Control theory
11,367 resources related to Control theory
IEEE Organizations related to Control theory
Back to TopConferences related to Control theory
Back to Top2018 15th International Workshop on Advanced Motion Control (AMC)
1. Advanced Motion Control2. Haptics, Robotics and HumanMachine Systems3. Micro/Nano Motion Control Systems4. Intelligent Motion Control Systems5. Nonlinear, Adaptive and Robust Control Systems6. Motion Systems for Robot Intelligence and Humanoid Robotics7. CPG based Feedback Control, Morphological Control8. Actuators and Sensors in Motion System9. Motion Control of Aerial/Ground/Underwater Robots10. Advanced Dynamics and Motion Control11. Motion Control for Assistive and Rehabilitative Robots and Systems12. Intelligent and Advanced Traffic Controls13. Computer Vision in Motion Control14. Network and Communication Technologies in Motion Control15. Motion Control of Soft Robots16. Automation Technologies in Primary Industries17. Other Topics and Applications Involving Motion Dynamics and Control
2018 IEEE 18th International Power Electronics and Motion Control Conference (PEMC)
Promote and coordinate the exchange and the publication of technical, scientific and economic information in the field of Power Electronics and Motion Control with special focus on countries less involved in IEEE related activities. The main taget is to create a forum for industrial and academic community.
2018 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES)
The conference will deal will all aspects of power electronics, motor drives and Power electronics applications to energy systems.
2017 10th Global Symposium on MillimeterWaves (GSMM)
The main theme of the symposium is MillimeterWave and Terahertz Sensing and Communications. It covers millimeter wave and THz antennas, circuits, devices, systems and applications.
2017 17th International Conference on Control, Automation and Systems (ICCAS)
Control Theory and Applications,Control Devices and Instruments,Industrial Applications of Control,Sensors and Signal Processing,Artificial Intelligent Systems,Autonomous Vehicle Systems, Navigation, Guidance and Control,Biomedical Instruments and Systems,Information and Networking,Multimedia Systems,Process Control Systems,Civil and Urban Control Systems Human Robot Interaction,Robot Mechanism and Control,Robot Vision,Exoskeletal Robot,Intelligent Robot and Service Robot,Robotic Applications
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Periodicals related to Control theory
Back to TopAntennas and Propagation, IEEE Transactions on
Experimental and theoretical advances in antennas including design and development, and in the propagation of electromagnetic waves including scattering, diffraction and interaction with continuous media; and applications pertinent to antennas and propagation, such as remote sensing, applied optics, and millimeter and submillimeter wave techniques.
Automatic Control, IEEE Transactions on
The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...
Broadcasting, IEEE Transactions on
Broadcast technology, including devices, equipment, techniques, and systems related to broadcast technology, including the production, distribution, transmission, and propagation aspects.
Communications, IEEE Transactions on
Telephone, telegraphy, facsimile, and pointtopoint television, by electromagnetic propagation, including radio; wire; aerial, underground, coaxial, and submarine cables; waveguides, communication satellites, and lasers; in marine, aeronautical, space and fixed station services; repeaters, radio relaying, signal storage, and regeneration; telecommunication error detection and correction; multiplexing and carrier techniques; communication switching systems; data communications; and communication theory. In addition to the above, ...
Computers, IEEE Transactions on
Design and analysis of algorithms, computer systems, and digital networks; methods for specifying, measuring, and modeling the performance of computers and computer systems; design of computer components, such as arithmetic units, data storage devices, and interface devices; design of reliable and testable digital devices and systems; computer networks and distributed computer systems; new computer organizations and architectures; applications of VLSI ...
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Xplore Articles related to Control theory
Back to TopAdaptive control of Markov chains, I: Finite parameter set
V. Borkar; P. Varaiya IEEE Transactions on Automatic Control, 1979
Consider a controlled Markov chain whose transition probabilities depend upon an unknown parameter α taking values in finite setA. To each α is associated a prespecified stationary control lawphi(alpha). The adaptive control law selects at each timetthe control action indicated byphi(alpha_{t})where αtis the maximum likelihood estimate of α. It is shown that αtconverges to a parameter α*such that the "closedloop" ...
