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arXiv 2022年

作者： Liu, Wenjie Sun, Jian Wang, Gang Bullo, Francesco Chen, Jie The State Key Lab of Intelligent Control and Decision of Complex Systems The School of Automation Beijing Institute of Technology Beijing100081 China The Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China The Mechanical Engineering Department The Center of Control Dynamical Systems and Computation UC Santa BarbaraCA93106-5070 United States The Department of Control Science and Engineering Tongji University Shanghai201804 China The State Key Lab of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China

Self-triggered control, a well-documented technique for reducing the communication overhead while ensuring desired system performance, is gaining increasing popularity. However, existing methods for self-triggered control require explicit system models that are assumed perfectly known a priori. An end-to-end control paradigm known as data-driven control learns control laws directly from data, and offers a competing alternative to the routine system identification-then-control method. In this context, the present paper puts forth data-driven self-triggered control schemes for unknown linear systems using data collected offline. Specifically, for output feedback control systems, a data-driven model predictive control (MPC) scheme is proposed, which computes a sequence of control inputs while generating a predicted system trajectory. A data-driven self-triggering law is designed using the predicted trajectory, to determine the next triggering time once a new measurement becomes available. For state feedback control systems, instead of capitalizing on MPC to predict the trajectory, a data-fitting problem using the pre-collected input-state data is solved, whose solution is employed to construct the self-triggering mechanism. Both feasibility and stability are established for the proposed self-triggered controllers, which are validated using numerical examples. © 2022, CC BY.

关键词： Model predictive control

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Adaptive Dynamic Programming and Optimal Stabilization for Linear systems with Time-varying Uncertainty

Adaptive Dynamic Programming and Optimal Stabilization for L...

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Asian control Conference

作者： Meng Zhang Ming-Gang Gan Jie Chen Zhong-Ping Jiang State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China Department of Electrical and Computer Engineering New York University Brooklyn NY

ISBN: (纸本)9781509015740;9781509015733

This paper focuses on the optimal control of continuous-time linear time-varying uncertain systems with completely unknown internal dynamics and proposes a novel approach which leads to an optimal controller with guaranteed stability. A model-free algorithm of adaptive dynamic programming is employed to deal with the uncertainty of system parameters, yielding an optimal feedback controller for the system subject to a predefined cost. Then the stability of the system in time-varying uncertain situation which may undergo parameter changes or jumps is analyzed from the perspective of finite-time stability. On the basis of these results, a switching control strategy is presented to ensure the stability of the time-varying uncertain system with desired optimality properties in the long run. The effectiveness of the strategy is verified by simulations on a DC torque motor servo system.

关键词： Time-varying systems Stability analysis Symmetric matrices Asymptotic stability Uncertainty Optimal control

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Exponential input-to-state stability of impulsive stochastic fuzzy Cohen-Grossberg neural networks with distributed infinite transmission delays

Exponential input-to-state stability of impulsive stochastic...

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第三十八届中国控制会议

作者： Weisong Zhou Dan Shi Wei Zhu Key Lab of Intelligent Analysis and Decision on Complex Systems Chongqing University of Posts and Telecommunications Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing University of Posts and Telecommunications

In this paper, we study a class of impulsive stochastic fuzzy delayed Cohen-Grossberg neural networks with distributed infinite transmission delays. By Razumikhin technique and differential inequalities, some sufficient conditions ensuring the mean-square exponential input-to-state stability of our considered Cohen-Grossberg neural networks are obtained. These results can include constant delay and bounded distributed delay as its special cases, and generalize and improve some earlier publications. A numerical example is demonstrated to verify the theoretical results.

关键词： Impulsive Stochastic Infinite delays Cohen-Grossberg neural networks Exponential input-to-state stability

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Exponential input-to-state stability of stochastic fuzzy delayed Cohen-Grossberg neural networks under impulsive perturbations

Exponential input-to-state stability of stochastic fuzzy del...

