NON-LINEAR DYNAMICS AND CONTROL OF CHAOS FOR A TACHOMETER

2002 ◽  
Vol 253 (4) ◽  
pp. 773-793 ◽  
Author(s):  
Z.-M. GE ◽  
J.-S. SHIUE
Keyword(s):  
Control Of Chaos ◽  
Linear Dynamics ◽  
Non Linear ◽  
Dynamics And Control ◽  
And Control

Synchronization, Non-linear Dynamics and Control

2018 ◽  
Author(s):  
Ginestra Bianconi
Keyword(s):  
Multivariate Time Series ◽  
Network Control ◽  
Structural Constraints ◽  
Multilayer Networks ◽  
Linear Dynamics ◽  
Multiplex Network ◽  
Non Linear ◽  
Dynamics And Control ◽  
The Stability ◽  
And Control

This chapter is entirely devoted to characterizing non-linear dynamics on multilayer networks. Special attention is given to recent results on the stability of synchronization that extend the Master Stability Function approach to the multilayer networks scenario. Discontinous synchronization transitions on multiplex networks recently reported in the literature are also discussed, and their application discussed in the context of brain networks. This chapter also presents an overview of the major results regarding pattern formation in multilayer networks, and the proposed characterization of multivariate time series using multiplex visibility graphs. Finally, the chapter discusses several approaches for multiplex network control where the dynamical state of a multiplex network needs to be controlled by eternal signals placed on replica nodes satisfying some structural constraints.

Simple System Dynamics and Control System Project Models

2014 ◽  
pp. 113-133
Author(s):  
A. S. White
Keyword(s):  
Control System ◽  
Software Project ◽  
Software Projects ◽  
Initial State ◽  
Control Models ◽  
Non Linear ◽  
Dynamics And Control ◽  
Control System Model ◽  
System Project ◽  
And Control

This chapter examines the established Systems Dynamics (SD) methods applied to software projects in order to simplify them. These methods are highly non-linear and contain large numbers of variables and built-in decisions. A SIMULINK version of an SD model is used here and conclusions are made with respect to the initial main controlling factors, compared to a NASA project. Control System methods are used to evaluate the critical features of the SD models. The eigenvalues of the linearised system indicate that the important factors are the hiring delay time, the assimilation time, and the employment time. This illustrates how the initial state of the system is at best neutrally stable with control only being achieved with complex non-linear decisions. The purpose is to compare the simplest SD and control models available required for “good” simulation of project behaviour with the Abdel-Hamid software project model. These models give clues to the decision structures that are necessary for good agreement with reality. The final simplified model, with five states, is a good match for the prime states of the Abdel-Hamid model, the NASA data, and compares favourably to the Ruiz model. The linear control system model has a much simpler structure, with the same limitations. Both the simple SD and control models are more suited to preliminary estimates of project performance.

On the Dynamics and Control of Robotic Manipulators with an Automatic Balancing Mechanism

1987 ◽  
Vol 201 (1) ◽  
pp. 25-34 ◽  
Author(s):  
W K Chung ◽  
H S Cho
Keyword(s):  
Control Algorithm ◽  
Controller Design ◽  
Torque Control ◽  
Manipulator Dynamics ◽  
Wide Range ◽  
Non Linear ◽  
Dynamics And Control ◽  
Characteristic Features ◽  
Automatic Balancing ◽  
And Control

Non-linear characteristics and uncertainty in manipulator dynamics caused by payload effects are major hurdles in controller design. To overcome such hurdles the authors have introduced an automatic balancing concept which has been proved to reduce the non-linear complexity in manipulator dynamics as well as to remove gravity loading. This paper examines the characteristic features of balanced manipulator dynamics in more detail and presents an efficient control algorithm suitable for the dynamics. Since the dynamics of a balanced manipulator are characterized by partially configuration-independent inertial properties, the present algorithm adopts two different control concepts ‘the computed torque control’ for the joint having coupled, configuration-dependent inertia and ‘an optimal constant feedback control’ for the joints having configuration-independent inertia. To evaluate the proposed control algorithm, simulation studies were made over a wide range of manipulator speeds and payloads. Based upon the simulation results, the efficiency of the controller is discussed in detail.

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