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作者： Gao, Chunyan Zhao, Qing Duan, Guang-Ren Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6G 2V4 Canada Center for Control Theory and Guidance Technology Harbin Institute of Technology Harbin 150001 China

ISBN: (纸本)9789881725592

This paper investigates the parameter estimation problem for a type of nonlinear system with multiplicative component faults and model uncertainties. Multiplicative faults are parameter changes within a process. Model uncertainties can be treated as an unknown input by appropriate transformation. An adaptive observer is proposed to estimate the multiplicative faults which are affected by system states and inputs. The component faults triggering oscillatory failures are considered. The proposed design is applied to a hydraulic rig model to verify its effectiveness. © 2011 Chinese Assoc of Automati.

关键词： Uncertainty analysis

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The application of BP Neural Network based on improved PSO in BF temperature forecast

The application of BP Neural Network based on improved PSO i...

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2nd Annual Conference on Electrical and control engineering, ICECE 2011

作者： Wang, Hongjun Li, Dexiong Zhao, Zhuoqun Qi, Huijuan Liu, Lina Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems Tianjin University of Technology Nankai District Tianjin 300191 China HNV College of Mechanical and Vehic Engineering Changsha 410082 China Shijiazhuang Institute of Railway Technology Qiaodong District Shijiazhuang 050041 China

ISBN: (纸本)9781424481637

The BP network has the disadvantages such as low learning efficiency, low speed of convergence, easily falling into the local minimum state, poor ability to adapt, ect. For PSO algorithm, it is fast for convergence, especially at the initial stage, simple for the computing, and is easy to implement. Compared with the genetic algorithms, it does have not the complex operations of hybrid codecs, mutation, so it is a good optimization algorithm. However, PSO algorithm also has some shortcomings it is more and more slow for convergence rate at the late evolution of the algorithm. In this paper, a new BP Neural Network based on improved Particle Swarm Optimization (PSO) is proposed. The convergence speed of this algorithm and the capacity of searching global extremum is increased through adjusting the adaptive capacity of learning factor. The simulation results illustrate that the improved PSO is superior to the standard BP algorithm and particle swarm optimization. © 2011 IEEE.

关键词： Neural networks

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Distributed Positioning of Autonomous Mobile Sensors with Application to Coverage control

Distributed Positioning of Autonomous Mobile Sensors with Ap...

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2011 American control Conference (ACC)

作者： Hans-Bernd Durr Milos S. Stankovic Karl Henrik Johansson Institute for Systems Theory and Automatic Control University of Stuttgart Germany ACCESS Linnaeus Center School of Electrical Engineering Royal Institute of Technology Stockholm Sweden

We consider problems in multi-agent systems where a network of mobile sensors needs to self-organize such that some global objective function is maximized. To deal with the agents' lack of global information we approach the problem in a game-theoretic framework where agents/players are only able to access local measurements of their own local utility functions whose parameters and detailed analytical forms may be unknown. We then propose a distributed and adaptive algorithm, where each agent applies a local extremum seeking feedback adopted to its specific motion dynamics, and prove its global practical stability, implying that the agents asymptotically reach a configuration that is arbitrary close to the globally optimal one. For the stability analysis we introduce a novel methodology based on a Lie bracket trajectory approximation and combine it with a potential game approach. We apply the proposed algorithm to the sensor coverage problem and solve it in a distributed way where the agents do not need any a priori knowledge about the distribution of the events to be detected and about the detection probabilities of the individual agents. The proposed scheme is illustrated through simulations.

关键词： Sensors Games Asymptotic stability Trajectory Mathematical model Stability analysis Convergence

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2D systems based iterative learning control revisited

2D systems based iterative learning control revisited

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2011 International Conference on Advanced Mechatronic Systems, ICAMechS 2011

作者： Hladowski, Lukasz Galkowski, Krzysztof Rogers, Eric Kummert, Anton Institute of Control and Computation Engineering University of Zielona Gora Podgorna 50 Zielona Gora Poland University of Wuppertal Germany School of Electronics and Computer Science University of Southampton Southampton SO17 1BJ United Kingdom Faculty of Electrical Information and Media Engineering Communication Theory University of Wuppertal Germany

ISBN: (纸本)9780955529375

Iterative learning control can be applied to systems that repeat the same task over a finite duration with resetting to the starting point once each one is complete. The idea of iterative learning control is to make use of information from previous executions of the task in order to update the control signal applied during the current execution and thereby sequentially improve performance. This form of control can be treated as a class of 2D systems. In this paper, a critical overview of progress on this application area for 2D systems theory is given, including results from experimental verification on a gantry robot, and discussion of some open research questions. © 2011 Intl Journal of Adv Mechatr.