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第37届中国控制会议

作者： Weisong Zhou Hao Zeng Donghai Wu Key Lab of Intelligent Analysis and Decision on Complex Systems Chongqing University of Posts and Telecommunications Key Laboratory of Industrial Internet of Things and Networked Control Ministry of EducationChongqing University of Posts and Telecommunications

In this paper,we study a class of stochastic fuzzy delayed Cohen-Grossberg neural networks under impulsive *** Razumikhin method and iteration technique,some sufficient conditions ensuring the mean-square exponential input-to-state stability of the designed delayed neural networks are *** results can generalize and improve some earlier publications.A numerical example is demonstrated to verify the theoretical results.

关键词： Impulsive Stochastic Cohen-Grossberg neural networks Exponential input-to-state stability

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Trot Pattern Generation for Quadruped Robot Based on the ZMP Stability Margin

Trot Pattern Generation for Quadruped Robot Based on the ZMP...

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2013 ICME International Conference on complex Medical Engineering(2013 ICME复合医学工程国际会议)

作者： Si Zhang Yi Liu Zhe Xu Junyao Gao Xingguang Duan Hui Li Zhangguo Yu Xuechao Chen Jing Li Huaxin Liu Xin LI Intelligent Robotics Institute Key Laboratory of Biomimetic Robots and Systems Ministry of Education State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Mechatronical Engineering Beijing Institute of Technology 5 Nandajie Zhongguancun Haidian Beijing 100081 China

ISBN: (纸本)9781467329705

Quadruped robot is expected to serve in complex conditions such as mountain road,grassland,etc.,therefore we desire a walking pattern generation that can guarantee both the speed and the stability of the quadruped *** order to solve this problem,this paper focuses on the stability for the tort pattern and proposes trot pattern generation for quadruped robot on the basis of ZMP stability *** foot trajectory is first designed based on the work space *** the ZMP and stability margin is computed to achieve the optimal trajectory of the midpoint of the hip joint of the *** angles of each joint are finally obtained through the inverse kinematics ***,the effectiveness of the proposed method is demonstrated by the results from the simulation and the experiment on the quadruped robot in BIT.

关键词： Quadruped robot ZMP stability margin Trot pattern

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Model-Based and Data-Driven control of Event- and Self-Triggered Discrete-Time LTI systems

arXiv

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arXiv 2022年

作者： Wang, Xin Berberich, Julian Sun, Jian Wang, Gang Allgöwer, Frank Chen, Jie The Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing10081 China The Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China The University of Stuttgart Institute for Systems Theory and Automatic Control Stuttgart70550 Germany The Department of Control Science and Engineering Tongji University Shanghai201804 China The State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

The present paper considers the model-based and data-driven control of unknown linear time-invariant discretetime systems under event-triggering and self-triggering transmission schemes. To this end, we begin by presenting a dynamic event-triggering scheme (ETS) based on periodic sampling, and a discrete-time looped-functional approach, through which a model-based stability condition is derived. Combining the model-based condition with a recent data-based system representation, a data-driven stability criterion in the form of linear matrix inequalities (LMIs) is established, which also offers a way of co-designing the ETS matrix and the controller. To further alleviate the sampling burden of ETS due to its continuous/periodic detection, a self-triggering scheme (STS) is developed. Leveraging pre-collected input-state data, an algorithm for predicting the next transmission instant is given, while achieving system stability. Finally, numerical simulations showcase the efficacy of ETS and STS in reducing data transmissions as well as of the proposed co-design methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear matrix inequalities

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Neural network structure design based on N-gauss activation function1

arXiv

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arXiv 2021年

作者： Lu, Xiangri Ma, Hongbin Zhang, Jingcheng State Key Laboratory of Intelligent Decision and Control for Complex Systems Beijing Institute ofTechnology Beijing China Institute of Pattern Recognition and Intelligent Systems School of Automation Beijing Institute ofTechnology China Institute of Navigation Guidance and Control School of Automation Beijing Institute of

Recent work has shown that the activation function of the convolutional neural network can meet the Lipschitz condition, then the corresponding convolutional neural network structure can be constructed according to the scale of the data set, and the data set can be trained more deeply, more accurately and more effectively. In this article, we have accepted the experimental results and introduced the core block N − Gauss, N − Gauss, and Swish (Conv1, Conv2, FC1) neural network structure design to train MNIST, CIFAR10, and CIFAR100 respectively. Experiments show that N − Gauss gives full play to the main role of nonlinear modeling of activation functions, so that deep convolutional neural networks have hierarchical nonlinear mapping learning capabilities. At the same time, the training ability of N − Gauss on simple one-dimensional channel small data sets is equivalent to the performance of ReLU and Swish. Copyright © 2021, The Authors. All rights reserved.

