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A two-stage optimization model for floating offshore wind farm layout and control

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Renewable Energy 2025年 253卷

作者： Siyu Tao Jisheng Yang Gang Zheng Ruiyang He Andrés E. Feijóo-Lorenzo College of Automation Engineering Nanjing University of Aeronautics and Astronautics No. 29 Jiangjun Road Nanjing 211106 China Department of Building Environment and Energy Engineering Hong Kong Polytechnic University No. 102 Pokfulam Road Pok Fu Lam Hong Kong 999077 China State Key Laboratory of Power System Operation and Control Tsinghua University Beijing 100084 China State Key Laboratory of Ocean Engineering Shanghai Jiao Tong University Shanghai 200240 China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing 102206 China Monash Biomedical Imaging Monash University Clayton Victoria 3800 Australia School of Engineering Cardiff University Cardiff CF243AA UK Department of Electrical Engineering University of Vigo Vigo 36310 Spain

The effective planning of wind turbine (WT) positions in a wind farm (WF) plays a crucial role in capturing wind energy and increasing power generation. control optimization of WT attitudes can also help reduce wake effects and increase power generation, thereby maximizing the annual electricity production (AEP) of a WF. In this paper, a two-stage optimization model is proposed for planning the stationary layout of floating offshore wind farms (FOWFs), as well as the positions and attitudes of the floating offshore wind turbines (FOWTs) via optimal control. In the first stage, a competitive particle swarm optimizer (CPSO) is employed to generate multiple FOWF layouts. In the second stage, the genetic algorithm (GA) is utilized to optimize the yaw angles and axial induction factors of the FOWTs for operation control. Unlike the existing approaches that address WT layout design and operation control sequentially and separately, the proposed method integrates these aspects to simplify the FOWTs operation and to improve their performances. Additionally, taking the movable characteristics of the FOWTs into consideration, the proposed model incorporates the positional movement of the FOWTs under varying wind conditions, making the power calculations more realistic. Case studies demonstrate that compared with the traditional layout-only optimization, the proposed two-stage optimization model can help increase the AEP of the FOWFs by 4.72 %.

关键词： Genetic algorithms

Learning of long-horizon sparse-reward robotic manipulator tasks with base controllers

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arXiv 2020年

作者： Wang, Guangming Xin, Minjian Wu, Wenhua Liu, Zhe Wang, Hesheng Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China Department of Computer Science and Engineering University of California San Diego La Jolla CA92093 United States Department of Computer Science and Technology University of Cambridge CambridgeCB2 1TN United Kingdom

Deep Reinforcement Learning (DRL) enables robots to perform some intelligent tasks end-to-end. However, there are still many challenges for long-horizon sparse-reward robotic manipulator tasks. On the one hand, a sparse-reward setting causes exploration inefficient. On the other hand, exploration using physical robots is of high cost and unsafe. In this paper, we propose a method of learning long-horizon sparse-reward tasks utilizing one or more existing traditional controllers named base controllers in this paper. Built upon Deep Deterministic Policy Gradients (DDPG), our algorithm incorporates the existing base controllers into stages of exploration, value learning, and policy update. Furthermore, we present a straightforward way of synthesizing different base controllers to integrate their strengths. Through experiments ranging from stacking blocks to cups, it is demonstrated that the learned state-based or image-based policies steadily outperform base controllers. Compared to previous works of learning from demonstrations, our method improves sample efficiency by orders of magnitude and improves the performance. Overall, our method bears the potential of leveraging existing industrial robot manipulation systems to build more flexible and intelligent controllers. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

Real-time trajectory generation for quadrotors using b-spline based non-uniform kinodynamic search

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arXiv 2019年

作者： Tang, Lvbang Wang, Hesheng Li, Peng Wang, Yong Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Beijing Institute of Control Engineering

In this paper, we propose a time-efficient approach to generate safe, smooth and dynamically feasible trajectories for quadrotors in the obstacle-cluttered environment. By using the uniform B-spline to represent trajectories, we transform the trajectory planning to a graph-search problem of B-spline control points in discretized space. Highly strict convex hull property of B-spline is derived to guarantee the dynamical feasibility of the entire trajectory. A novel non-uniform kinodynamic search strategy is adopted, and the step length is dynamically adjusted during the search process according to the Euclidean signed distance field (ESDF), making the trajectory achieve reasonable time-allocation and be away from obstacles. Nonstatic initial and goal states are allowed, therefore it can be used for online local replanning as well as global planning. Extensive simulation and hardware experiments show that our method achieves higher performance compared with the state-of-the-art method. Copyright © 2019, The Authors. All rights reserved.

