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2013 6th IEEE Conference on Robotics, Automation and Mechatronics, RAM 2013

作者： Luo, Wenhao Peng, Jun Wang, Jing Department of Automation Shanghai Jiao Tong University Ministry of Education of China Shanghai 200240 China School of Information Science and Engineering Central South University Hunan Engineering Laboratory for Advanced Control and Intelligent Automation Changsha 410083 China Computer Engineering Program Bethune-Cookman University Daytona Beach FL 32114 United States

ISBN: (纸本)9781479911998

In this paper, we present an improved analytic method to the optimal trajectory generation of an autonomous underwater vehicle (AUV) in a dynamic environment. The proposed approach explicitly incorporates both the AUV kinematic and the geometric constraints due to dynamic obstacles and the terrain while rendering the near-shortest path by a performance index related to the path length. In particular, the proposed design is based on a family of parameterized trajectories determined by three adjustable parameters, which provides a unified way to reformulate the geometric constraints and performance index into a set of parameterized constraint equations. To that end, such a constrained optimization problem boils down to optimize those adjustable parameters, which can be analytically solved in the parameter space. The proposed solution enhances the methodologies of real-time path planning for robots in 3D environment. Simulation results verify the effectiveness of the proposed method. © 2013 IEEE.

关键词： Autonomous underwater vehicles

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Application of neuro-fuzzy models in short term electricity load forecast

Application of neuro-fuzzy models in short term electricity ...

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International CSI computer Conference, CSICC

作者： A. R. Koushki M. Nosrati Maralloo C. Lucas A. Kalhor Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department University of Tehran Tehran Iran

One of the important requirements for operational planning of electrical utilities is the prediction of hourly load up to several days, known as short term load forecasting (STLF). Considering the effect of its accuracy on system security and also economical aspects, there is an on-going attention toward putting new approaches to the task. Recently, neuro fuzzy modeling has played a successful role in various applications over nonlinear time series prediction. This paper presents a neuro-fuzzy model for the application of short-term load forecasting. This model is identified through locally liner model tree (LoLiMoT) learning algorithm. The model is compared to a multilayer perceptron and Kohonen classification and intervention analysis. The models are trained and assessed on load data extracted from EUNITE network competition.

关键词： Predictive models Load forecasting Multilayer perceptrons Economic forecasting Power system modeling Power generation economics Fuel economy Artificial neural networks Power system economics Temperature

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Universal enzymatic numerical P systems with small number of enzymatic variables

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Science China(Information Sciences) 2018年第9期61卷 38-49页

作者： Zhiqiang ZHANG Tingfang WU ANDrei P■UN Linqiang PAN Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Automation Huazhong University of Science and Technology Department of Computer Science Faculty of Mathematics and Computer ScienceUniversity of Bucharest Bioinformatics Department National Institute of Research and Development for Biological Sciences School of Electric and Information Engineering Zhengzhou University of Light Industry

Numerical P systems(for short, NP systems) are distributed and parallel computing models inspired from the structure of living cells and economics. Enzymatic numerical P systems(for short, ENP systems) are a variant of NP systems, which have been successfully applied in designing and implementing controllers for mobile robots. Since ENP systems were proved to be Turing universal, there has been much work to simplify the universal systems, where the complexity parameters considered are the number of membranes, the degrees of polynomial production functions or the number of variables used in the *** the number of enzymatic variables, which is essential for ENP systems to reach universality, has not been investigated. Here we consider the problem of searching for the smallest number of enzymatic variables needed for universal ENP systems. We prove that for ENP systems as number acceptors working in the all-parallel or one-parallel mode, one enzymatic variable is sufficient to reach universality; while for the one-parallel ENP systems as number generators, two enzymatic variables are sufficient to reach *** results improve the best known results that the numbers of enzymatic variables are 13 and 52 for the all-parallel and one-parallel systems, respectively.

