem and a dynamic pricing problem for parking based on real-world data. © 1963-2012 IEEE.

关键词： Optimization algorithms

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Parametric S-Parameter Prediction Using Deep Learning

Parametric S-Parameter Prediction Using Deep Learning

引用

IEEE Topical Meeting on Electrical Performance of Electronic Packaging (EPEPS)

作者： Vinayak Bansal Lihong Feng Valentin de la Rubia Peter Benner Department of Chemical Engineering Indian Institute of Technology Delhi India Computational Methods in Systems and Control Theory Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany Departamento de Mateádtica Aplicada a las TIC Universidad Politécnica de Madrid Madrid Spain

We construct a neural network model of S-parameters, from which the S-parameters can be quickly pre-dicted. Numerical tests on a filter model show that the proposed method accurately predicts the S-parameters with mul... 详细信息

ISBN: (数字)9798350351231

ISBN: (纸本)9798350351248

关键词： Deep learning Accuracy Transfer functions Predictive models Stability analysis Scattering parameters Numerical models Feedforward neural networks Numerical stability Electronics packaging

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Open access resources to support the first course in feedback, dynamics and control

引用

IFAC-PapersOnLine 2022年第17期55卷 1-6页

作者： A. Serbezov K. Zakova A. Visioli J.A. Rossiter B. Douglas J. Hedengren Department of Chemical Engineering Rose-Hulman Institute of Technology Terre Haute IN USA Faculty of Electrical Engineering and Information Technology Slovak University of Technology in Bratislava Slovakia Department of Mechanical and Industrial Engineering University of Brescia Italy Department of Automatic Control and Systems Engineering University of Sheffield Sheffield S1 3JD UK Engineering Media LLC Kirkland WA 98034 USA Department of Chemical Engineering Brigham Young University Provo UT 84602 USA

The Technical Committees for control Education of IFAC and IEEE have started a collaborative initiative to collect, curate, and disseminate high quality freely available resources that support instruction of the first control course for undergraduate students in engineering and applied sciences programs. An initial survey with limited distribution provided 64 resources that cover all topics in a typical introductory control curriculum. This paper gives an initial glimpse of the resources collected thus far. It also suggests possible ways for categorization and dissemination.

关键词： engineering education Automatic control curriculum E-learning in control engineering Open access resources in automatic control Automatic control virtual learning environments

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A Unified Framework for Multi-Agent Formation with a Nonrepetitive Leader Trajectory: Adaptive control and Iterative Learning control

TechRxiv

引用

TechRxiv 2023年

作者： Jiang, Wei Chen, Yiyang Chen, Hongtian De Schutter, Bart Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland School of Mechanical and Electrical Engineering Soochow University Suzhou215137 China Department of Chemical and Materials Engineering University of Alberta EdmontonT6G 1H9 Canada Delft Center for Systems and Control Delft University of Technology Delft Netherlands

Formation tracking (FT) control aims at handling cooperative tasks in multi-agent systems (MASs) to achieve desired performance. In these tasks, the leader’s input is generally non-zero and unknown to all followers, i.e., its trajectory can be arbitrary and non-repetitive. In this paper, the additive property of linear systems is exploited to develop a unified framework for FT tasks of MASs, consisting of adaptive observer-based control (AOC) and iterative learning control (ILC). This framework employs an AOC controller to guarantee a fixed-shape formation between the leader and followers during the whole process, which reserves the initial condition for ILC. Also, it employs ILC to improve the FT performance of certain repetitive tasks (followers rotating around the leader) over the trials. This gives rise to a fully distributed algorithm working for a directed communication graph containing a spanning tree without requiring any eigenvalue information from the Laplacian matrix of the graph, which enables its application to MASs with a large number of agents. Comparisons are provided via a numerical simulation to show that the proposed combined AOC-ILC algorithm has less FT error than pure AOC (without ILC), which validates the feasibility and efficacy of this algorithm. © 2023, CC BY.

关键词： Multi agent systems

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