关键词： Trajectories

State of charge estimation of parallel connected battery cells via descriptor system theory

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arXiv 2020年

作者： Zhang, Dong Couto, Luis D. Benjamin, Sebastien Zeng, Wente Coutinho, Daniel F. Moura, Scott J. Department of Civil and Environmental Engineering University of California BerkeleyCA United States Department of Control Engineering and System Analysis Université Libre de Bruxelles BrusselsB-1050 Belgium Saft Batteries Total S.A. Total S.A. Department of Automation and Systems UFSC PO BOX 476 88040-900 Florianópolis SC Brazil

— This manuscript presents an algorithm for individual Lithium-ion (Li-ion) battery cell state of charge (SOC) estimation when multiple cells are connected in parallel, using only terminal voltage and total current measurements. For battery packs consisting of thousands of cells, it is desirable to estimate individual SOCs by only monitoring the total current in order to reduce sensing cost. Mathematically, series connected cells yield dynamics given by ordinary differential equations under classical full voltage sensing. In contrast, parallel connected cells are evidently more challenging because the dynamics are governed by a nonlinear descriptor system, including differential equations and algebraic equations arising from voltage and current balance across cells. An observer with linear output error injection is formulated, where the individual cell SOCs and local currents are locally observable from the total current and voltage measurements. The asymptotic convergence of differential and algebraic states is established by considering local Lipschitz continuity property of system nonlinearities. Simulation results on LiNiMnCoO2/Graphite (NMC) cells illustrate convergence for SOCs, local currents, and terminal voltage. Copyright © 2020, The Authors. All rights reserved.

关键词： Nonlinear equations

A vision-based path planning and following system for a miniature robotic fish

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A vision-based path planning and following system for a mini...

2019 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2019

作者： Lu, Yue Chen, Xingyu Wu, Zhengxing Yan, Yan Yu, Junzhi State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory for Turbulence and Complex System Department of Mechanics and Engineering Science BIC-ESAT College of Engineering Peking University Beijing100871 China

ISBN: (纸本)9781728137261

Most existing path following algorithms for middle- or large-sized underwater vehicles minimize yaw difference of the control object. Breaking the canonical manner, this paper presents a vision-based path planning and following system for miniature robotic fish. As for path planning, the proposed system finds a gird path based on the AStar algorithm and selects a series of target points in the grid path. By connecting these points in order, a curve path can be planned. In terms of path following, the proposed system forgoes to give a specific yaw and only uses a global camera. It obtains coordinates of the robotic fish in real time by KCF tracking algorithm to calculate an approximate motion vector. A PID controller is employed to drive the difference angle between the motion vector and the target vector (the vector from the robotic fish to the target point) to zero. By swimming to target points one by one, the robotic fish can move to the terminus following the planned path. Finally, aquatic experiments are conducted to verify the effectiveness of the vision-based path planning and following system. The results reveal that the developed system can generate a safe and smooth path, and the robotic fish can follow the path with acceptable deviation. © 2018 IEEE.

关键词： Motion planning

Opacity of Nondeterministic Transition systems: A (Bi)Simulation Relation Approach

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Opacity of Nondeterministic Transition Systems: A (Bi)Simula...

作者： Zhang, Kuize Yin, Xiang Zamani, Majid ACCESS Linnaeus Center School of Electrical Engineering KTH Royal Institute of Technology Stockholm10044 Sweden Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai200240 China Computer Science Department University of Colorado Boulder BoulderCO80309 United States

In this paper, we propose several opacity-preserving (bi)simulation relations for nondeterministic transition systems (NTSs) in terms of initial-state opacity, current-state opacity, K-step opacity, and infinite-step opacity. We also show how one can leverage quotient constructions to compute such relations. As a result, although the opacity verification problem for infinite NTSs is generally undecidable, if one can find such an opacity-preserving relation from an infinite NTS to a finite one, the (lack of) opacity of the infinite NTS can be easily verified over the finite one, which is decidable. © 1963-2012 IEEE.

