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Distributed Cooperative Attitude Tracking for Multiple Flexible Spacecraft under a Directed Graph

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Distributed Cooperative Attitude Tracking for Multiple Flexi...

第三十八届中国控制会议

作者： Lei Wang Zhuoyue Song Housheng Su Yiqun Zou Xiafei Tang State Key Laboratory of Intelligent Control and Decision of Complex Systems and School of Automation Beijing Institute of Technology School of Automation Huazhong University of Science and Technology School of Automation Central South University School of Electrical and Information Engineering Changsha University of Science and Technology

This paper is concerned with the problem of cooperative attitude tracking and vibration reduction for multiple flexible spacecraft without modal variable measurement under a directed communication topology. Firstly, a distributed attitude synchronization control law for MRPs representation is proposed in the absence of external disturbances, where the modal information is obtained using a modal variable estimator. To deal with a more practical case that the spacecraft are affected by external disturbances, the control law is further modified by integrating a disturbance observer and a feedforward compensating scheme,in which the lumped disturbances including the external disturbances and errors of the estimated modal information are observed and compensated in finite time. Under this enhanced control scheme, the attitude tracking errors and vibration variables will converge to zero even in the presence of external disturbances, and the controller is continuous and chattering-free. Besides,compared with the existing results, the communication graph in this paper is assumed to be directed and contain a spanning tree with the virtual leader as the root node. A numerical example is illustrated to demonstrate the effectiveness of the proposed results.

关键词： Attitude tracking Multiple flexible spacecraft Directed graph External disturbance

Event-based model predictive control of discrete-time non-linear systems with external disturbances

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IET control Theory & Applications 2019年第1期13卷 27-35页

作者： Mengzhi Wang Jian Sun Jie Chen School of Automation Beijing Institute of Technology Beijing 100081 People's Republic of China Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing 100081 People's Republic of China

The event-based model predictive control (MPC) problem for discrete-time non-linear control systems with external disturbances is studied. Two triggering conditions are designed based on whether the system state is within the terminal region or not. The first one is triggered outside the terminal region when the difference between the actual system state trajectory and the corresponding optimal state trajectory violates a relative threshold, while the second one is triggered inside the terminal region if the difference between the actual system state trajectory and the predicted nominal state trajectory violates a desired relative threshold. The feasibility analysis and stability analysis are given in detail. Sufficient conditions for feasibility relating to the triggering threshold and disturbance bound are obtained. Sufficient conditions for stability of the event-based control system referring to system parameters, triggering threshold and disturbance bound are given. Besides, the upper bound of the state trajectory is obtained and a robust invariant set that the system state will enter in finite time is given. Finally, simulation examples are given to validate the effectiveness of the proposed method.

关键词： nonlinear control systems robust control stability predictive control control system analysis control system synthesis discrete time systems linear systems discrete-time nonlinear systems external disturbances event-based model predictive control problem discrete-time nonlinear control systems triggering conditions terminal region actual system state trajectory corresponding optimal state trajectory predicted nominal state trajectory desired relative threshold sufficient conditions triggering threshold disturbance event-based control system system parameters

A statistical learning approach to reactive power control in distribution systems

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arXiv 2019年

作者： Yang, Qiuling Sadeghi, Alireza Wang, Gang Giannakis, Georgios B. Sun, Jian State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States

Pronounced variability due to the growth of renewable energy sources, flexible loads, and distributed generation is challenging residential distribution systems. This context, motivates well fast, efficient, and robust reactive power control. Real-time optimal reactive power control is possible in theory by solving a non-convex optimization problem based on the exact model of distribution flow. However, lack of high-precision instrumentation and reliable communications, as well as the heavy computational burden of non-convex optimization solvers render computing and implementing the optimal control challenging in practice. Taking a statistical learning viewpoint, the input-output relationship between each grid state and the corresponding optimal reactive power control is parameterized in the present work by a deep neural network, whose unknown weights are learned offline by minimizing the power loss over a number of historical and simulated training pairs. In the inference phase, one just feeds the real-time state vector into the learned neural network to obtain the 'optimal' reactive power control with only several matrix-vector multiplications. The merits of this novel statistical learning approach are computational efficiency as well as robustness to random input perturbations. Numerical tests on a 47-bus distribution network using real data corroborate these practical merits. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

Two-timescale voltage control in distribution grids using deep reinforcement learning

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arXiv 2019年

作者： Yang, Qiuling Wang, Gang Sadeghi, Alireza Giannakis, Georgios B. Sun, Jian State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States

Modern distribution grids are currently being challenged by frequent and sizable voltage fluctuations, due mainly to the increasing deployment of electric vehicles and renewable generators. Existing approaches to maintaining bus voltage magnitudes within the desired region can cope with either traditional utility-owned devices (e.g., shunt capacitors), or contemporary smart inverters that come with distributed generation units (e.g., photovoltaic plants). The discrete on-off commitment of capacitor units is often configured on an hourly or daily basis, yet smart inverters can be controlled within milliseconds, thus challenging joint control of these two types of assets. In this context, a novel two-timescale voltage regulation scheme is developed for distribution grids by judiciously coupling data-driven with physicsbased optimization. On a faster timescale, say every second, the optimal setpoints of smart inverters are obtained by minimizing instantaneous bus voltage deviations from their nominal values, based on either the exact alternating current power flow model or a linear approximant of it;whereas, on the slower timescale (e.g., every hour), shunt capacitors are configured to minimize the longterm discounted voltage deviations using a deep reinforcement learning algorithm. Extensive numerical tests on a real-world 47-bus distribution network as well as the IEEE 123-bus test feeder using real data corroborate the effectiveness of the novel scheme. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