关键词： Learning algorithms

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Robustness of Prediction Based Delay Compensation for Nonlinear Systems

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IFAC Proceedings Volumes 2011年第1期44卷 203-208页

作者： Rolf Findeisen Lars Grüne Jürgen Pannek Paolo Varutti Otto–von–Guericke–Universität Magdeburg Institute for Automation Engineering Chair for Systems Theory and Automatic Control University of Bayreuth Institute of Mathematics Chair of Applied Math Curtin University of Technology Perth Australia

Abstract control of systems where the information between the controller, actuator, and sensor can be lost or delayed can be challenging with respect to stability and performance. One way to overcome the resulting problems is the use of prediction based compensation schemes. Instead of a single input, a sequence of (predicted) future controls is submitted and implemented at the actuator. If suitable, so-called prediction consistent compensation and control schemes, such as certain predictive control approaches, are used, stability of the closed loop in the presence of delays and packet losses can be guaranteed. In this paper, we show that control schemes employing prediction based delay compensation approaches do posses inherent robustness properties. Specifically, if the nominal closed loop system without delay compensation is ISS with respect to perturbation and measurement errors, then the closed loop system employing prediction based delay compensation techniques is robustly stable. We analyze the influence of the prediction horizon on the robustness gains and illustrate the results in simulation.

关键词： Delay information loss nonlinear stability ISS robustness predictive control

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Event-based NMPC for networked control systems over UDP-like communication channels

Event-based NMPC for networked control systems over UDP-like...

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作者： Varutti, Paolo Findeisen, Rolf Laboratory for Systems Theory and Automatic Control Institute for Automation Engineering Otto-von-Guericke University Magdeburg Germany Intl. Max Planck Res. Sch. for Analysis Des. and Optimization in Chem. and Biochem. Proc. Eng. Magdeburg Germany

ISBN: (纸本)9781457700804

Networked controlled systems have recently received attention from the industry since they allow for flexibility and cost reduction. However, due to the fact that communication media can be subject to random delays, packet dropouts, jitters and other uncertainties, destabilization of the closed loop system can occur. Model predictive control has demonstrated to be a valid solution to cope with these issues. On the other hand, it typically relies on TCP-like (or connection oriented) protocols, i.e. either the received or the lost information is acknowledged. In this work, we propose an event-based model predictive control algorithm for nonlinear continuous time systems subject to state and input constraints which is based on UDP-like communication. We show that without the use of any acknowledgment or error message we can derive a compensation algorithm, which used in combination with the controller, under mild conditions, guarantees closed loop stability. The solution is applied to a continuous stirred tank reactor where an exothermic irreversible reaction takes place. The simulations show the effectiveness of the presented algorithm. © 2011 AACC American Automatic control Council.

关键词： Model predictive control

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Mixed-integer programming for optimal path planning of robotic manipulators

Mixed-integer programming for optimal path planning of robot...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Hao Ding Gunther Reißig Dominic Groß Olaf Stursberg Institute of Control and System Theory Department of Electrical Engineering and ComputerScience University of Kassel Germany Department AeroSpace Engineering University of the Federal Armed Forces Munich Neubiberg Germany

One of the fundamental problems in the field of robotic motion planning is to safely and efficiently drive the end effector of a robotic manipulator to a specified goal position. Here, safety refers to the requirement that the robotic manipulator must have no collision with surrounding obstacles, and efficiency requires that some predefined cost function is minimized. In addition, kinematic and dynamic constraints have to be satisfied. These requirements lead to non-convex optimization problems, which may be approximated by mixed-integer linear programs (MILPs). The solution of the latter, however, is often intolerably complex due to a huge number of binary decision variables. In the present paper, we consider motion planning scenarios with polyhedral obstacles and velocity constraints for the joint positions of the robotic manipulator. We provide a geometric result whose application leads to MILPs with drastically reduced numbers of binary decision variables. Computational efficiency is demonstrated for two- and three-link manipulators interacting with obstacles, where the number of simplex steps during the MILP solution is reduced by a factor of roughly 200 over previous methods. We also demonstrate the application of the proposed method to an industrial robot.

