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arXiv 2023年

作者： Mehdifar, Farhad Lindemann, Lars Bechlioulis, Charalampos P. Dimarogonas, Dimos V. The Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Thomas Lord Department of Computer Science University of Southern California Los AngelesCA United States The Division of Systems and Control The Department of Electrical and Computer Engineering University of Patras Patra Greece

This paper proposes a novel control framework for handling (potentially coupled) multiple time-varying output constraints for uncertain nonlinear systems. First, it is shown that the satisfaction of multiple output constraints boils down to ensuring the positiveness of a scalar variable (the signed distance from the time-varying output-constrained set’s boundary). Next, a single funnel constraint is designed properly, whose satisfaction ensures convergence to and invariance of the time-varying output-constrained set. Then a robust and low-complexity funnel-based feedback controller is designed employing the prescribed performance control method. Finally, a simulation example clarifies and verifies the proposed approach. Copyright © 2023, The Authors. All rights reserved.

关键词： Time varying control systems

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Learning For Predictive control: A Dual Gaussian Process Approach

arXiv

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arXiv 2022年

作者： Liu, Yuhan Wang, Pengyu Tóth, Roland Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands Flight Dynamics and Control Laboratory Department of Aerospace Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

An important issue in model-based control design is that an accurate dynamic model of the system is generally nonlinear, complex, and costly to obtain. This limits achievable control performance in practice. Gaussian process (GP) based estimation of system models is an effective tool to learn unknown dynamics directly from input/output data. However, conventional GP-based control methods often ignore the computational cost associated with accumulating data during the operation of the system and how to handle forgetting in continuous adaption. In this paper, we present a novel Dual Gaussian Process (DGP) based model predictive control (MPC) strategy that enables efficient use of online learning based predictive control without the danger of catastrophic forgetting. The bio-inspired DGP structure is a combination of a long-term GP and a short-term GP, where the long-term GP is used to keep the learned knowledge in memory and the short-term GP is employed to rapidly compensate unknown dynamics during online operation. Furthermore, a novel recursive online update strategy for the short-term GP is proposed to successively improve the learnt model during online operation. Effectiveness of the proposed strategy is demonstrated via numerical simulations. © 2022, CC BY-NC-SA.

关键词： Model predictive control

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A Consensus-Based Generalized Multi-Population Aggregative Game with Application to Charging Coordination of Electric Vehicles

arXiv

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arXiv 2023年

作者： Ghavami, Mahsa Bakhshayesh, Babak Ghaffarzadeh Haeri, Mohammad Como, Giacomo Kebriaei, Hamed The Advanced Control Systems Lab Department of Electrical Engineering Sharif University of Technology Tehran Iran School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran The Department of Mathematical Sciences Politecnico di Torino Torino Italy

This paper introduces a consensus-based generalized multi-population aggregative game coordination approach with application to electric vehicles charging under transmission line constraints. The algorithm enables agents to seek an equilibrium solution while considering the limited infrastructure capacities that impose coupling constraints among the users. The Nash-seeking algorithm consists of two interrelated iterations. In the upper layer, population coordinators collaborate for a distributed estimation of the coupling aggregate term in the agents’ cost function and the associated Lagrange multiplier of the coupling constraint, transmitting the latest updated values to their population’s agents. In the lower layer, each agent updates its best response based on the most recent information received and communicates it back to its population coordinator. For the case when the agents’ best response mappings are non-expansive, we prove the algorithm’s convergence to the generalized Nash equilibrium point of the game. Simulation results demonstrate the algorithm’s effectiveness in achieving equilibrium in the presence of a coupling constraint. © 2023, CC BY-NC-ND.

关键词： Electric vehicles

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Flexible control of the Interline DC Power Flow controller Using Virtual Capacitor in VSC-HVDC Grids

Flexible Control of the Interline DC Power Flow Controller U...

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Power Electronic and Drive systems and Technologies Conference (PEDSTC)

作者： Mirhamed Pourmirasghariyan Mirsajed Pourmirasghariyan Seyed Saeid Heidari Yazdi Seyyed Yousef Mousazadeh Mousavi Kumars Rouzbehi G. B. Gharehpetian Departement of Electrical and Computer Engineering Amirkabir University of Technology Tehran Iran School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Electrical & Computer Engineering School of Engineering and Digital Science Nazarbayev University Nur-Sultan Kazakhstan Faculty of Engineering and Technology University of Mazandaran Sari Iran Department of Systems and Automatic Control Engineering University of Seville Seville Spain

The performance of Interline DC Power Flow controllers (IDC-PFCs) in Voltage Source Converters (VSC)-based High Voltage Direct Current (HVDC) grids, can be affected due to different issues. The current limitation of HVDC lines, the voltage limitation of HVDC buses, and DC voltage of the IDC-PFC intermediary capacitor prevent effective and efficient operation of IDC-PFCs. In this paper, it is shown that this issue can be overcome by using a virtual capacitor in parallel with the IDC-PFC intermediary capacitor. Also, an energy control-based scheme is proposed for the operation of IDC-PFCs in VSC-HVDC grid. The benefits of using the virtual capacitor are: widening the operational area of the IDC-PFCs for the determined duty cycle and injecting more voltage in series to the interconnected HVDC line to control the related HVDC line current. The proposed solution is successfully evaluated on a CIGRE three-terminal VSC-HVDC grid which is modeled by linearized space-state equations.

