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arXiv 2023年

作者： Mitze, Ruth Kvasnica, Michal Mönnigmann, Martin Automatic Control and Systems Theory Ruhr-Universität Bochum Bochum Germany Institute of Information Engineering Automation and Mathematics Faculty of Chemical and Food Technology Slovak University of Technology Bratislava Slovakia

This paper studies symmetric constrained linear-quadratic optimal control problems and their parametric solutions. The parametric solution of such a problem is a piecewise-affine feedback law that can be equivalently expressed as a set of active sets. We show symmetries of the optimal control problem entail symmetries of the active sets, which can be used to simplify finding the set of active sets considerably. Specifically, we improve a recently proposed method for the dynamic-programming-based enumeration of all active sets. The achieved reduction of the computational effort is illustrated with an example. © 2023, CC BY-NC-ND.

关键词： Quadratic programming

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Efficient Formulation of Collision Avoidance Constraints in optimization Based Trajectory Planning and control

Efficient Formulation of Collision Avoidance Constraints in ...

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control Technology and Applications (CCTA),

作者： Max Lutz Thomas Meurer Chair of Automatic Control Faculty of Engineering Kiel University Kiel Germany

ISBN: (纸本)9781665436441

To be applicable to real world scenarios trajectory planning schemes for mobile autonomous systems must be able to efficiently deal with obstacles in the area of operation. In the context of optimization based trajectory planning and control a number of different approaches to formulate collision avoidance constraints can be found in the literature. This work presents a novel formulation building on constructive solid geometry (CSG) to describe collision avoidance constraints. It is highly efficient due to a very low number of nonlinear inequality constraints required for a given number of obstacles and sample points and in contrast to the original CSG formulation allows to consider the controlled system’s shape. To allow for a comparison, popular methods to represent obstacles from the literature are summarized and characterized, namely the simple ellipsoidal representation, the original CSG method as well as a direct and an indirect implementation of a signed distance based approach. A benchmark example shows the good performance of the proposed formulation. Here, optimal trajectory planning for marine surface vessels formulated as a nonlinear programming problem is used, where the scenario is designed based on the maritime test field in Kiel, Germany.

关键词： Trajectory planning Shape Autonomous systems Simulation Buildings Programming control systems

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An Experimental Research on Temperature Accuracy Algorithm in Fan Coil Thermostat

An Experimental Research on Temperature Accuracy Algorithm i...

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China International Electrical and Energy Conference (CIEEC)

作者： Yunus Arı Ergün Cücük Seda Karateke Electric and Electronic Engineering Institute of Graduate Studies Istanbul University - Cerrahpaşa Istanbul Turkey Department of Research and Development Ges Teknik Air Conditioning Control and Automation Systems Industry and Trade Inc. Istanbul Turkey Department of Computer Engineering Faculty of Engineering Istanbul Topkapı University Istanbul Turkey

The efficient operation of HVAC&R systems are based on keeping indoor temperature and air quality at an optimum level without disturbing comfort. Starting from this point, in this experimental research, the factors affecting the measurement accuracy during the room temperature detection of a wall mount fan coil thermostat were examined. Among the external effects, the effect of the device components on the measurement was focused and the solution of the measurement deviations occurred in this extent was investigated. Based on the experiments, the algorithm that must be applied to reach the optimum temperature measurement of a room thermostat developed for Fan Coil systems was proposed.

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A Power Split Strategy for a Vehicular Hybrid Energy Storage System Using Adaptive Neuro Fuzzy Inference System 8

A Power Split Strategy for a Vehicular Hybrid Energy Storage...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Li, Jiao Zhang, Lijun Yang, Longzhi Wang, Bo School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Huazhong University of Science and Technology Belt and Road Joint Laboratory on Measurement and Control Technology Wuhan430074 China Northumbria University Faculty of Engineering and Environment Department of Computer Science and Digital Technologies Newcastle upon TyneNE1 8ST United Kingdom

ISBN: (纸本)9798350300178

This paper presents a real-time power split strategy for a battery-supercapacitor hybrid energy storage system. The objective of the proposed strategy is to alleviate battery degradation through effective supercapacitor utilization. The proposed strategy employs an adaptive neural fuzzy inference system, which is capable of learning a power split strategy from offline optimization results and distributing power in real-time. The performance of the strategy proposed and a low pass filtering technique is compared through simulations. The results demonstrate that the proposed method can effectively reduce battery capacity loss and perform well under unknown driving cycles in real time. © 2023 IEEE.

关键词： Supercapacitor

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Driving Envelope: On Vehicle Stability Through Tire Capacities

Driving Envelope: On Vehicle Stability Through Tire Capaciti...

