Modeling the Spatial Dynamics of Robotic Manipulators with Flexible Links and Joint Clearances

1993 ◽  
Vol 115 (4) ◽  
pp. 839-847 ◽  
Author(s):  
T. Kakizaki ◽  
J. F. Deck ◽  
S. Dubowsky
Keyword(s):  
Control System ◽  
Spatial Dynamics ◽  
Dynamic Effects ◽  
Nonlinear Functions ◽  
Detailed Model ◽  
Actuator Dynamics ◽  
Dynamics And Control ◽  
Joint Clearances ◽  
And Control ◽  
System Characteristics

A dynamic modeling method is presented for spatial elastic manipulators that can account for a number of their realistic properties, including bearing clearances, actuator dynamics, and control system characteristics. Forces in the bearing clearances are modeled by nonlinear functions of the links’ relative motions and the internal geometry of the connection, or by experimentally measured properties. A detailed model is given for a revolute connection with radial and axial clearances. Results obtained for a SCARA manipulator show that the combined dynamic effects of bearing clearances, link elasticity, and control system characteristics can significantly degrade the system’s performance.

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.

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.

scholarly journals Spatial Dynamics and Control of Cell Differentiation

2011 ◽  
Vol 100 (3) ◽  
pp. 171a
Author(s):  
Olivier Cinquin ◽  
Amanda Cinquin ◽  
Michael Chiang ◽  
Adrian Paz
Keyword(s):  
Cell Differentiation ◽  
Spatial Dynamics ◽  
Dynamics And Control ◽  
And Control

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.

Research of Collaborative Simulation Modeling of High-Speed Machine Tools

2014 ◽  
Vol 608-609 ◽  
pp. 31-35
Author(s):  
Jin Da Zhu ◽  
Li Bing Liu ◽  
Ze Qing Yang
Keyword(s):  
Control System ◽  
Simulation Modeling ◽  
Machine Tools ◽  
Open Architecture ◽  
Cnc Machine Tools ◽  
Cnc Machine ◽  
Collaborative Simulation ◽  
Dynamics And Control ◽  
Domain Models ◽  
And Control

Dynamic effects of machining processing could not be ignored with the increasing speed of CNC machine tools, the analysis of kinematic behavior related to the kinematics, dynamics and control system etc, and there were a lot of coupling relationship of these systems. A simulation of single discipline could not reflect the overall performance with having to the simplification of the other discipline. It proposed a collaborative simulation modeling method based HLA for CNC machine tools. The development process of HLA-based simulation mainly involved system design, high level modeling, domain modeling, transformation of domain models, federal integration. Finally, this paper described an open architecture of collaborative simulation modeling with RTI, and developed the dynamics and control system models using Matlab and Adams software respectively. It provided theoretical support to solve coupling and interaction of the multi domain models of machine tools products in the design and development.

Trajectory Control of an Automated Guided Vehicle Using Feedback Linearization

2006 ◽  
Author(s):  
S. M. Mehdi Ansarey M. ◽  
M. J. Mahjoob
Keyword(s):  
Control System ◽  
Feedback Linearization ◽  
Degrees Of Freedom ◽  
Automated Guided Vehicle ◽  
State Variables ◽  
Discrete Curvatures ◽  
Steering Mechanism ◽  
Dynamics And Control ◽  
And Control ◽  
Three Degrees Of Freedom

In this paper, the dynamics and control of an automated guided vehicle (AGV) is described. The objective is to control the vehicle direction and location with respect to a prescribed trajectory. This is accomplished based on an optimum control strategy using vehicle state variables. A four-wheel vehicle with three degrees of freedom including longitudinal, lateral and yaw motion is considered. The nonlinearity of the tire and steering mechanism is also included. The control system design for circular, straight forward and composite path is presented based on feedback linearization. Some trajectory simulation for discrete curvatures is carried out. The controller was implemented within MATLAB environment. The design was also evaluated using ADAMS full vehicle assembly. The results demonstrated the accuracy of the model and the effectiveness of the developed control system.

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