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IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshops, SASOW

作者： Payam Zahadat Michael Bodi Ziad Salem Frank Bonnet Marcelo Elias De Oliveira Francesco Mondada Karlo Griparic Tomislav Haus Stjepan Bogdan Rob Mills Pedro Mariano Luis Correia Olga Kernbach Serge Kernbach Thomas Schmickl Artificial Life Laboratory of the Department of Zoology Karl-Franzens University Graz Austria Computer Engineering Department Aleppo University Syrian Arab Republic Rbotic Systems Laboratory Swiss Federal Institute of Technology (EPFL) Lausanne Switzerland Ecole Polytechnique Federale de Lausanne Lausanne VD CH Faculty of Electrical Engineering and Computing Laboratory for Robotics and Intelligent Control Systems Zagreb Croatia Universidade de Lisboa Lisboa Lisboa PT Cybertronica Research Research Center of Advanced Robotics and Environmental Science Stuttgart Germany

The goal of the work presented here is to influence the overall behaviour of specific animal societies by integrating computational mechatronic devices (robots) into those societies. To do so, these devices should be accepted by the animals aspart of the society and/or as part of the collectively formed environment. For that, we have developed two sets of robotic hardware for integrating into societies of two different animals: zebra fish and young honeybees. We also developed mechanisms to provide feedback from the behaviours of societies for the controllers of the robotic system. Two different computational methods are then used as the controllers of the robots in simulation and successfully adapted by evolutionary algorithms to influence the simulated animals for desired behaviours. Together, these advances in mechatronic hardware, feedback mechanisms, and controller methodology are laying essential foundations to facilitate experiments on modulating self-organised behaviour in mixed animal -- robot societies.

关键词： Animals Heating Vibrations Robot sensing systems Hardware Biochemistry

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Parameter Tuning for Nacelle-based Passive Structural control of a Spar-type Floating Wind Turbine

Parameter Tuning for Nacelle-based Passive Structural Contro...

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Annual Conference of the IEEE Industrial Electronics Society

作者： Yulin Si Hamid Reza Karimi Huijun Gao Department of Engineering University of Agder Faculty of Engineering and Science Research Institute of Intelligent Control and Systems Harbin Institute of Technology

ISBN: (纸本)9781479902255

This paper deals with the modeling and parameter tuning of a spar-type floating wind turbine with a tuned mass damper (TMD) installed in nacelle. Firstly, a mathematical model for the system surge-heave-pitch motion is established based on first principles. Secondly, different parameter tuning methods are adopted to find the optimal TMD parameters for load reduction. Thirdly, nonlinear wind turbine simulations with different designs are conducted under different wind and wave conditions. The results show that TMD with small spring and damping coefficients will help to produce much load reduction in above rated condition. However, it may deteriorate system performance when the turbine is working below rated. In contrast, the design with large spring and damping constants will achieve moderate load reduction in both working conditions.

关键词： tuning Wind Energy Conversion Systems turbines Damping ratio structure control Working Conditions Floating System performance LOAD damping constant waves Pitching motion

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Benchmarking intelligent service robots through scientific competitions: The RoboCup@Home approach

Benchmarking intelligent service robots through scientific c...

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2013 AAAI Spring Symposium

作者： Holz, Dirk Iocchi, Luca Van Der Zant, Tijn Autonomous Intelligent Systems Group Department of Computer Science VI University of Bonn Germany Dept. of Computer Control and Management Engineering Sapienza University of Rome Italy Dept. of Artificial Intelligence Faculty of Natural Sciences University of Groningen Netherlands

ISBN: (纸本)9781577356011

The dynamical and uncertain environments of domestic service robots, which includehumans, require rethinking of the benchmarking principles for testing these robots. Since 2006 RoboCup@Home has used statistical procedures to track and steer the progress ofdomestic service robots. This paper explains the procedures and shows outcomes of these international benchmarking efforts. Although aspects such as shopping in a supermarket receive a fair amount of attention in the robotics community, the authors believe that a recently implemented test is the most important outcome of RoboCup@Home, namely the benchmarking of robot cognition. © 2013, Association for the Advancement of artificial intelligence.

