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A perspective on Attitude control Issues and Techniques

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arXiv 2022年

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

This paper reviews the attitude control problems for rigid-body systems, starting from the attitude representation for rigid body kinematics. Highly redundant rotation matrix defines the attitude orientation globally and uniquely by 9 parameters, which is the most fundamental one, without any singularities;minimum 3-parameter Euler angles or (modified) Rodrigues parameters define the attitude orientation neither globally nor uniquely, but the former exhibits kinematical singularity and Gimbal lock, while the latter two exhibit geometrical singularity;once-redundant axis-angle or unit quaternion globally define the attitude rotation but not uniquely using 4 parameters, but the former is not appropriate to define very small or very large rotations, while the latter shows unwinding phenomenon despite of the reduced computation burden. In addition, we explore the relationships among those attitude representations, including the connections among Gimbal lock, unwinding phenomenon and a nowhere dense set of zero Lebesgue measure. Based on attitude representations, we analyze different attitude control laws, almost global control and global attitude control, nominal and general robustness, as well as the technique *** Codes 70E50 Copyright © 2022, The Authors. All rights reserved.

关键词： Attitude control

Fuzzy PID controller Optimized by Improved Gravitational Search Algorithm for Load Frequency control in Multi-area Power System

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IAENG International Journal of Computer Science

IAENG International Journal of Computer Science 2022年第1期49卷 125-139页

作者： Chen, Gonggui Qin, Feng Long, Hongyu Zeng, Xianjun Kang, Peng Zhang, Jinming Professor of Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China Graduate student of Chongqing University of Posts and Telecommunications Chongqing400065 China Professor level senior engineer of Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Chongqing400065 China Senior engineer of State Grid Chongqing Electric Power Company Chongqing400015 China Senior engineer of Economic and Technology Research Institute State Grid Chongqing Electric Power Company Chongqing401120 China

In this paper, an objective function considering performance index and an improved gravitational search algorithm (IGSA) are proposed to optimize the parameters of fuzzy PID (FPID) controller. IGSA is obtained by combining elite strategies and the mining capability of PSO algorithm. The IGSA FPID method which integrates IGSA and FPID controller is tested in a typical two-area non-reheat system, and system responses reflect the advantages of proposed objective function and IGSA. Additionally, the proposed IGSA FPID method still has excellent effects even on the two-area multi-source system with thermal power, hydropower and gas turbines. Meanwhile, the effect of high voltage direct current (HVDC) links in suppressing fluctuations is verified. Due to the popularity of wind power, a novel three-area interconnected power system with nonlinearity connected wind turbine (WT) is designed. Under the control of IGSA FPID, the good stability and dynamic performance of mentioned three-area system are verified. Therefore, FPID controller optimized by IGSA has excellent control effects, stability and universal applicability on the different power systems with multiple area and sources. © 2022. All Rights Reserved.

关键词： Wind turbines

Star-Convex Constrained Optimization for Visibility Planning with Application to Aerial Inspection

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arXiv 2022年

作者： Liu, Tianyu Wang, Qianhao Zhong, Xingguang Wang, Zhepei Xu, Chao Zhang, Fu Gao, Fei State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China Huzhou Institute of Zhejiang University Huzhou313000 China Department of Mechanical Engineering The University of Hong Kong Hong Kong

The visible capability is critical in many robot applications, such as inspection and surveillance, etc. Without the assurance of the visibility to targets, some tasks end up not being complete or even failing. In this paper, we propose a visibility guaranteed planner by star-convex constrained optimization. The visible space is modeled as star convex polytope (SCP) by nature and is generated by finding the visible points directly on point cloud. By exploiting the properties of the SCP, the visibility constraint is formulated for trajectory optimization. The trajectory is confined in the safe and visible flight corridor which consists of convex polytopes and SCPs. We further make a relaxation to the visibility constraints and transform the constrained trajectory optimization problem into an unconstrained one that can be reliably and efficiently solved. To validate the capability of the proposed planner, we present the practical application in site inspection. The experimental results show that the method is efficient, scalable, and visibility guaranteed, presenting the prospect of application to various other applications in the future. Copyright © 2022, The Authors. All rights reserved.