关键词： Gaussian distribution

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Solving Large-scale systems of Random Quadratic Equations via Stochastic Truncated Amplitude Flow

Solving Large-scale Systems of Random Quadratic Equations vi...

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European Signal Processing Conference

作者： Gang Wang Georgios B. Giannakis Jie Chen Dept. of ECE and Digital Tech. Center Univ. of Minnesota Mpls MN USA State Key Lab of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China

This work develops a new iterative algorithm, which is called stochastic truncated amplitude flow (STAF), to recover an unknown signal x ∈ R~n from m "phaseless" quadratic equations of the form ψ_i = |a_i~T x|, 1 ≤ i ≤ m. This problem is also known as phase retrieval, which is NP-hard in general. Building on an amplitude-based nonconvex least-squares formulation, STAF proceeds in two stages: s1) Orthogonality-promoting initialization computed using a stochastic variance reduced gradient algorithm;and, s2) Refinements of the initial point through truncated stochastic gradient-type iterations. Both stages handle a single equation per iteration, therefore lending STAF well to Big Data applications. Specifically for independent Gaussian {a_i}_(i=1)~m vectors, STAF recovers exactly any x exponentially fast when there are about as many equations as unknowns. Finally, numerical tests demonstrate that STAF improves upon its competing alternatives.

关键词： Stochastic processes Signal processing algorithms Runtime Mathematical model Computational complexity Europe Signal processing

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Study of Direction Probability and Algorithm of Improved Marriage in Honey Bees Optimization for Weapon Network System

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Defence Technology（防务技术） 2009年第2期5卷 152-157页

作者：杨晨光涂序彦陈杰 Systems Engineering Research Institute China State Shipbuilding Corporation Department of Automation School of Information Science and Technology Beijing Institute of Technology Ministry of Education of Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology

To solve the weapon network system optimization problem against small raid objects with low attitude,the concept of direction probability and a new evaluation index system are *** calculating the whole damaging probability that changes with the defending angle,the efficiency of the whole weapon network system can be subtly *** such method,we can avoid the inconformity of the description obtained from the traditional index *** new indexes are also proposed,*** index,overlap index and cover index,which help manage the relationship among several *** normalizing the computation results with the Sigmoid function,the matching problem between the optimization algorithm and indexes is well ***,the algorithm of improved marriage in honey bees optimization that proposed in our previous work is applied to optimize the embattlement *** is carried out to show the efficiency of the proposed indexes and the optimization algorithm.

关键词：网络系统优化问题破坏概率算法改进核武器蜜蜂婚姻 Sigmoid函数

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Model Free Adaptive control Algorithm based on ReOSELM for Autonomous Driving Vehicles

Model Free Adaptive Control Algorithm based on ReOSELM for A...

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第40届中国控制会议

作者： Xiaofei Zhang Hongbin Ma Zhichao Wang Mingyu Fan Bolin Zhao School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology The College of Computer Science and Artificial Intelligence Wenzhou University

Different road conditions and dynamic environment bring significant challenges to the control system of autonomous driving vehicle（ADV）. As is known, historical data collected from ADV contains valuable information about control systems,therefore, it is a promising thing to study adaptive control algorithms that have certain learning ability. In order to improve the control performance of ADV and the efficiency in data usage, in this paper, a model free adaptive control algorithm based on regularized online sequential extreme learning machine（ReOSELM） is introduced, it is difficult to analyze the algorithm based on neural network, and the system stability by improved update algorithm of ReOSELM is proved. Simulation results indicate that the proposed algorithm is effective in improving control precision when ADV is turning, and experimental results on an autonomous driving vehicle show that this algorithm is effective in real environment.

关键词： data-driven control autonomous driving vehicle model free adaptive control regularized online sequential extreme learning machine

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