关键词： Benchmarking

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Nonlinear analysis of dynamical complex networks 2014

Abstract and Applied Analysis

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Abstract and Applied Analysis 2014年第SI42期2014卷 1-4页

作者： Wang, Zidong Shen, Bo Dong, Hongli Hu, Jun He, Xiao Ding, Derui Department of Computer Science Brunel University Uxbridge Middlesex UB8 3PH United Kingdom Communication Systems and Networks (CSN) Research Group Faculty of Engineering King Abdulaziz University Jeddah 21589 Saudi Arabia School of Information Science and Technology Donghua University Shanghai 200051 China College of Electrical and Information Engineering Northeast Petroleum University Daqing 163318 China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin 150001 China Department of Applied Mathematics Harbin University of Science and Technology Harbin 150080 China Department of Automation Tsinghua University Beijing 100084 China Department of Applied Mathematics Anhui Polytechnic University Wuhu 241000 China

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Model Free Predictive control for a Nonlinear Biological System with intelligent Optimization Approach

Model Free Predictive Control for a Nonlinear Biological Sys...

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第25届中国控制与决策会议

作者： Mojtaba Zaare Mehrjerdi Ghassem Amoabediny Behzad Moshiri Arman Ashktorab Babak Nadjar Araabi Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering Faculty of Engineering University of Tehran Research Center for New Technologies in Life Science Engineering University of Tehran Department of Life Science Engineering Faculty of New Technologies University of Tehran Department of Chemical Engineering Faculty of Engineering University of Tehran

ISBN: (纸本)9781467355339

Today the importance of life science and its related processes are undeniable. Modeling and control of these kind of processes are too complicate because of existence of delay in growth and also nonlinear behavior of micro-organisms. Model predictive control is one of the most popular advanced controlling strategies in this industry, however its dependence on accurate model for predicting future input and output values is limitating. If there is a way that could predict the future values of the process properly, it is possible to overcome to the existing challenges. In this paper we design a model free predictive controller by using a trained recurrent neural network as a predictor for prediction stage at MPC and using GA for solving the associated optimization problem that result the optimal control signal sequence.

关键词： NNMPC GA Biological System Aerated Flask

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Model Predictive control with Sequential Linearization Approach for a Nonlinear Time Varying Biological System

Model Predictive Control with Sequential Linearization Appro...

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第25届中国控制与决策会议

作者： Mojtaba Zaare Mehrjerdi Behzad Moshiri Ghassem Amoabediny Arman Ashktorab Babak Nadjar Araabi Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering Faculty of Engineering University of Tehran Research Center for New Technologies in Life Science Engineering University of Tehran Department of Life Science Engineering Faculty of New Technologies University of Tehran Department of Chemical Engineering Faculty of Engineering University of Tehran

ISBN: (纸本)9781467355339

As life science progress and its consequences provide human life necessities, controlling these kinds of processes has recently become very important. Unfortunately because of including delay and nonlinear behavior of micro-organisms these processes have nonlinear time varying model and so controlling them is too complicated. These nonlinear dynamics are too slow and so it is possible to linearize the model step by step and apply control signal to the local linearized models. In this paper we design a model predictive controller for each linearized model at time unit and as a result a nonlinear time varying system has been controlled properly. As these localized models are open loop unstable, we have to use close loop paradigm also due to its numerically robustness. Finally result has validated with experimental values and reliability of this approach has been exposed.

关键词： MPC, Sequential Linearization, Biological System control, Aerated Flask

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Path Planning of Wheel Loader type Robot for Scooping and Loading Operation by Genetic Algorithm

Path Planning of Wheel Loader type Robot for Scooping and Lo...