关键词： Opacity

Inverse-system method for identification of damping-rate functions in non-Markovian quantum systems

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Physical Review A 2020年第4期102卷 042227-042227页

作者： Shibei Xue Lingyu Tan Rebing Wu Min Jiang Ian R. Petersen Department of Automation Shanghai Jiao Tong University Shanghai 200240 People's Republic of China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 People's Republic of China Department of Automation Tsinghua University Beijing 100084 People's Republic of China Center for Quantum Information Science and Technology TNList Beijing 100084 People's Republic of China School of Electronic and Information Engineering Soochow University Suzhou 215006 People's Republic of China Research School of Electrical Energy and Materials Engineering The Australian National University Canberra 0200 Australia

Identification of complicated quantum environments lies in the core of quantum engineering, which systematically constructs an environment model with the aim of accurate control of quantum systems. In this paper, we present an inverse-system method to identify damping rate functions, which describe non-Markovian environments in time-convolution-less master equations. To access information on the environment, we couple a finite-level quantum system to the environment and measure time traces of local observables of the system. By using sufficient measurement results, an algorithm is designed, which can simultaneously estimate multiple damping rate functions for different dissipative channels. Further, we show that identifiability for the damping rate functions corresponds to the invertibility of the system and a necessary condition for identifiability is also given. The effectiveness of our method is shown in examples of an atom and three-spin-chain non-Markovian systems.

关键词： Open quantum systems Quantum control

Reaching Non-Negative Edge Consensus of Networked Dynamical systems

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IEEE TRANSACTIONS ON CYBERNETICS 2018年第9期48卷 2712-2722页

作者： Wang, Xiao Ling Su, Housheng Chen, Michael Z. Q. Wang, Xiao Fan Chen, Guanrong College of Automation Nanjing University of Posts and Telecommunications Nanjing China School of Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan China School of Automation Nanjing University of Science and Technology Nanjing China Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Department of Electronic Engineering City University of Hong Kong Hong Kong

In this paper, the problem of non-negative edge consensus of undirected networked linear time-invariant systems is addressed by associating each edge of the network with a state variable, for which a distributed algorithm is constructed. Sufficient conditions referring only to the number of edges are derived for non-negative edge consensus of the networked systems. Subsequently, the linear programming method and a low-gain feedback technique are introduced to simplify the design of the feedback gain matrix for achieving the non-negative edge consensus. It is found that the low-gain feedback technique has a good effect on the non-negative edge consensus of the networked systems subject to input saturation. Numerical simulations are presented to verify the effectiveness of the theoretical results.

关键词： Distributed algorithm line graph low-gain feedback networked system non-negative edge consensus

An inverse-system method for identification of damping rate functions in non-Markovian quantum systems

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arXiv 2020年

作者： Xue, Shibei Tan, Lingyu Wu, Rebing Jiang, Min Petersen, Ian R. Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Department of Automation Tsinghua University Beijing100084 China Center for Quantum Information Science and Technology TNList Beijing100084 China School of Electronic and Information Engineering Soochow University Suzhou215006 China Research School of Electrical Energy and Materials Engineering Australian National University Canberra0200 Australia

Identification of complicated quantum environments lies in the core of quantum engineering, which systematically constructs an environment model with the aim of accurate control of quantum systems. In this paper, we present an inverse-system method to identify damping rate functions which describe non-Markovian environments in time-convolution-less master equations. To access information on the environment, we couple a finite-level quantum system to the environment and measure time traces of local observables of the system. By using sufficient measurement results, an algorithm is designed, which can simultaneously estimate multiple damping rate functions for different dissipative channels. Further, we show that identifiability for the damping rate functions corresponds to the invertibility of the system and a necessary condition for identifiability is also given. The effectiveness of our method is shown in examples of an atom and three-spin-chain non-Markovian systems. Copyright © 2020, The Authors. All rights reserved.

关键词： Quantum optics