Observations on the role of influence in the difficulty of social network control
Dave McKenney; Tony White 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2016
Previous work introducing the idea of distributionbased network control determined that some seemingly similar networks can have significantly different levels of controllability. This work investigates these differences in controllability in more detail and finds that one of the driving factors behind controllability may be the influence dynamics within the network. These results suggest that existing structural heuristics for control set ...
C. G. Quintero M; J. L. de la Rosa; J. Vehi 2005 International Symposium on Computational Intelligence in Robotics and Automation, 2005
This paper shows the impact of the atomic capabilities concept to include controloriented knowledge of linear control systems in the decisions making structure of physical agents. These agents operate in a real environment managing physical objects (e.g. their physical bodies) in coordinated tasks. This approach is presented using an introspective reasoning approach and control theory based on the specific tasks ...
An algorithm for inverting linear dynamic systems
W. Porter IEEE Transactions on Automatic Control, 1969
An algorithm is presented for inverting transformations associated with linear dynamic systems. The induced inverse system, when it exists, operates on outputs and generates the corresponding inputs of the system to which it is inverse. The applications for inverse systems are found in such diverse areas as control, coding, filtering, and sensitivity analysis.
Elena Zattoni; Giovanni Marro 2015 54th IEEE Conference on Decision and Control (CDC), 2015
This work deals with compensation of inaccessible or measurable disturbances in a class of switching systems: i.e., those defined by a set of discretetime linear timeinvariant modes and a switching signal satisfying a minimum dwell time constraint. In more detail, the problem consists in finding a mode dependent, switching compensation scheme that guarantees zero output asymptotically, for any admissible disturbance, ...
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Educational Resources on Control theory
Back to TopeLearning
Adaptive control of Markov chains, I: Finite parameter set
V. Borkar; P. Varaiya IEEE Transactions on Automatic Control, 1979
Consider a controlled Markov chain whose transition probabilities depend upon an unknown parameter α taking values in finite setA. To each α is associated a prespecified stationary control lawphi(alpha). The adaptive control law selects at each timetthe control action indicated byphi(alpha_{t})where αtis the maximum likelihood estimate of α. It is shown that αtconverges to a parameter α*such that the "closedloop" ...
Observations on the role of influence in the difficulty of social network control
Dave McKenney; Tony White 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2016
Previous work introducing the idea of distributionbased network control determined that some seemingly similar networks can have significantly different levels of controllability. This work investigates these differences in controllability in more detail and finds that one of the driving factors behind controllability may be the influence dynamics within the network. These results suggest that existing structural heuristics for control set ...
C. G. Quintero M; J. L. de la Rosa; J. Vehi 2005 International Symposium on Computational Intelligence in Robotics and Automation, 2005
This paper shows the impact of the atomic capabilities concept to include controloriented knowledge of linear control systems in the decisions making structure of physical agents. These agents operate in a real environment managing physical objects (e.g. their physical bodies) in coordinated tasks. This approach is presented using an introspective reasoning approach and control theory based on the specific tasks ...
An algorithm for inverting linear dynamic systems
W. Porter IEEE Transactions on Automatic Control, 1969
An algorithm is presented for inverting transformations associated with linear dynamic systems. The induced inverse system, when it exists, operates on outputs and generates the corresponding inputs of the system to which it is inverse. The applications for inverse systems are found in such diverse areas as control, coding, filtering, and sensitivity analysis.
Elena Zattoni; Giovanni Marro 2015 54th IEEE Conference on Decision and Control (CDC), 2015
This work deals with compensation of inaccessible or measurable disturbances in a class of switching systems: i.e., those defined by a set of discretetime linear timeinvariant modes and a switching signal satisfying a minimum dwell time constraint. In more detail, the problem consists in finding a mode dependent, switching compensation scheme that guarantees zero output asymptotically, for any admissible disturbance, ...
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IEEEUSA EBooks

