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arXiv 2025年

作者： Zhang, Kunpeng Xu, Lei Yi, Xinlei Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Department of Mechanical Engineering University of Victoria VictoriaBCV8W 2Y2 Canada Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai201210 China Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen GroningenAG 9747 Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper considers distributed online nonconvex optimization with time-varying inequality constraints over a network of agents. For a time-varying graph, we propose a distributed online primal–dual algorithm with compressed communication to efficiently utilize communication resources. We show that the proposed algorithm establishes an O(T max{1−θ1,θ1}) network regret bound and an O(T 1−θ1/2) network cumulative constraint violation bound, where T is the number of iterations and θ1 ∈ (0, 1) is a user-defined trade-off parameter. When Slater’s condition holds (i.e, there is a point that strictly satisfies the inequality constraints at all iterations), the network cumulative constraint violation bound is reduced to O(T 1−θ1). These bounds are comparable to the state-of-the-art results established by existing distributed online algorithms with perfect communication for distributed online convex optimization with (time-varying) inequality constraints. Finally, a simulation example is presented to validate the theoretical results. Copyright © 2025, The Authors. All rights reserved.

关键词： Convex optimization

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Intent Pattern Recognition of Lower-limb Motion Based on Mechanical Sensors

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IEEE/CAA Journal of Automatica Sinica 2017年第4期4卷 651-660页

作者： Zuojun Liu Wei Lin Yanli Geng Peng Yang School of Control Science and Engineering Hebei University of Technology Engineering Research Center of Intelligent Rehabilitation Ministry of Education Department of Electrical Engineering and Computer Science Case Western Reserve University Hebei University of Technology

Based on the regularity nature of lower-limb motion,an intent pattern recognition approach for above-knee prosthesis is proposed in this paper. To remedy the defects of recognizer based on electromyogram(EMG), we develop a pure mechanical sensor architecture for intent pattern recognition of lower-limb motion. The sensor system is composed of an accelerometer, a gyroscope mounted on the prosthetic socket, and two pressure sensors mounted under the sole. To compensate the delay in the control of prosthesis, the signals in the stance phase are used to predict the terrain and speed in the swing phase. Specifically, the intent pattern recognizer utilizes intraclass correlation coefficient(ICC) according to the Cartesian product of walking speed and terrain. Moreover, the sensor data are fused via DempsterShafer's theory. And hidden Markov model(HMM) is used to recognize the realtime motion state with the reference of the prior step. The proposed method can infer the prosthesis user's intent of walking on different terrain, which includes level ground,stair ascent, stair descent, up and down ramp. The experiments demonstrate that the intent pattern recognizer is capable of identifying five typical terrain-modes with the rate of 95.8%. The outcome of this investigation is expected to substantially improve the control performance of powered above-knee prosthesis.

关键词： Above-knee prosthesis hidden Markov model(HMM) intra-class correlation coefficient(ICC) intent pattern recognition sensor fusion

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DESYNCHRONIZATION OF TWO COUPLED LIMIT-CYCLE OSCILLATORS USING AN ASTROCYTE-INSPIRED controlLER

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International Journal of Biomathematics 2014年第1期7卷 1-23页

作者： GHAZAL MONTASERI MOHAMMAD JAVAD YAZDANPANAH Advanced Control Systems Laboratory School of Electrical and Computer EngineeringCollege of Engineering University of Tehran Tehran Iran Advanced Control Systems LaboratoryControl and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

Astrocytes have potential to break synchrony between neurons. Authors＇ recent researches reveal that astrocytes vary the synchronization threshold and provide an appropriate feedback control in stabilizing neural activities. In this study, we propose an astrocyte-inspired controller for desynchronization of two coupled limit-cycle oscillators as a minimal network model. The design procedure consists of two parts. First, based on the astrocyte model, the structure of the dynamic controller is suggested. Then, to have an emcient controller, parameters of controller are tuned through an optimization algo- rithm. The proposed bio-inspired controller takes advantages of three important proper- ties：（1） the controller desynchronizes the oscillators without any undesirable effects （e.g. stopping, annihilating or starting divergent oscillations）; （2） it consumes little effort to preserve the desirable desynchronized state; and （3） the controller is robust with respect to parameters＇ variations. Simulation results reveal the ability of the proposed controller.

