Predictive input delay compensation for motion control systems

This paper presents an analytical approach for the prediction of future motion to be used in input delay compensation of time-delayed motion control systems. The method makes use of the current and previous input values given to a nominally behaving system in order to realize the prediction of the future motion of that system. The generation of the future input is made through an integration which is realized in discrete time setting. Once the future input signal is created, it is used as the reference input of the remote system to enforce an input time delayed system, conduct a delay-free motion. Following the theoretical formulation, the proposed method is tested in experiments and the validity of the approach is verified

TELEOPERATED SYSTEM WITH ACCELEROMETERS FOR DISABILITY

This project involves the implementation of a teleoperated arm using an embedded platform based on a reconfigurable logic device (FPGA) configured and programmed in VHDL for Atmega 328p, using servomotors MG996R brand and a communication terminal with accelerometers, scheduled language C. the system is incompatible with teleoperated robotic autonomy (understood as the case where control and decision making are performed by the robot itself). That is why robots are teleoperated tasks perception of the environment, complex manipulation that are performed by humans and planning, ie, the operator acts in real time closed loop control high level. The evolved systems provide sensory feedback to the operator environment (strength, distance). In this manipulation we used an anthropomorphic arm with automatic controllers that replicate the movements of the operator

Networked control and observation for Master-Slave systems

2009, 350 p. 110 illus., Hardcover. ISBN: 978-0-387-85594-3This chapter concerns the design of a remote control loop constituted by a Slave system (with computing and energy limitations) and a Master computer, communicating via an Internet connection. In such a situation, the communication cost is reduced but the Quality of Service of the Internet connection is not guaranteed. In particular, when the Slave dynamics are expected to be fast enough, the network induces perturbations (delays, jitters, packet dropouts and sampling) that may damage the performance. Here, the proposed solution relies on a delay-dependent, state-feedback control, computed by the Master on the basis of an observer. This last estimates the present Slave's state from its past sampled outputs, despite the various delays. Then, the computing task is concentrated in the Master. The theoretical results are based on the Lyapunov-Krasovskii functional and the approach of LMI, which guarantee the stabilization performance with respect to the expected maximum delay of the connection. Two strategies are applied: one is a constant controller/observer gain strategy, which takes into account a fixed upperbound for the communication delay. The second strategy aims at improving the performance by adapting the gains to the available network QoS (here, with two possible upperbounds)

Stabilization and control of teleoperation systems with time delays

A control scheme for teleoperation systems with time delay is developed based on the concept of passivity. This control method requires neither detailed knowledge of the manipulator systems nor the mathematical models of the environments, and it is applicable for any time delays. The main contribution of this method is that it is less conservative than the traditional passivity based method. In this method, the passivity controller only operates when the system loses passivity, while in a traditional passivity formulation, the controller works at all times during operation and thus adversely affect the performance of the system.;Using the proposed control scheme, a sub-system is defined that is composed of the communication channel, slave robot and the manipulated environment. This sub system is treated as a one-port network component, and passivity theory is applied to this component to assure stability. The energy flowing into the one-port network, in the form of the control command and the force feedback, is monitored. A passivity regulator is activated to maintain the passivity of the network by modifying the feedback force to the master, and thus adjust the energy exchange between the master and the communication channel.;When this method is applied, only the information at the interface between the master manipulator and the communication channel is collected and observed, there is no need for accurate or detailed knowledge of the structure or timing of the communication channel. The method can make the system lossless regardless of the feedback force, the coordinating force controlling the slave joint motions or the contact force. The approach can stabilize the system regardless of the time delay, discontinuities with environmental contact, or discretization of the physical plant. It will pose no problem when the environmental contact force is directly fed back. The results of this work show that it is advantageous to use the measured environmental force as the feedback, providing superior performance for free motion and more realistic haptic feedback for the operator from the remote environment.;Simulation and experimental results are presented to verify the proposed control scheme

A switched system approach to exponential stabilization through communication network