关键词： Constrained optimization

Efficient View Path Planning for Autonomous Implicit Reconstruction

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Efficient View Path Planning for Autonomous Implicit Reconst...

IEEE International Conference on Robotics and Automation (ICRA)

作者： Jing Zeng Yanxu Li Yunlong Ran Shuo Li Fei Gao Lincheng Li Shibo He Jiming Chen Qi Ye Zhejiang University Hangzhou China Fuxi AI Lab NetEase Hangzhou China State Key Laboratory of Industrial Control Technology and also the Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province College of Control Science and Engineering Zhejiang University China

Implicit neural representations have shown promising potential for 3D scene reconstruction. Recent work applies it to autonomous 3D reconstruction by learning information gain for view path planning. Effective as it is, the computation of the information gain is expensive, and compared with that using volumetric representations, collision checking using the implicit representation for a 3D point is much slower. In the paper, we propose to 1) leverage a neural network as an implicit function approximator for the information gain field and 2) combine the implicit fine-grained representation with coarse volumetric representations to improve efficiency. Further with the improved efficiency, we propose a novel informative path planning based on a graph-based planner. Our method demonstrates significant improvements in the reconstruction quality and planning efficiency compared with autonomous reconstructions with implicit and explicit representations. We deploy the method on a real UAV and the results show that our method can plan informative views and reconstruct a scene with high quality.

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Automatic Depth Optimization for Quantum Approximate Optimization Algorithm

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arXiv 2022年

作者： Pan, Yu Tong, Yifan Yang, Yi State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou310027 China Department of Mathematics and Statistics McGill University MontrealQCH3A 0B9 Canada

Quantum Approximate Optimization Algorithm (QAOA) is a hybrid algorithm whose control parameters are classically optimized. In addition to the variational parameters, the right choice of hyperparameter is crucial for improving the performance of any optimization model. control depth, or the number of variational parameters, is considered as the most important hyperparameter for QAOA. In this paper we investigate the control depth selection with an automatic algorithm based on proximal gradient descent. The performances of the automatic algorithm are demonstrated on 7-node and 10-node Max-Cut problems, which show that the control depth can be significantly reduced during the iteration while achieving an sufficient level of optimization accuracy. With theoretical convergence guarantee, the proposed algorithm can be used as an efficient tool for choosing the appropriate control depth as a replacement of random search or empirical rules. Moreover, the reduction of control depth will induce a significant reduction in the number of quantum gates in circuit, which improves the applicability of QAOA on Noisy Intermediate-scale Quantum (NISQ) devices. © 2022, CC BY.

关键词： Optimization

Robust optimization for quantum reinforcement learning control using partial observations

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Physical Review A 2022年第6期105卷 062443-062443页

作者： Chen Jiang Yu Pan Zheng-Guang Wu Qing Gao Daoyi Dong State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou 310027 China The School of Automation Science and Electrical Engineering and Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China The School of Engineering and Information Technology University of New South Wales Canberra Australian Capital Territory 2600 Australia

The current quantum reinforcement learning control models often assume that the quantum states are known a priori for control optimization. However, full observation of the quantum state is experimentally infeasible due to the exponential scaling of the number of required quantum measurements on the number of qubits. In this paper, we investigate a robust reinforcement learning method using partial observations to overcome this difficulty. This control scheme is compatible with near-term quantum devices, where the noise is prevalent and predetermining the dynamics of the quantum state is practically impossible. We show that this simplified control scheme can achieve similar or even better performance when compared to the conventional methods relying on full observation. We demonstrate the effectiveness of this scheme on examples of quantum state control and the quantum approximate optimization algorithm (QAOA). It has been shown that high-fidelity state control can be achieved even if the noise amplitude is at the same level as the control amplitude. Besides, an acceptable level of optimization accuracy can be achieved for a QAOA with a noisy control Hamiltonian. This robust control optimization model can be trained to compensate for the uncertainties in practical quantum computing.