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IEEE Symposium on Intelligent Vehicle

作者： Denis Efremov Martin Klauč o Tomá š Haniš Department of Control Engineering Faculty of Electrical Engineering Czech Technical University in Prague Nov&#x00E9 M&#x011B sto Czech Republic Institute of Information Engineering Automation and Mathematics Slovak University of Technology in Bratislava Bratislava Slovakia

ISBN: (数字)9781665488211

ISBN: (纸本)9781665488228

Integrated automated safety systems in vehicles significantly reduced the number of car crashes. They help the driver in critical maneuvers when tires lose their grip on the driving surface. For instance, the technology of the anti-lock braking system and its augmentations (electronic stability control and traction control system) has already saved thousands of lives. Nevertheless, we still see room for improvement. This work defines boundaries in the vehicle state-space, excluding unstable vehicle maneuvers. Such boundaries form a so-called driving envelope. The resulting set includes all states where the vehicle’s wheels are not locked, overspun, or skidding. For the definition of the driving envelope, we use the Pacejka tire model and nonlinear single-track model. This paper shows how each tire dynamic property results in vehicle dynamics. Also, it discusses the application of nonlinear and linearized driving envelope boundaries on a single-track model. Then it shows that the linearized driving envelope constraints form a close to control invariant set over the vehicle state-space. Thus, the driving envelope is almost a feasible set, and it could be used in the model predictive control approaches with soft constraints. Protecting the driving envelope, one can preserve each wheel from locking, wheelspin, and skidding.

关键词： Intelligent vehicles Wheels control systems Tires Stability analysis Safety Automobiles

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Can Digital Intelligence and Cyber-Physical-Social Systems Achieve Global Food Security and Sustainability?

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IEEE/CAA Journal of Automatica Sinica 2023年第11期10卷 2070-2080页

作者： Yanfen Wang Mengzhen Kang Yali Liu Juanjuan Li Kai Xue Xiujuan Wang Jianqing Du Yonglin Tian Qinghua Ni Fei-Yue Wang College of Resources and Environment University of Chinese Academy of SciencesBeijing 100049 Beijing Yanshan Earth Critical Zone National Research Station University of Chinese Academy of SciencesBeijing 101408 State Key Laboratory of Tibetan Plateau Earth System Environment and ResourcesInstitute of Tibetan Plateau ResearchChinese Academy of SciencesBeijing 100101China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of Sciences(CASIA)Beijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China School of Grassland Science Beijing Forestry UniversityBeijing 100083China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China State Key Laboratory of Plateau Ecology and Agriculture Qinghai UniversityXining 810016 Key Laboratory of Adaptation and Evolution of Plateau Biota Northwest Institute of Plateau BiologyChinese Academy of SciencesXining 810001 Binzhou Institute of Technology Weiqiao-UCAS Science and Technology ParkBinzhou 256606China Beijing Yanshan Earth Critical Zone National Research Station University of Chinese Academy of SciencesBeijing 101408China Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078 State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of Sciences(CASIA)Beijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078 Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of SciencesBeijing 100098 State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China IEEE

Plants sequester carbon through photosynthesis and provide primary productivity for the ecosystem. However, they also simultaneously consume water through transpiration, leading to a carbon-water balance relationship. Agricultural production can be regarded as a form of carbon sequestration *** the perspective of the natural-social-economic complex ecosystem, excessive water usage in food production will aggravate regional water pressure for both domestic and industrial purposes. Hence, achieving a harmonious equilibrium between carbon and water resources during the food production process is a key scientific challenge for ensuring food security and sustainability. Digital intelligence(DI) and cyber-physical-social systems(CPSS) are emerging as the new research paradigms that are causing a substantial shift in the conventional thinking and methodologies across various scientific fields, including ecological science and sustainability studies. This paper outlines our recent efforts in using advanced technologies such as big data, artificial intelligence(AI), digital twins, metaverses, and parallel intelligence to model, analyze, and manage the intricate dynamics and equilibrium among plants, carbon, and water in arid and semiarid ecosystems. It introduces the concept of the carbon-water balance and explores its management at three levels: the individual plant level, the community level, and the natural-social-economic complex ecosystem level. Additionally, we elucidate the significance of agricultural foundation models as fundamental technologies within this context. A case analysis of water usage shows that, given the limited availability of water resources in the context of the carbon-water balance, regional collaboration and optimized allocation have the potential to enhance the utilization efficiency of water resources in the river basin. A suggested approach is to consider the river basin as a unified entity and coordinate the relationship between the

关键词： Carbon-water balance decision-support digital intelligence(DI) foundation models planning

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Parameter Optimization of Interval Type-2 Fuzzy Neural Networks Based on PSO and BBBC Methods

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 247-257页

作者： Jiajun Wang Tufan Kumbasar the School of Automation Hangzhou Dianzi University IEEE the Control and Automation Engineering Department Faculty of Electrical and Electronics EngineeringIstanbul Technical University