International audienceThe paper considers a networked control loop, where the plant is a "slave" part, and the remote controller and observer constitute the "master". Since the performance of Networked Control Systems (NCS) depends on the Quality of Service (QoS) available from the network, it is worth to design a controller that takes into account qualitative information on the QoS in realtime. The goal of the design is to provide a controller that guarantees two things: 1) high performances (here expressed by exponential decay rates) when the QoS remains globally the same; 2) global stability when the QoS changes. In order to guarantee the global stability, the controller will switch by respecting a dwell time constraint. The dwell time parameters are obtained by using the switched system theories and the obtained conditions are Linear Matrix Inequalities (LMI). An experiment illustrates how the controller can be implemented for a control over Internet application (remote control of a small robot)

Teleoperación [de robots]: técnicas, aplicaciones, entorno sensorial y teleoperación inteligente

En este trabajo centraremos la atención en los sistemas robóticos teleoperados, especialmente analizaremos los sistemas teleoperados desde internet, veremos una clasificación de las metodologías de teleoperación, los diferentes sistemas de control y daremos una visión del estado del arte en este ámbito de conocimiento

Cooperative control of multi-robot system with force reflecting via internet

Lo Wang Tai.Thesis (M.Phil.)--Chinese University of Hong Kong, 2002.Includes bibliographical references (leaves 58-63).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.iiiTables of Content --- p.ivList of Figures --- p.viiList of Tables --- p.viiiChapter Chapter1 --- Introduction --- p.1Chapter 1.1 --- Internet-based Tele-cooperation --- p.1Chapter 1.1.1 --- Cooperative Control of Multiple Robot --- p.1Chapter 1.1.2 --- Internet-based Teleoperation --- p.3Chapter 1.1.3 --- Time Delay of Internet Communication --- p.4Chapter 1.2 --- Related Work --- p.5Chapter 1.3 --- Motivation and Contribution --- p.6Chapter 1.3.1 --- Motivation --- p.6Chapter 1.3.2 --- Contribution --- p.7Chapter 1.4 --- Outline of the thesis --- p.8Chapter Chapter2 --- The Internet Robotic System --- p.9Chapter 2.1 --- System Architecture --- p.9Chapter 2.2 --- The Hardware --- p.12Chapter 2.2.1 --- Operator System --- p.12Chapter 2.2.2 --- Mobile Robot System --- p.13Chapter 2.2.3 --- Multi-fingered Robot Hand System --- p.17Chapter 2.2.4 --- Visual Tracking System --- p.19Chapter 2.3 --- Software Design --- p.21Chapter 2.3.1 --- Robot Client and Arm Client --- p.22Chapter 2.3.2 --- Robot Server --- p.23Chapter 2.3.3 --- Image Server --- p.25Chapter 2.3.4 --- Arm Server --- p.75Chapter 2.3.5 --- Arm Controller --- p.27Chapter 2.3.6 --- Finger Server --- p.27Chapter 2.3.7 --- Finger Controller --- p.27Chapter 2.3.8 --- Robot Tracker --- p.28Chapter 2.3.9 --- Interaction Forwarder --- p.28Chapter Chapter3 --- Event-based Control for Force Reflecting Teleoperation --- p.29Chapter 3.1 --- Modeling and Control --- p.29Chapter 3.1.1 --- Model of Operator System --- p.31Chapter 3.1.2 --- Model of Mobile Robot System --- p.33Chapter 3.1.3 --- Model of Multi-fingered Hand System --- p.34Chapter 3.2 --- Force Feedback Generation --- p.35Chapter 3.2.1 --- Obstacle Avoidance --- p.35Chapter 3.2.2 --- Singularity Avoidance --- p.38Chapter 3.2.3 --- Interaction Rendering --- p.40Chapter Chapter4 --- Experiments --- p.42Chapter 4.1 --- Experiment1 --- p.42Chapter 4.2 --- Experiment2 --- p.47Chapter 4.3 --- Experiment3 --- p.52Chapter Chapter5 --- Future Wok --- p.54Chapter Chapter6 --- Conclusions --- p.56Bibliography --- p.5