关键词： Fluctuations HVDC transmission Capacitors High-voltage techniques Boosting Mathematical models Power conversion

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Drone-assisted Remote Gas Sensing in Volcanic Scenarios

Drone-assisted Remote Gas Sensing in Volcanic Scenarios

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International Conference on Unmanned Aircraft systems (ICUAS)

作者： Dario Calogero Guastella Giuseppe Sutera Gaetano Giudice Domenico Longo Giovanni Muscato Department of Electrical Electronic and Computer Engineering Robotic Systems Group University of Catania Italy Istituto Nazionale di Geofisica e Vulcanologia Catania Italy Department of Agriculture Food and Environment University of Catania Italy

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

The study of volcanoes is of primary importance due to the great impact that eruptions can have on the economy, safety, air traffic, and several other human activities. A main indicator of volcanic activities, crucial to predicting events, is based on the analysis of gases emitted from a volcano's vents. Acquisition of gas samples is, however, challenging due to extreme working conditions and safety precautions. A further challenge is also locating and relocating volcanic gas measurement devices, as plume density and direction can quickly change. In this paper, we propose to use an open-path gas analyzer mounted on a Pan-Tilt Unit that tracks a multirotor equipped with suitable mirrors. This allows to autonomously perform gas measurement missions in volcanic environments via Tunable Diode Laser Absorption Spectroscopy. The great advantage brought by such a multirotor-based approach is the rapid and accurate measurement of gases, which makes monitoring and prediction activities safer, timely, and cost-effective. The effectiveness and accuracy of the proposed approach has been tested in a real application.

关键词： Gases Spectroscopy Accuracy Volcanic activity Vents Urban areas Pollution measurement

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On constraints in first-order optimization: a view from non-smooth dynamical systems

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2022年第1期23卷 11681-11727页

作者： Michael Muehlebach Michael I. Jordan Learning and Dynamical Systems Group Max Planck Institute for Intelligent Systems Tübingen Germany Department of Electrical Engineering and Computer Sciences Department of Statistics University of California Berkeley CA

We introduce a class of first-order methods for smooth constrained optimization that are based on an analogy to non-smooth dynamical systems. Two distinctive features of our approach are that (i) projections or optimizations over the entire feasible set are avoided, in stark contrast to projected gradient methods or the Frank-Wolfe method, and (ii) iterates are allowed to become infeasible, which differs from active set or feasible direction methods, where the descent motion stops as soon as a new constraint is encountered. The resulting algorithmic procedure is simple to implement even when constraints are nonlinear, and is suitable for large-scale constrained optimization problems in which the feasible set fails to have a simple structure. The key underlying idea is that constraints are expressed in terms of velocities instead of positions, which has the algorithmic consequence that optimizations over feasible sets at each iteration are replaced with optimizations over local, sparse convex approximations. In particular, this means that at each iteration only constraints that are violated are taken into account. The result is a simplified suite of algorithms and an expanded range of possible applications in machine learning.

关键词： convex optimization nonconvex optimization constrained optimization non-smooth dynamical systems gradient-based optimization convergence rate analysis

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Skeleton-Based Multi-Stream Adaptive Graph Convolutional Network for Indoor Scene Action Recognition

Skeleton-Based Multi-Stream Adaptive Graph Convolutional Net...

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2023 China Automation Congress, CAC 2023

作者： Li, Jiazhuo Chen, Luefeng Li, Min Wu, Min Pedrycz, Witold Hirota, Kaoru The School of Automation The Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Wuhan430074 China The Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada The Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland The Department of Computer Engineering Istinye University Sariyer Istanbul34396 Turkey The Tokyo Institute of Technology Tokyo226-8502 Japan

ISBN: (纸本)9798350303759

With the rapid advances in computer vision, human action recognition has gradually received attention, but the current methods still exhibit some problems in indoor environments. The human skeleton, as the framework of human motion, contains high-quality actional feature information, and the skeleton-based action recognition method effectively avoid the interference of interior background noise and has advantages in indoor action recognition. The outstanding effect of graph convolutional networks on graph structure data processing has led to its rapid development and wide application in skeleton-based action recognition. Second-order skeletal information also contains a large number of actional features but is not effectively utilized. The artificial predefined topology of the human skeleton map has limitations, and cannot reflect the interaction between limbs. To solve the above problems, this article designs an adaptive weighted multi-stream graph convolutional network (AM-GCN) based on skeletal information, using an attention mechanism to enhance the network's ability to extract actional features, and an adaptive layer to make the construction graph more flexible, incorporating second-order skeletal features through a dual-stream architecture. In this article, the NTU-RGB+D dataset has been used for the experiments, the results show that the method in this article has good results. © 2023 IEEE.