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International Conference on control, Automation and Systems

作者： Toshinobu Take Kentaro Ichikawa Kazuya Okawa Shigeru Sarata Takashi Tsubouchi Akira Torige Department of Systems Design Engineering Faculty of Science and Technology Seikei University Japan Department of Machinery and Control Systems Graduate School and Faculty of Engineering Chiba University Japan Department of Intelligent Interaction Technologies Graduate School of Systems and Information Engineering University of Tsukuba Japan

ISBN: (纸本)9781479905492

There is a demand of mining automation by heavy equipment in an open mine. Authors aim to automization of motion for wheel loader in scooping and loading operation. In this paper, we applied the optimization method by Genetic Algorithm to the path planning for the wheel loader on scooping and loading operation. We showed the quasi-optimized path and demonstrated by the miniature wheel loader robot.

关键词： Loading Operation by Genetic Algorithm wheel loader on scooping miniature wheel loader robot

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Displacement prediction of landslide based on PSOGSA-ELM with mixed kernel

Displacement prediction of landslide based on PSOGSA-ELM wit...

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Cheng Lian Zhigang Zeng Wei Yao Huiming Tang School of Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry China School of Computer Science South-Central University for Nationalities Wuhan China Faculty of Engineering China University of Geosciences Wuhan China

The prediction of landslide displacement is essential for carrying out to improve the disaster warning system and reduce casualties and property losses. This study applies a novel neural network technique, extreme learning machine (ELM) with kernel function, to landslide displacement prediction problem. However, the generalization performance of ELM with kernel function depends closely on the kernel types and the kernel parameters. In this paper, we use a convex combination of Gaussian kernel function and polynomial kernel function in ELM, which may use these two types of kernel functions' advantages. In order to avoid blindness and inaccuracy in parameter selection, a novel hybrid optimization algorithm based on the combination of Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) is used to optimize the regularization parameter C, the Gaussian kernel parameter γ, the polynomial kernel parameter q and the mixing weight coefficient η. The performance of our model is verified through two case studies in Baishuihe landslide and Yuhuangge landslide.

关键词： Kernel Terrain factors Optimization Hazards Reservoirs Training Support vector machines

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Study on landslide deformation prediction based on recurrent neural network under the function of rainfall

Study on landslide deformation prediction based on recurrent...

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19th International Conference on Neural Information Processing, ICONIP 2012

作者： Chen, Huangqiong Zeng, Zhigang Tang, Huiming Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan 430074 China Faculty of Engineering China University of Geosciences Wuhan Hubei 430074 China

ISBN: (纸本)9783642344770

Landslide deformation prediction has significant practical value that can provide guidance for preventing the disaster and guarantee the safety of people's life and property. In this paper, a method based on recurrent neural network (RNN) for landslide prediction is presented. The results show that the prediction accuracy of RNN model is much higher than the feedforward neural network model for Baishuihehe landslide. Therefore, the RNN model is an effective and feasible method to further improve accuracy for landslide displacement prediction. © 2012 Springer-Verlag.

关键词： Recurrent neural networks

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ℋ∞Model Reduction for Discrete-Time Markovian Jump Systems with Deficient Mode Information

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Mathematical Problems in engineering 2013年第1期2013卷

作者： Yanling Wei Mao Wang Hamid Reza Karimi Nan Wang Jianbin Qiu Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin 150001 *** Department of Engineering Faculty of Engineering and Science University of Agder 4898 Grimstad Norwayuia.no

This paper investigates the problem of ℋ ∞ model reduction for a class of discrete-time Markovian jump linear systems (MJLSs) with deficient mode information, which simultaneously involves the exactly known, partially unknown, and uncertain transition probabilities. By fully utilizing the properties of the transition probability matrices, together with the convexification of uncertain domains, a new ℋ ∞ performance analysis criterion for the underlying MJLSs is first derived, and then two approaches, namely, the convex linearisation approach and iterative approach, for the ℋ ∞ model reduction synthesis are proposed. Finally, a simulation example is provided to illustrate the effectiveness of the proposed design methods.

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