Optimal fusion estimation for stochastic systems with cross-correlated sensor noises

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Science China(Information Sciences) 2017年第12期60卷 57-70页

作者： Liping YAN Yuanqing XIA Mengyin FU School of Automation Key Laboratory of Intelligent Control and Decision of Complex SystemsBeijing Institute of Technology

This paper is concerned with the optimal fusion of sensors with cross-correlated sensor *** taking linear transformations to the measurements and the related parameters, new measurement models are established, where the sensor noises are decoupled. The centralized fusion with raw data, the centralized fusion with transformed data, and a distributed fusion estimation algorithm are introduced, which are shown to be equivalent to each other in estimation precision, and therefore are globally optimal in the sense of linear minimum mean square error(LMMSE). It is shown that the centralized fusion with transformed data needs lower communication requirements compared to the centralized fusion using raw data directly, and the distributed fusion algorithm has the best flexibility and robustness and proper communication requirements and computation complexity among the three algorithms(less communication and computation complexity compared to the existed distributed Kalman filtering fusion algorithms). An example is shown to illustrate the effectiveness of the proposed algorithms.

关键词： optimal estimation distributed fusion Kalman filter cross-correlated noises linear transformation

Distribution system state estimation: an overview of recent developments

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Frontiers of Information Technology & Electronic Engineering 2019年第1期20卷 4-17页

作者： Wang Gang Giannakis Georgios B Chen Jie Sun Jian Department of Electrical and Computer Engineering and Digital Technology Center University of Minnesota USA Minneapolis MN 55455 School of Automation Beijing Institute of Technology Beijing 100081 Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing 100081

In the envisioned smart grid, high penetration of uncertain renewables, unpredictable participation of (industrial) customers, and purposeful manipulation of smart meter readings, all highlight the need for accurate, fast, and robust power system state estimation (PSSE). Nonetheless, most real-time data available in the current and upcoming transmission/distribution systems are nonlinear in power system states (i.e., nodal voltage phasors). Scalable approaches to dealing with PSSE tasks undergo a paradigm shift toward addressing the unique modeling and computational challenges associated with those nonlinear measurements. In this study, we provide a contemporary overview of PSSE and describe the current state of the art in the nonlinear weighted least-squares and least-absolutevalue PSSE. To benchmark the performance of unbiased estimators, the Cramer-Rao lower bound is developed. Accounting for cyber attacks, new corruption models are introduced, and robust PSSE approaches are outlined as well. Finally, distribution system state estimation is discussed along with its current challenges. Simulation tests corroborate the effectiveness of the developed algorithms as well as the practical merits of the theory.

关键词： state estimation Cramer-Rao bound Feasible point pursuit Semidefinite relaxation Proximal linear algorithm Composite optimization Cyber attack Bad data detection

Dynamical primal-dual accelerated method with applications to network optimization

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arXiv 2019年

作者： Zeng, Xianlin Lei, Jinlong Chen, Jie Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Department of control science and engineering Tongji University Shanghai China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai China

This paper develops a continuous-time primal-dual accelerated method with an increasing damping coefficient for a class of convex optimization problems with affine equality constraints. We then analyze critical values for parameters in the proposed method and prove that the rate of convergence is O(t12 ) by choosing proper parameters. As far as we know, this is the first continuous-time primal-dual accelerated method that can obtain the optimal rate. Then this work applies the proposed method to two network optimization problems, a distributed optimization problem with consensus constraints and a distributed extended monotropic optimization problem, and obtains two variant distributed algorithms. Finally, numerical simulations are given to demonstrate the efficacy of the proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Continuous time systems

Distributed computation for solving the sylvester equation based on optimization

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arXiv 2019年

作者： Deng, Wen Zeng, Xianlin Hong, Yiguang School of Mathematical Sciences University of Chinese Academy of Sciences Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

This paper solves the Sylvester equation in the form of AX + XB = C in a distributed way, and proposes three distributed continuous-time algorithms for three cases. We start with the basic algorithm for solving a least squares solution of the equation, and then give a simplified algorithm for the case when there is an exact solution to the equation, followed by an algorithm with regularization case. Based on local information and appropriate communication among neighbor agents, we solve the distributed computation problem of the Sylvester equation from the optimization viewpoint, and we prove the convergence of proposed algorithms to an optimal solution in three different cases, with help of the convex optimization and semi-stability. Copyright © 2019, The Authors. All rights reserved.

关键词： Convex optimization

Distributed sub-optimal resource allocation via a projected form of singular perturbation

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arXiv 2019年

作者： Liang, Shu Zeng, Xianlin Chen, Guanpu Hong, Yiguang Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

Distributed optimization for resource allocation problems is investigated and a sub-optimal continuous-time algorithm is proposed. Our algorithm has lower order dynamics than others to reduce burdens of computation and communication, and is applicable to weight-balanced graphs. Moreover, it can deal with both local constraints and coupled inequality constraints, and remove the requirement of twice differentiability of the cost function in comparison with the existing sub-optimal algorithm. However, this algorithm is not easy to be analyzed since it involves singular perturbation type dynamics with projected non-differentiable righthand side. We overcome the encountered difficulties and obtain results including the existence of an equilibrium of the algorithm, the sub-optimality in the sense that the solution approaches to the optimal solution as an adjustable parameter tends to zero, and the convergence of the algorithm. Copyright © 2019, The Authors. All rights reserved.

关键词： Resource allocation