Foundations of Robotics presents the fundamental concepts and methodologies for the analysis, design, and control of robot manipulators. It explains the physical meaning of the concepts and equations used, and it provides, in an intuitively clear way, the necessary background in kinetics, linear algebra, and control theory. Illustrative examples appear throughout.The author begins by discussing typical robot manipulator mechanisms and their controllers. He then devotes three chapters to the analysis of robot manipulator mechanisms. He covers the kinematics of robot manipulators, describing the motion of manipulator links and objects related to manipulation. A chapter on dynamics includes the derivation of the dynamic equations of motion, their use for control and simulation and the identification of inertial parameters. The final chapter develops the concept of manipulability.The second half focuses on the control of robot manipulators. Various positioncontrol algorithms that guide the manipulator's end effector along a desired trajectory are described Two typical methods used to control the contact force between the end effector and its environments are detailed For manipulators with redundant degrees of freedom, a technique to develop control algorithms for active utilization of the redundancy is described. Appendixes give compact reviews of the function atan2, pseudo inverses, singularvalue decomposition, and Lyapunov stability theory.Tsuneo Yoshikawa teaches in the Division of Applied Systems Science in Kyoto University's Faculty of Engineering.

Solutions to Selected Exercises
Foundations of Robotics presents the fundamental concepts and methodologies for the analysis, design, and control of robot manipulators. It explains the physical meaning of the concepts and equations used, and it provides, in an intuitively clear way, the necessary background in kinetics, linear algebra, and control theory. Illustrative examples appear throughout.The author begins by discussing typical robot manipulator mechanisms and their controllers. He then devotes three chapters to the analysis of robot manipulator mechanisms. He covers the kinematics of robot manipulators, describing the motion of manipulator links and objects related to manipulation. A chapter on dynamics includes the derivation of the dynamic equations of motion, their use for control and simulation and the identification of inertial parameters. The final chapter develops the concept of manipulability.The second half focuses on the control of robot manipulators. Various positioncontrol algorithms that guide the manipulator's end effector along a desired trajectory are described Two typical methods used to control the contact force between the end effector and its environments are detailed For manipulators with redundant degrees of freedom, a technique to develop control algorithms for active utilization of the redundancy is described. Appendixes give compact reviews of the function atan2, pseudo inverses, singularvalue decomposition, and Lyapunov stability theory.Tsuneo Yoshikawa teaches in the Division of Applied Systems Science in Kyoto University's Faculty of Engineering.

Relations Between Attenuation and Phase in Feedback Amplifier Design
This chapter contains sections titled: References Introduction Relations Between Attenuation and Phase in Physical Networks Overall Feedback Loop Characteristics Maximum Obtainable Feedback Relative Importance of Tubes and Circuit in Limiting Feedback Amplifiers of Other Types Example

Foundations of Robotics presents the fundamental concepts and methodologies for the analysis, design, and control of robot manipulators. It explains the physical meaning of the concepts and equations used, and it provides, in an intuitively clear way, the necessary background in kinetics, linear algebra, and control theory. Illustrative examples appear throughout.The author begins by discussing typical robot manipulator mechanisms and their controllers. He then devotes three chapters to the analysis of robot manipulator mechanisms. He covers the kinematics of robot manipulators, describing the motion of manipulator links and objects related to manipulation. A chapter on dynamics includes the derivation of the dynamic equations of motion, their use for control and simulation and the identification of inertial parameters. The final chapter develops the concept of manipulability.The second half focuses on the control of robot manipulators. Various positioncontrol algorithms that guide the manipulator's end effector along a desired trajectory are described Two typical methods used to control the contact force between the end effector and its environments are detailed For manipulators with redundant degrees of freedom, a technique to develop control algorithms for active utilization of the redundancy is described. Appendixes give compact reviews of the function atan2, pseudo inverses, singularvalue decomposition, and Lyapunov stability theory.Tsuneo Yoshikawa teaches in the Division of Applied Systems Science in Kyoto University's Faculty of Engineering.