关键词： Convolution

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Long-Range Low-Power Wireless Sensor to Monitor Temperature and Humidity of Dairy Calves

Long-Range Low-Power Wireless Sensor to Monitor Temperature ...

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IEEE Biomedical Circuits and systems (BIOCAS)

作者： Jia Xue Kow Johan Osorio Estevez Ana Flavia Souza Lima Yuezhong Xu Dong Sam Ha Bradley Department of Electrical and Computer Engineering Multifunctional Integrated Circuits and Systems (MICS) Group Virginia Tech Blacksburg Virginia USA School of Animal Sciences Virginia Tech Blacksburg Virginia USA

ISBN: (数字)9798350354959

ISBN: (纸本)9798350354966

This paper presents a low-power wireless sensor for monitoring the temperature and humidity of dairy calves over extended distances. The wireless sensor consists of a microcontroller with a LoRa ® radio, temperature and humidity sensors, and a power controller. The sensor samples data once every minute and remains in sleep mode most of the time. To save power during sleep, the power controller shuts off power to the sensor. The sensor was prototyped, and its operation was verified in a field test with three calves. The sensor is active for data sensing, processing, and transmission for about 458 ms in each cycle and dissipates an average of 16.13 mW during the active period. It is estimated that the sensor would operate for approximately 33 months using a CR2477 coin cell battery with a sampling rate of one data point per minute.

关键词： Temperature sensors Wireless communication Temperature measurement Wireless sensor networks Temperature distribution Humidity Sensors Batteries Biomedical monitoring Monitoring

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Navigation in Time-Varying Densities: An Operator Theoretic Approach

Navigation in Time-Varying Densities: An Operator Theoretic ...

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European control Conference (ECC)

作者： Shankar A. Deka Umesh Vaidya Dimos V. Dimarogonas Department of Electrical Engineering Computer Science Decision and Control Systems division KTH Royal Institute of Technology Stockholm Sweden Department of Mechanical Engineering Clemson University Clemson South Carolina USA

This paper considers the problem of optimizing robot navigation with respect to a time-varying objective encoded into a navigation density function. We are interested in designing state feedback control laws that lead to an almost everywhere stabilization of the closed-loop system to an equilibrium point while navigating a region optimally and safely (that is, the transient leading to the final equilibrium point is optimal and satisfies safety constraints). Though this problem has been studied in literature within many different communities, it still remains a challenging non-convex control problem. In our approach, under certain assumptions on the time-varying navigation density, we use Koopman and Perron-Frobenius Operator theoretic tools to transform the problem into a convex one in infinite dimensional decision variables. In particular, the cost function and the safety constraints in the transformed formulation become linear in these functional variables. Finally, we present some numerical examples to illustrate our approach, as well as discuss the current limitations and future extensions of our framework to accommodate a wider range of robotics applications.

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Reinforcement Learning-based Output Structured Feedback for Distributed Multi-Area Power System Frequency control

Reinforcement Learning-based Output Structured Feedback for ...

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American control Conference (ACC)

作者： Kyung-Bin Kwon Sayak Mukherjee Hao Zhu Thanh Long Vu Department of Electrical and Computer Engineering The University of Texas at Austin Austin TX USA Optimization and Control Group Pacific Northwest National Laboratory Richland WA USA

Load frequency control (LFC) is a key factor to maintain stable frequency in multi-area power systems. As modern power systems evolve from a centralized to distributed paradigm, LFC needs to consider the peer-to-peer (P2P) based scheme that considers limited information from the information-exchange graph for the generator control of each interconnected area. This paper aims to solve a data-driven constrained LQR problem with mean-variance risk constraints and output structured feedback, and applies this framework to solve the LFC problem in multi-area power systems. By reformulating the constrained optimization problem into a minimax problem, the stochastic gradient descent max-oracle (SGDmax) algorithm with zero-order policy gradient (ZOPG) is adopted to find the optimal feedback gain from the learning, while guaranteeing convergence. In addition, to improve the adaptation of the proposed learning method to new or varying models, we construct an emulator grid that approximates the dynamics of a physical grid and performs training based on this model. Once the feedback gain is obtained from the emulator grid, it is applied to the physical grid with a robustness test to check whether the controller from the approximated emulator applies to the actual system. Numerical tests show that the obtained feedback controller can successfully control the frequency of each area, while mitigating the uncertainty from the loads, with reliable robustness that ensures the adaptability of the obtained feedback gain to the actual physical grid.

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