关键词： Astrocyte dynamic model bio-inspired controller coupled limit-cycle oscillators desynchronization optimization

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Radiomics model based on computed tomography images for prediction of radiation-induced optic neuropathy following radiotherapy of brain and head and neck tumors

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Heliyon 2025年第1期11卷 e41409页

作者： Nafchi, Elham Raiesi Fadavi, Pedram Amiri, Sepideh Cheraghi, Susan Garousi, Maryam Nabavi, Mansoureh Daneshi, Iman Gomar, Marzieh Molaie, Malihe Nouraeinejad, Ali Department of Radiation Sciences Faculty of Allied Medicine Iran University of Medical Sciences Tehran Iran Department of Radiation Oncology School of Medicine Iran University of Medical Science Tehran Iran Department of Information Technology Faculty of Electrical and Computer Engineering University of Tehran Tehran Iran Radiation Biology Research Center Iran University of Medical Sciences Tehran Iran Radiation Oncology Research Center (RORC) Cancer Institute Tehran University of Medical Sciences Tehran Iran Department of Clinical Oncology Haft-e-Tir Hospital Iran University of Medical Science Tehran Iran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Optometry and Vision Science School of Rehabilitation Tehran University of Medical Science Tehran Iran

Purpose: We aimed to build a machine learning-based model to predict radiation-induced optic neuropathy in patients who had treated head and neck cancers with radiotherapy. Materials and methods: To measure radiation-induced optic neuropathy, the visual evoked potential values were obtained in both case and control groups and compared. Radiomics features were extracted from the area segmented which included the right and left optic nerves and chiasm. We integrated CT image features with dosimetric and clinical data subsequently, ranked 5 supervised ML models Bernoulli Naive Bayes, Decision Tree, Gradient Boosting Decision Trees, K-Nearest Neighbor, and Random Forest on 4 input datasets to predict radiation-induced visual complications classifiers by implementing 5-fold cross-validation. The F1 score, accuracy, sensitivity, specificity, and area under the ROC curve were compared to access prediction capability. Results: radiation-induced optic neuropathy affected 31 % of the patients. 856 radiomic characteristics were extracted and selected from each segmented area. Decision Tree and Random Forest with the highest AUC (97 % and 95 % respectively) among the five classifiers were the most powerful algorithms to predict radiation-induced optic neuropathy. Chiasm with higher sensitivity and precision was able to predict radiation-induced optic neuropathy better than right or left optic nerve or combination of all radiomic features. Conclusion: We found that combination of radiomic, dosimetric, and clinical factors can predict radiation-induced optic neuropathy after radiation treatment with high accuracy. To acquire more reliable results, it is recommended to conduct visual evoked potential tests before and after radiation therapy, with multiple follow-up courses and more patients. © 2024 The Authors

关键词： Computed tomography Machine learning Radiation-induced optic neuropathy Radiomics Visual evoked potential

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Consensus Problem in General Linear Multiagent Systems Under Stochastic Topologies

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IFAC-PapersOnLine 2016年第13期49卷 13-18页

作者： Rezaee, Hamed Abdollahi, Farzaneh The Center of Excellence on Control and Robotics Department of Electrical Engineering Amirkabir University of Technology Tehran Iran Department of Electrical and Computer Engineering Concordia University MontrealQuebec Canada

Consensus control of a class of multiagent systems with general linear dynamics is studied. Based on solution of some linear matrix inequalities, a protocol is obtained which guarantees achieving consensus among agents in the presences of stochastically switching topologies. By invoking the concept super-martingales, it is shown that if the probability of the network connectivity is not zero, the agents reach almost sure consensus upon their state vectors. Despite existing results in the literature for consensus control of general linear multiagent systems under stochastic networks, the proposed consensus protocol in this paper requires no knowledge on the set of feasible topologies, and this issue significantly decreases the design computational costs. Simulation results for a diving consensus problem among a team of autonomous underwater vehicles validate the proposed consensus protocol. © 2016

关键词： Multi agent systems Autonomous underwater vehicles Linear matrix inequalities Stochastic systems Topology Almost sure consensus computational costs Consensus problems Consensus protocols Linear dynamics Network connectivity Of autonomous underwater vehicles Stochastic networks

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Active disturbance rejection control： between the formulation in time and the understanding in frequency

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control Theory and Technology 2016年第3期14卷 250-259页

作者： Qing ZHENG Zhiqiang GAO Department of Electrical and Computer Engineering Gannon University Erie PA 16541 U.S.A. Center for Advanced Control Technologies Department of Electrical and Computer Engineering Cleveland State University Cleveland OH 44115 U.S.A.

With the rapid deployments of the active disturbance rejection control （ADRC） as a bonafide industrial technology in the background, this paper summarizes some recent results in the analysis of linear ADRC and offers explanations in the frequency response language with which practicing engineers are familiar. Critical to this endeavor is the concept of bandwidth, which has been used in a more general sense. It is this concept that can serve as the link between the otherwise opaque state space formulation of the ADRC and the command design considerations and concerns shared by practicing engineers. The remarkable characteristics of a simple linear ADRC was first shown in the frequency domain, followed by the corresponding analysis in time domain, where the relationship between the tracking error and the ADRC bandwidth is established. It is shown that such insight is only possible by using the method of solving linear differential equations, instead of the more traditional techniques such as the Lyapunov methods, which tend to be more conservative and difficult to grasp by engineers. The insight obtained from such analysis is further demonstrated in the simulation validation.