关键词： bio-inspired computing numerical P system mobile robot membrane controller universality

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Fixation of competing strategies when interacting agents differ in the time scale of strategy updating

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Physical Review E 2016年第3期94卷 032407-032407页

作者： Jianlei Zhang Franz J. Weissing Ming Cao Department of Automation College of Computer and Control Engineering Nankai University People's Republic of China Theoretical Biology Group Centre for Ecological and Evolutionary Studies University of Groningen The Netherlands Network Analysis and Control Group Institute for Industrial Engineering University of Groningen The Netherlands

A commonly used assumption in evolutionary game theory is that natural selection acts on individuals in the same time scale; e.g., players use the same frequency to update their strategies. Variation in learning rates within populations suggests that evolutionary game theory may not necessarily be restricted to uniform time scales associated with the game interaction and strategy adaption evolution. In this study, we remove this restricting assumption by dividing the population into fast and slow groups according to the players' strategy updating frequencies and investigate how different strategy compositions of one group influence the evolutionary outcome of the other's fixation probabilities of strategies within its own group. Analytical analysis and numerical calculations are performed to study the evolutionary dynamics of strategies in typical classes of two-player games (prisoner's dilemma game, snowdrift game, and stag-hunt game). The introduction of the heterogeneity in strategy-update time scales leads to substantial changes in the evolution dynamics of strategies. We provide an approximation formula for the fixation probability of mutant types in finite populations and study the outcome of strategy evolution under the weak selection. We find that although heterogeneity in time scales makes the collective evolutionary dynamics more complicated, the possible long-run evolutionary outcome can be effectively predicted under technical assumptions when knowing the population composition and payoff parameters.

关键词： Complex systems Evolutionary dynamics Social systems Social networks

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The Minimum Principle for Hybrid Systems with Partitioned State Space and Unspecified Discrete State Sequence

The Minimum Principle for Hybrid Systems with Partitioned St...

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2010 49th IEEE Conference on Decision and control

作者： Benjamin Passenberg Peter E. Caines Marion Sobotka Olaf Stursberg Martin Buss Institute of Automatic Control Department of Electrical Engineering and Information Technology Technische Universitat Munchen Munchen Germany Department of Electrical and Computer Engineering and the Center for Intelligent Machines McGill University Montreal Canada Institute of Control and System Theory Department of Electrical Engineering and Computer Science University of Kassel Kassel Germany

ISBN: (纸本)9781424477456

The hybrid minimum principle (HMP) gives necessary conditions to be satisfied for optimal solutions of a hybrid dynamical system. In particular, the HMP accounts for autonomous switching between discrete states that occurs whenever the trajectory hits switching manifolds. In this paper, the existing HMP is extended for hybrid systems with partitioned state space to provide necessary conditions for optimal trajectories that pass through an intersection of switching manifolds. This extension is especially useful for the numerical solution of hybrid optimal control problems as it allows for algorithms with significant reduction of computational complexity. Algorithms based on previous versions of the HMP solve separate optimal control problems for each possible sequence of discrete states. The extension enables us to consider the optimal sequence as subject of optimal control that is varied and finally determined during a single optimization run. A first numerical result illustrates the effectiveness of an algorithm based on the extended HMP.

关键词： state space minimum principle hybrid systems Manifolds Optimal control DYNAMICAL SYSTEMS discrete element

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Panel discussion whitherto robust control 4

Panel discussion whitherto robust control

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4th IFAC Symposium on Robust control Design, ROCOND 2003

作者： Tempo, Roberto Basar, Tamer Khargonekar, Pramod P. Kokotovic, Petar V. Scherer, Carsten W. IEIIT-CNR Politecnico di Torino Italy Coordinated Science Laboratory University of Illinois Urbana-Champaign United States College of Engineering University of Florida Gainesville United States Department of Electrical and Computer Engineering University of California Santa Barbara United States Mechanical Engineering Systems and Control Group Delft University of Technology Netherlands

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A Partial-State Observer for a Class of MIMO Nonlinear Systems

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IFAC Proceedings Volumes 2014年第3期47卷 3676-3681页

作者： Lei Wang Alberto Isidori Hongye Su Weihua Xu State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University 310027 Hangzhou P.R.China Department of Computer Control and Management Engineering University of Rome “La Sapienza” Rome Italy

In this paper, we consider input-affine invertible MIMO nonlinear systems which can be transformed into a special normal form by means of the structure algorithm. The normal form highlights a partial state, a subset of state variables, which plays in this setting a role similar to that of the outputs and its derivatives in a SISO system. It is shown that, if a system in this class can be asymptotically stabilized by means of a static feedback from that partial state, then semiglobal stabilization can be achieved via dynamic feedback driven by the output of the system. The dynamic feedback in question is based a (non–trivial) extension to MIMO systems of the standard high–gain observer.