关键词： Joints Collision avoidance End effectors Planning Service robots

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Human arm motion modeling and long-term prediction for safe and efficient Human-Robot-Interaction

Human arm motion modeling and long-term prediction for safe ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Hao Ding Gunther Reißig Kurniawan Wijaya Dino Bortot Klaus Bengler Olaf Stursberg Institute of Control and System Theory Department of Electrical Engineering and Computer Science University of Kassel Germany Institute of Ergonomics Technische Universität München Garching Germany

Modeling and predicting human behavior is indispensable when industrial robots interacting with human operators are to be manipulated safely and efficiently. One challenge is that human operators tend to follow different motion patterns, depending on their intention and the structure of the environment. This precludes the use of classical estimation techniques based on kinematic or dynamic models, especially for the purpose of long-term prediction. In this paper, we propose a method based on Hidden Markov Models to predict the region of the workspace that is possibly occupied by the human within a prediction horizon. In contrast to predictions in the form of single points such as most likely human positions as obtained from previous approaches, the regions obtained here may serve as safety constraints when the robot motion is planned or optimized. This way one avoids collisions with a probability not less than a predefined threshold. The practicability of our method is demonstrated by successfully and accurately predicting the motion of a human arm in two scenarios involving multiple motion patterns.

关键词： Hidden Markov models Humans Robots Safety Predictive models Trajectory Silicon

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Knowledge-based artificial fish-swarm algorithm

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IFAC Proceedings Volumes 2011年第1期44卷 14705-14710页

作者： Ying Wu Xiao-Zhi Gao Kai Zenger Control Theory and Engineering Department Harbin Institute of Technology Harbin 150001 China Department of Automation and Systems Technology Aalto University School of Electrical Engineering Espoo P.O.BOX 15500 FI-00076 Aalto Finland

Abstract In this paper, a knowledge-based Artificial Fish-Swarm (AFA) optimization algorithm with crossover, CAFAC, is proposed to enhance the optimization efficiency and combat the blindness of the search of the AFA. In our CAFAC, the crossover operator is first explored. The knowledge in the Culture Algorithm (CA) is next utilized to guide the evolution of the AFA. Both the normative knowledge and situational knowledge is used to direct the step size as well as direction of the evolution in the AFA. Ten high-dimensional and multi-peak functions are employed to investigate this new algorithm. Numerical simulation results demonstrate that it can indeed outperform the original AFA.

关键词： Artificial Fish-Swarm Algorithm (AFA) Crossover operator Culture Algorithm (CA)

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Stabilization of linear systems with distributed delay and saturation nonlinearity

Stabilization of linear systems with distributed delay and s...

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Chinese control Conference (CCC)

作者： Bin Zhou Huijun Gao Zongli Lin Guang-Ren Duan Center for Control Theory and Guidance Technology Harbin Institute of Technology China Space Control and Inertial Technology Research Center Harbin Institute of Technology China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia U.S.A.

This paper is concerned with stabilization of a linear system with distributed input delay and input saturation. Both constant and time-varying delays are considered. In the case that the input delay is constant, under the stabilizability assumption on an auxiliary system, it is shown that the system can be stabilized by state feedback for an arbitrarily large delay as long as the open-loop system is not exponentially unstable. In the case that the input delay is time-varying, but bounded, it is shown that the system can be stabilized by state feedback if the non-asymptotically stable poles of the open-loop system are all located at the origin. In both cases, stabilizing controllers are explicitly constructed by utilizing the parametric Lyapunov equation based low gain design approach we recently developed. It is also shown that in the presence of actuator saturation and under the same assumptions on the system, these controllers achieve semi-global stabilization. Some discussions on the assumptions we impose on the system are given. A numerical example illustrate the effectiveness of the proposed stabilization approach.

关键词： Delay State feedback Linear systems Delay effects Eigenvalues and eigenfunctions Riccati equations

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