关键词： Machine learning Quantum control Quantum engineering

Performance-Aware control of Modular Batteries For Fast Frequency Response

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arXiv 2025年

作者： He, Yutong Ruan, Guangchun Zhong, Haiwang State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Tsinghua University Beijing100084 China Lab for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States

Modular batteries can be aggregated to deliver frequency regulation services for power grids. Although utilizing the idle capacity of battery modules is financially attractive, it remains challenging to consider the heterogeneous module-level characteristics such as dynamic operational efficiencies and battery degradation. In addition, real-time decision making within seconds is required to enable fast frequency response. In order to address these issues, this paper proposes a performance-aware scheduling approach for battery modules to deliver fast frequency response (FFR) support. In particular, the conduction loss and switching loss of battery packs as well as converters are captured within a mix-integer quadratic constrained program (MIQCP). The cycle-based aging model identifies the aging cost of battery modules during frequent cycling by introducing the aging subgradient calculation and linearization. Case studies based on real-world battery data show that the proposed scheduling approach can effectively reduce power loss cost by nearly 28%-57% and battery aging cost by 4%-15% compared to conventional methods, which can also enhance the SoC balance. Copyright © 2025, The Authors. All rights reserved.

关键词： Battery management systems

A Reinforcement Learning-Based Detection Method for False Data Injection Attack in Distributed Smart Grid

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A Reinforcement Learning-Based Detection Method for False Da...

International Conference on Informative and cybernetics for Computational Social systems (ICCSS)

作者： Kuo Zhang Zheng-Guang Wu Institute of Cyber-Systems and Control Zhejiang University Hangzhou China The State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

ISBN: (纸本)9781665402460

False data injection attack(FDIA) is a traditional attack for the smart grid. There are many methods for the detection of the FDIA, but few of them can send the attack alarm successfully without an attack model. In this paper, we propose a reinforcement learning-based FDIA detection method for the distributed smart grid. The detection problem is formulated as a partially observable Markov decision process(POMDP) problem, and the observation of the POMDP can be obtained from the estimation of state and attack which come from the Kalman filter. By using the Sarsa algorithm, we can get a Q-table through online training. Finally, we use the IEEE-118 bus power system to evaluate the performance of our detector, and numerical results show the accurate response for the FDIA.

关键词： Training Learning systems Estimation Distributed databases Detectors Markov processes Smart grids

Intention-Aware Planner for Robust and Safe Aerial Tracking

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Intention-Aware Planner for Robust and Safe Aerial Tracking

IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Qiuyu Ren Huan Yu Jiajun Dai Zhi Zheng Jun Meng Li Xu Chao Xu Fei Gao Yanjun Cao The State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China The Robotics Institute of Zhejiang University Yuyao China Huzhou Institute Zhejiang University Huzhou China Huzhou Laboratory of Autonomous Intelligent Systems Huzhou China College of Electrical Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Autonomous target tracking with quadrotors has wide applications in many scenarios, such as cinematographic follow-up shooting or suspect chasing. Target motion prediction is necessary when designing the tracking planner. However, the widely used constant velocity or constant rotation assumption can not fully capture the dynamics of the target. The tracker may fail when the target happens to move aggressively, such as sudden turn or deceleration. In this paper, we propose an intention-aware planner by additionally considering the intention of the target to enhance safety and robustness in aerial tracking applications. Firstly, a designated intention prediction method is proposed, which combines a user-defined potential assessment function and a state observation function. A reachable region is generated to speci cally evaluate the turning intentions. Then we design an intention-driven hybrid A* method to predict the future possible positions for the target. Finally, an intention-aware optimization approach is designed to generate a spatial-temporal optimal trajectory, allowing the tracker to perceive unexpected situations from the target. Benchmark comparisons and real-world experiments are conducted to validate the performance of our method.

关键词： Target tracking Semantics Prediction methods Turning Prediction algorithms Robustness Safety Planning Trajectory optimization Quadrotors