The systematic design of feedback systems requires an ability to quantify the effect of control inputs (e.g., buffer size) on measured outputs (e.g., response times), both of which may vary with time. Indeed, developing such models is at the heart of applying control theory in practice. In this chapter we introduce linear difference equations to model the dynamics of computing systems and employ insights from queueing theory to construct such models. We discuss briefly how difference equations can be constructed from first principles. Our focus, however, is to construct models using statistical or blackbox methods, a process that is referred to as system identification.

The Linear Filter for a Single Time Series
This chapter contains sections titled: References Formulation of the General Filter Problem Minimization Problem for Filters The Factorization of the Spectrum Prediction and Filtering The Error of Performance of a Filter; LongLag Filters Filters and Ergodic Theory Computation of Specific Filter Characteristics Lagging Filters The Determination of Lag and Number of Meshes in a Filter Detecting Filters for High Noise Level Filters for Pulses Filters Having Characteristics Linearly Dependent on Given Characteristics Computation of Filter: RÃ©umÃ©

Evaluation of the Robustness of Visual Behaviors through Performance Characterization
The robustness of visual behaviors implemented in an active vision system depends both on the vision algorithms and control structure. To improve robustness an evaluation of the performance of the several system algorithms must be done. Performance analysis can be done within the framework of control theory because notions such as stability and controllability can contribute to a better understanding of the algorithms and architectures weaknesses. In this chapter we discuss the generation of reference test target trajectories and we characterize the performance of smooth pursuit and vergence. The responses to motion are used to accurately identify implementation problems and possible optimizations. The system evaluation leads to solutions to enhance the global performance and robustness.

Acquire the tools for understanding new architectures and algorithms of dynamical recurrent networks (DRNs) from this valuable field guide, which documents recent forays into artificial intelligence, control theory, and connectionism. This unbiased introduction to DRNs and their application to timeseries problems (such as classification and prediction) provides a comprehensive overview of the recent explosion of leading research in this prolific field. A Field Guide to Dynamical Recurrent Networks emphasizes the issues driving the development of this class of network structures. It provides a solid foundation in DRN systems theory and practice using consistent notation and terminology. Theoretical presentations are supplemented with applications ranging from cognitive modeling to financial forecasting. A Field Guide to Dynamical Recurrent Networks will enable engineers, research scientists, academics, and graduate students to apply DRNs to various realworld problems and learn about different areas of active research. It provides both stateoftheart information and a road map to the future of cuttingedge dynamical recurrent networks.

ContinuousTime Model Reference Adaptive Control
This chapter deals with continuoustime model reference adaptive control using output feedback for output tracking. The key components of model reference adaptive control theory  a priori plant knowledge, controller structure, plantmodel matching, adaptive laws, stability, robustness, and robust adaptation  are addressed in a comprehensive formation and, in particular, stability and robustness analysis is given in a simplified framework. The plantmodel matching equation for a standard model reference controller structure is studied in a tutorial formula. Design and analysis of model reference adaptive control schemes are given for plants with relative degree 1 or larger, using a Lyapunov or gradient method based on a standard quadratic or nonquadratic cost function. Robust adaptive control is formulated and solved in a compact framework. Assumptions on plant unmodeled dynamics are clarified, and robust adaptive laws are analyzed. Closedloop signal boundedness and mean tracking error properties are proved. To develop adaptive control schemes without using the sign of the high frequency gain of the controlled plant, a modified controller parametrization leads to a framework of adaptive control using a Nussbaum gain for stable parameter adaptation and closedloop stability and asymptotic output tracking.

The purpose of this paper is to study questions regarding controllability, observability, and realization theory for a particular class of systems for which the state space is a differentiable manifold which is simultaneously a group or, more generally, a coset space. We show that it is possible to give rather explicit expressions for the reachable set and the set of indistinguishable states in the case of autonomous systems. We also establish a type of state space isomorphism theorem. These results parallel, and in part specialize to, results available for the familiar case described by x(t) = Ax(t) + Bu(t), y(t) = Cx(t). Our objective is to reduce all questions about the system to questions about Lie algebras generated from the coefficient matrices entering in the description of the system and in that way arrive at conditions which are easily visualized and tested.
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