关键词： MIMO nonlinear systems structure algorithm normal forms minimum-phase systems nonlinear observers

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Generation Undulatory Locomotion of C. elegans in A Crawling Robot via Biomimetic Learning

Generation Undulatory Locomotion of C. elegans in A Crawling...

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IEEE International Conference on control and Automation

作者： Qinyuan Ren Xin Deng Jianxin Xu College of Control Science and Engineering Zhejiang University College of Computer Science and Technology Chongqing University of Posts and Telecommunications Department of Electrical and Computer Engineering National University of Singapore

ISBN: (纸本)9781509017393

This paper investigates generation undulatory locomotion of Caenorhabditis elegans (C. elegans) in a crawling robot via biomimetic learning. Firstly, the anatomy and the morphology of C. elegans are explored. Then, the locomotion of C. elegans is analyzed from cellular level by using dynamic neuron networks (DNNs) and center pattern generations (CPGs). Based on these biological analyses, a biomimetic crawling robot is developed and a digital approximation method is adopted for the robot mimicking the locomotion of C. elegans. Finally, experimental results verify the effectiveness of the robotic system and the motion generation approach.

关键词： Caenorhabditis elegans Locomotion Robot creeping Biomimetics

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An Algorithm for Discrete State Sequence and Trajectory Optimization for Hybrid Systems with Partitioned State Space

An Algorithm for Discrete State Sequence and Trajectory Opti...

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2010 49th IEEE Conference on Decision and control

作者： Benjamin Passenberg Marion Sobotka Olaf Stursberg Martin Buss Peter E. Caines Institute of Automatic Control Department of Electrical Engineering and Information Technology Technische Universitat Munchen Munchen Germany Institute of Control and System Theory Department of Electrical Engineering and Computer Science University of Kassel Kassel Germany Department of Electrical and Computer Engineering and the Center for Intelligent Machines McGill University Montreal Canada

ISBN: (纸本)9781424477456

An algorithm for hybrid optimal control is proposed that varies the discrete state sequence based on gradient information during the search for an optimal trajectory. The algorithm is developed for hybrid systems with partitioned state space. It uses a version of the hybrid minimum principle that allows optimal trajectories to pass through intersections of switching manifolds, which enables the algorithm to vary the sequence. Consequently, the combinatorial complexity of former algorithms can be avoided, since not each possible sequence has to be investigated separately anymore. The convergence of the algorithm is proven and a numerical example demonstrates the efficiency of the algorithm.

关键词： Switches Manifolds Trajectory Complexity theory Partitioning algorithms Convergence Aerospace electronics

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Behavior of task scheduling algorithms with probabilistic module execution times

Behavior of task scheduling algorithms with probabilistic mo...

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IEEE Symposium on computers and Communications (ISCC)

作者： M. Kholief I. Awad M. El-Derini M. Nagi H. Ali Department of Computer Science Old Dominion University Norfolk VA USA Department of Computer Science and Automatic Control Faculty of Engineering Alexandria University Alexandria Egypt

Static task scheduling in distributed computing systems is a very complex problem and known to be NP-hard. This problem is even harder when the module execution times become probabilistic. In this paper we study the effect of probabilistic module execution times on the performance of task-scheduling algorithms. We show that in static task scheduling, for probabilistic module execution times, and in the existence of some factors there is no need to use an expensive task-scheduling algorithm. Given any two static task-scheduling algorithms that use deterministic module execution times in assigning task modules to the distributed system, the performance of these two algorithms will not remain the same when these module execution times become probabilistic rather than deterministic. We also study the effects of some factors an our results.

关键词： Scheduling algorithm Processor scheduling computer science Automatic control Read only memory Distributed computing Heuristic algorithms State-space methods

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