Event Sampled Adaptive Control of Robot Manipulators and Mobile Robot Formations

Abstract

In this chapter, the design of adaptive control of both robot manipulator and consensus-based formation of networked mobile robots in the presence of uncertain robot dynamics and with event-based feedback is presented. The linearity in the unknown parameter (LIP) property is utilized to represent the uncertain nonlinear dynamics of the robotic manipulator which is subsequently employed to generate the control torque with event-sampled measurement update. For the case of consensus-based formation control of mobile robots, by utilizing the LIP based representation of the robot dynamics, an adaptive back-stepping based controller with event-sampled feedback is designed. The networked robots communicate their location and velocity information with their neighbors which is ultimately utilized to derive the desired velocities to move the robots to a required consensus-based formation. The control torque is designed to minimize the velocity tracking error by explicitly taking into account the dynamics of the individual robot and by relaxing the perfect velocity tracking assumption. The Lyapunov stability method is utilized in both the applications to develop an event-sampling condition and to demonstrate the tracking performance of the robot manipulator and consensus of the overall formation of the networked mobile robots. Finally, simulation results are presented to verify theoretical claims and to demonstrate the reduction in the computations with event-sampled control execution.

Department(s)

Electrical and Computer Engineering

Research Center/Lab(s)

Intelligent Systems Center

Keywords and Phrases

Adaptive consensus; Event-sampled control; Lip; Mobile robot formations

International Standard Book Number (ISBN)

978-1-4987-6487-2

Document Type

Book - Chapter

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2016 CRC Press, All rights reserved.

Publication Date

01 Nov 2016

Link to Full Text

Share

 
COinS