Control Theory of Digitally Networked Dynamic Systems

2014 ◽  
Keyword(s):  
Control Theory ◽  
Dynamic Systems ◽  
Networked Dynamic Systems

Control Theory, Dynamics, and Continuous Interaction

2018 ◽  
Author(s):  
Roderick Murray-Smith
Keyword(s):  
Control Theory ◽  
Human Computer Interaction ◽  
Systems Theory ◽  
Dynamic Systems ◽  
Trajectory Generation ◽  
Interaction Techniques ◽  
Dynamic Systems Theory ◽  
Classical Work ◽  
Continuous Interaction

This chapter reviews the role of theory and dynamic systems theory for understanding common interaction techniques including: targetting, trajectory generation, panning, scrolling and zooming. It explains how can be seen to be at the foundations of Human–Computer Interaction and might be essential for making progress in novel forms of interface. It reinterprets Fitts’ classical work with theoretic tools. It also highlights the limitations of theory for design of human–computer loops.

A parametric family of Massey-type methods: inference, prediction, and sensitivity

2020 ◽  
Vol 16 (3) ◽  
pp. 255-269
Author(s):  
Enrico Bozzo ◽  
Paolo Vidoni ◽  
Massimo Franceschet
Keyword(s):  
Quantitative Analysis ◽  
Control Theory ◽  
Dynamic Systems ◽  
Parametric Family ◽  
Time Varying ◽  
Multi Agent ◽  
Key Features ◽  
The Stability ◽  
Agent Coordination ◽  
Time Aware

AbstractWe study the stability of a time-aware version of the popular Massey method, previously introduced by Franceschet, M., E. Bozzo, and P. Vidoni. 2017. “The Temporalized Massey’s Method.” Journal of Quantitative Analysis in Sports 13: 37–48, for rating teams in sport competitions. To this end, we embed the temporal Massey method in the theory of time-varying averaging algorithms, which are dynamic systems mainly used in control theory for multi-agent coordination. We also introduce a parametric family of Massey-type methods and show that the original and time-aware Massey versions are, in some sense, particular instances of it. Finally, we discuss the key features of this general family of rating procedures, focusing on inferential and predictive issues and on sensitivity to upsets and modifications of the schedule.

Control of General Dynamic Systems With Periodically Varying Parameters Via Liapunov-Floquet Transformation

1994 ◽  
Vol 116 (4) ◽  
pp. 650-658 ◽  
Author(s):  
S. C. Sinha ◽  
P. Joseph
Keyword(s):  
Control Theory ◽  
Dynamic Systems ◽  
Pole Placement ◽  
Transition Matrices ◽  
Varying Coefficients ◽  
Full State ◽  
A New Technique ◽  
Original Time ◽  
Linear Periodic Systems ◽  
Time Invariant

A new technique in the design of controllers for linear dynamic systems with periodically varying coefficients is presented. The idea is to utilize the well-known Liapunov-Floquet (L-F) transformation such that the original time-varying system can be reduced to a form suitable for the application of standard time-invariant methods of control theory. For this purpose, a procedure for computing the L-F matrices for general linear periodic systems is outlined. In this procedure, the state transition matrices are expressed in terms of Chebyshev polynomials which permits the computation of L-F matrices as explicit functions of time. Further, it is shown that controllers can be designed in the transformed domain via full state or observer based feedback using principles of pole placement and optimal control theory. The effectiveness of the proposed technique is demonstrated through two examples. The first example belongs to the class of commutative systems while in the second example a triple inverted pendulum subjected to a periodic follower load is considered. It is found that both types of controllers can be successfully designed.

scholarly journals Opacité des artefacts d'un système workflow

2014 ◽  
Vol Volume 17 - 2014 - Special... ◽  
Author(s):  
Eric Badouel ◽  
Mohamadou Lamine Diouf
Keyword(s):  
Control Theory ◽  
Dynamic Systems ◽  
Discrete Event ◽  
Discrete Event Dynamic Systems ◽  
Workflow Systems ◽  
Nous Dirons ◽  
International Audience ◽  
Event Dynamic

International audience A property (of an object) is opaque to an observer when he or she cannot deduce the property from its set of observations. If each observer is attached to a given set of properties (the so-called secrets), then the system is said to be opaque if each secret is opaque to the corresponding observer. Opacity has been studied in the context of discrete event dynamic systems where technique of control theory were designed to enforce opacity. To the best of our knowledge, this paper is the first attempt to formalize opacity of artifacts in data-centric workflow systems. We motivate this problem and give some assumptions that guarantee the decidability of opacity. Some techniques for enforcing opacity are indicated. Une propriété d'un objet est dite opaque pour un observateur si celui-ci ne peut déduire que la propriété est satisfaite sur la base de l'observation qu'il a de cet objet. Supposons qu'un certain de nombre de propriétés (appelées secrets) soient attachées à chaque intervenant d'un système, nous dirons alors que le système lui-même est opaque si chaque secret d'un observateur lui est opaque: il ne peut percer aucun des secrets qui lui ont été attachés. L'opacité a été étudiée préalablement dans le contexte des systèmes à événements discrets où différents jeux d'hypothèses ont pu être identifiés pour lesquels on pouvait d'une part décider de l'opacité d'un système et d'autre part développer des techniques pour diagnostiquer et/ou forcer l'opacité. Ce papier constitue, à notre connaissance, la première contribution au problème de l'opacité des artefacts d'un système à flots de tâches (système workflow). Notre propos est par conséquent de formaliser ce problème en dégageant les hypothèses qui doivent être posées sur ces systèmes pour que l'opacité soit décidable. Nous indiquons quelques techniques pour assurer l'opacité d'un système.

Estimation and Control Theory: Application to Modeling Human Behavior

1977 ◽  
Vol 19 (4) ◽  
pp. 315-329 ◽  
Author(s):  
William B. Rouse ◽  
Daniel Gopher
Keyword(s):  
Control Theory ◽  
Human Behavior ◽  
Dynamic Systems ◽  
Optimal Estimation ◽  
Linear Dynamic Systems ◽  
Discrete Time Systems ◽  
Theory Application ◽  
Estimation And Control ◽  
And Control ◽  
Time Systems

The methodology of estimation and control theory is considered in terms of response, stability, estimation, and control of linear dynamic systems. Within the context of discrete-time systems, multi-input, multi-output, nth-order linear systems are discussed, and general results for optimal estimation, optimal control, and other topics are presented. The application of these results to modeling human behavior is considered with special emphasis on man-machine system models.

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