关键词： Active disturbance rejection control extended state observer uncertain systems stability bandwidth

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New results in global stabilization for stochastic nonlinear systems

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control Theory and Technology 2016年第1期14卷 57-67页

作者： Tao BIAN Zhong-Ping JIANG Control and Networks Lab Department of Electrical and Computer Engineering Tandon School of Engineering New York University 5 Metrotech Center Brooklyn NY 11201 U.S.A.

This paper presents new results on the robust global stabilization and the gain assignment problems for stochastic nonlinear systems. Three stochastic nonlinear control design schemes are developed. Furthermore, a new stochastic gain assignment method is developed for a class of uncertain interconnected stochastic nonlinear systems. This method can be combined with the nonlinear small-gain theorem to design partial-state feedback controllers for stochastic nonlinear systems. Two numerical examples are given to illustrate the effectiveness of the proposed methodology.

关键词： Stochastic nonlinear systems stochastic input-to-output stability stabilization gain assignment small-gain

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Optimal sensors and actuators placement for large-scale switched systems

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International Journal of Dynamics and control 2019年第1期7卷 147-156页

作者： Seyed Sakha, Masoud Shaker, Hamid Reza Tahavori, Maryamsadat Department of Control Engineering Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Center for Energy Informatics University of Southern Denmark Odense Denmark

The problem of sensor and actuator placement is computationally expensive in particular in dealing with large-scale systems. This problem is even more computationally intensive in the case of switched systems. In this paper, we propose a new numerical approach for sensor and actuator placement for large-scale switched systems. We first introduce restricted genetic algorithm (RGA). RGA is an evolutionary algorithm which is developed specifically for sensors and actuator placement. We then use RGA to reduce the computational burden of the sensor and actuator placement for switched system. The proposed method uses the generalized gramians for switched systems which are called nice gramians. The nice gramians quantify the level of observability and reachability of switched systems. To the best of our knowledge, this paper presents the first results on sensor and actuator placement of switched systems. We show the effectiveness of the approach with the help of numerical examples. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Genetic algorithms

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Neuron PI control for semi-active suspension system of tracked vehicle

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Journal of Central South University 2011年第2期18卷 444-450页

作者：曾谊晖刘少军鄂加强 College of Mechanical and Electrical Engineering Central South University Computer Numerical Control Tooling Center Hunan International Economics University College of Mechanical and Vehicle Engineering Hunan University

A neuron proportion integration (PI) control strategy for semi-active suspension system of tracked vehicle was proposed based on its unique structure and the multiple and complex environment of the driving traffic. An adaptive genetic algorithm is used to optimize the parameters of the neuron PI controller. The simulation result of the neuron PI control for semi-active suspension system of tracked vehicle indicates that the vertical amplitude,pitch angle and vertical acceleration of the vehicle are well controlled. The root mean square (RMS) of the vertical amplitude decreases by 37.2%,and 45.2% for the pitch angle,38.6% for the vertical acceleration. The research of neuron PI control experiment for the semi-active suspension system of the tracked vehicle model mining in benthal indicates that the RMS of the weight acceleration vibrating along the vertical direction decreases by 29.5%,the power spectral density resonance peak of the acceleration of the car body decreases by 23.8%.

关键词： tracked vehicle magneto rheological damper semi-active suspension preview technology neuron PI control

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Algorithms for the traveling salesman problem with neighborhoods involving a Dubins vehicle

Algorithms for the traveling salesman problem with neighborh...

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作者： Isaacs, Jason T. Klein, Daniel J. Hespanha, João P. Center for Control Dynamical Systems and Computation Department of Electrical and Computer Engineering University of California Santa Barbara CA 93106 United States

ISBN: (纸本)9781457700804

We study the problem of finding the minimum-length curvature constrained closed path through a set of regions in the plane. This problem is referred to as the Dubins Traveling Salesperson Problem with Neighborhoods (DTSPN). Two algorithms are presented that transform this infinite dimensional combinatorial optimization problem into a finite dimensional asymmetric TSP by sampling and applying the appropriate transformations, thus allowing the use of existing approximation algorithms. We show for the case of disjoint regions, the first algorithm needs only to sample each region once to produce a tour within a factor of the length of the optimal tour that is independent of the number of regions. We present a second algorithm that performs no worse than the best existing algorithm and can perform significantly better when the regions overlap. © 2011 AACC American Automatic control Council.

关键词： Traveling salesman problem

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