Interval type-2 fuzzy neural networks(IT2FNNs)can be seen as the hybridization of interval type-2 fuzzy systems(IT2FSs) and neural networks(NNs). Thus, they naturally inherit the merits of both IT2 FSs and NNs. Although IT2 FNNs have more advantages in processing uncertain, incomplete, or imprecise information compared to their type-1 counterparts, a large number of parameters need to be tuned in the IT2 FNNs,which increases the difficulties of their design. In this paper,big bang-big crunch(BBBC) optimization and particle swarm optimization(PSO) are applied in the parameter optimization for Takagi-Sugeno-Kang(TSK) type IT2 FNNs. The employment of the BBBC and PSO strategies can eliminate the need of backpropagation computation. The computing problem is converted to a simple feed-forward IT2 FNNs learning. The adoption of the BBBC or the PSO will not only simplify the design of the IT2 FNNs, but will also increase identification accuracy when compared with present methods. The proposed optimization based strategies are tested with three types of interval type-2 fuzzy membership functions(IT2FMFs) and deployed on three typical identification models. Simulation results certify the effectiveness of the proposed parameter optimization methods for the IT2 FNNs.

关键词： Big bang-big crunch (BBBC) interval type-2 fuzzy neural networks (IT2FNNs) parameter optimization particle swarm optimization (PSO)

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Extended NNs-Based Adaptive Event-Triggered Asymptotic control for Nonlinear Systems Against Actuator Faults

Extended NNs-Based Adaptive Event-Triggered Asymptotic Contr...

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International Symposium on Autonomous Systems (ISAS)

作者： Shanlin Liu Yingwei Zhang Xudong Zhao Hesheng Wang College of Information Science and Engineering Northeastern University Shenyang China State Laboratory of Synthesis Automation of Process Industry Northeastern University Shenyang China Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian China 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 IntelligentControl and Management Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350363173

ISBN: (纸本)9798350363180

In this article, an adaptive event-triggered asymp¬totically fault-tolerant control (FTC) issue for nonlinear systems with actuator faults is investigated. An extended neural networks (NNs) technique is introduced to estimate unknown functions of the system under consideration. Compared with traditional NNs that ignore approximation errors, the proposed extended NNs further processes approximation errors, reducing the conservatism of the proposed algorithm. Then, within the framework of backstepping method, a new adaptive control strategy is developed by combining event-triggered mechanism (ETM) and bound estimation (BE) approach, which ensures the asymptotic convergence of system states while also reducing the transmission of unnecessary information, thus improving the utilization of communication resources. At the same time, the designed controller eliminates the adverse effects of actuator failures and system nonlinear terms. Finally, a simulation example of a single-link robot system is provided to demonstrate that the developed control strategy can ensure that all signals are bounded and system states are asymptotically convergent.

关键词： Actuators Backstepping Fault tolerant systems Process control Artificial neural networks control systems Approximation error

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Depression Symptom Assessment Based on Emotional Conflict and Fuzzy RBF Neural Network

Depression Symptom Assessment Based on Emotional Conflict an...

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第34届中国过程控制会议

作者： Jie Song Tao Zhang Mi Li Department of Automation Faculty of Information TechnologyBeijing University of Technology Beijing International Collaboration Base on Brain Informatics and Wisdom Services Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Engineering Research Center of Digital Community Ministry of Education

Depression is a widespread emotional disorder that is marked by a lasting and intense feeling of sadness,diminished enthusiasm,an absence of joy,and difficulty in *** this study,we first constructed a fuzzy RBF neural network model that conforms to fuzzy reasoning logic for depression symptom *** then employed the fuzzy c-means technique to establish the starting values for the three crucial parameters of the ***,grounded in the traits of parameter training,we proposed a method to optimize network parameter *** the practical application of depression symptom evaluation,we collected emotional conflict decision-making behavior data from 86 patients with *** this basis,we completed feature construction,feature normalization and feature dimensionality *** emotional conflict features and fuzzy RBF neural network to predict the PHQ-9 scores of patients with depression,we finally obtained an MAE of 3.84 and an RMSE of 4.98.

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Multi-population Cooperative Particle Swarm Optimization Algorithm and Its Application to Hyperparameter Optimization of Neural Network Model

Multi-population Cooperative Particle Swarm Optimization Alg...

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第34届中国过程控制会议

作者： Jiulin Sun Yanbo Chen Haiyan Li Mi Li Department of Automation Faculty of Information TechnologyBeijing University of Technology Beijing International Collaboration Base on Brain Informatics and Wisdom Services Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Engineering Research Center of Digital Community Ministry of Education

The lack of effective communication and cooperation among subpopulations leads to subpopulation aggregation,which affects the movement direction and search accuracy of the *** this study,we propose a multi-population cooperative particle swarm optimization(MCPSO) to improve communication and cooperation among *** MCPSO,we reconstruct the population optimal position(gbest) and subpopulation optimal position(lbest).Additionally,we establish exploration,exploitation,and auxiliary subpopulations and assign them different *** results using the CEC2017test function in three dimensions(30,50,and 100) demonstrate the superior performance of *** applied to hyperparameter optimization of neural network model,the results show that MCPSO outperforms other PSO algorithms,thereby validating its effectiveness and practicality.

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