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.

Towards a Minimal Realisable System Dynamics Project Model

2012 ◽  
Vol 5 (1) ◽  
pp. 57-73 ◽  
Author(s):  
A. S. White
Keyword(s):  
Systems Dynamics ◽  
Software Project ◽  
Minimum Level ◽  
Software Projects ◽  
Initial State ◽  
Large Numbers ◽  
Non Linear ◽  
Project Model ◽  
Assimilation Time ◽  
Good Agreement

This paper looks at 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. 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 show the minimum level of complexity required for “good” simulation of project behaviour considering the Abdel-Hamid software project model and three simpler versions. These models give clues to the decision structures that are necessary for good agreement with reality.

scholarly journals Research and development of Q-Baller -- a spherical wheeled robot

2017 ◽  
Author(s):  
◽  
Jiamin Wang
Keyword(s):  
Control System ◽  
Dynamic Models ◽  
Circuit Board ◽  
Embedded Control ◽  
Wheeled Robot ◽  
Dynamics And Control ◽  
Continuous Gain Scheduling ◽  
Machine Interface ◽  
And Control ◽  
And Robotics

The Spherical Wheeled Robot (Ball-Bot) is a family of robots that can maintain balance standing on a ball and use it as its wheel to move around. In recent years, there have been several successful Ball-Bot designs. We attempt to develop a new spherical wheeled robot product named "Q-Baller" to study its dynamics and control system. The Q-Baller has been designed to ahieve the economic and effective prototyping. A detailed dynamic model of the mechatronic system has been established and analyzed. Control studies have been conducted based on the dynamic models, and new control methods has been proposed to realize continuous gain scheduling. Exclusive simulations have been performed to test the performance of the controllers and reference planning. The Q-Baller hardware has been prototyped and functional. Robotic circuit board, human machine interface and embedded control system have also been developed to make up the full robotic system. The Q-Baller prototype will be tested after the system is fully adjusted, and further researches in control and robotics will be conducted in the future.

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.

Kinematics, Dynamics, and Control of Tendon-Driven Mechanisms Using Signal Flow Graphs

1998 ◽  
Author(s):  
Meng-Sang Chew ◽  
Theeraphong Wongratanaphisan
Keyword(s):  
Control System ◽  
Transfer Function ◽  
Closed Loop ◽  
Transfer Functions ◽  
Signal Flow ◽  
Signal Flow Graphs ◽  
Loop Transfer Function ◽  
Dynamics And Control ◽  
Flow Graphs ◽  
And Control

Abstract This paper presents the analysis of the kinematics, dynamics and controls of tendon-driven mechanism under the framework of signal flow graphs. For decades, the signal flow graphs have been applied in many areas, particularly in controls, for determining the closed-loop transfer function of a control system. The tendon-driven mechanism considered here consists of several subsystems including actuator-controller dynamics, mechanism kinematics and mechanism dynamics. Each subsystem will be derived and represented by signal flow graphs. The representation of the whole system can be carried out by connecting the graphs of subsystems at the corresponding nodes. Transfer functions can then be obtained by using Mason’s rules. A 3-DOF robot finger utilizing tendon-driven mechanism is used as an illustrative example.

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