Optimized Adaptive Tracking Control for an Underactuated Vibro-Driven Capsule System

Liu, Pengcheng and Yu, Hongnian and Cang, Shuang (2018) Optimized Adaptive Tracking Control for an Underactuated Vibro-Driven Capsule System. Nonlinear Dynamics . ISSN 0924-090X

Full content URL: https://doi.org/10.1007/s11071-018-4458-9

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Optimized Adaptive Tracking Control for an Underactuated Vibro-Driven Capsule System
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Optimized adaptive tracking control for an underactuated vibro-driven capsule system
https://doi.org/10.1007/s11071-018-4458-
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Item Type:Article
Item Status:Live Archive

Abstract

This paper studies the issue of adaptive trajectory tracking for an underactuated
vibro-driven capsule system and presents a novel motion generation framework. In this
framework, feasible motion trajectory is derived through investigating dynamic constraints
and kernel control indexes that underlie the underactuated dynamics. Due to the
underactuated nature of the capsule system, the global motion dynamics cannot be directly
controlled. The main objective of optimization is to indirectly control the friction-induced
stick-slip motions to reshape the passive dynamics and by doing so, to obtain optimal system
performance in terms of average speed and energy efficacy. Two tracking control schemes
are designed through a closed-loop feedback linearization approach and an adaptive variable
structure method with an auxiliary control variable, respectively. The reference model is accurately matched in a finite-time horizon. The key point is to define an exogenous state variable whose dynamics is employed as a control input. The tracking performance and system stability are investigated through rigorous theoretic analysis. Extensive simulation studies are conducted to demonstrate the effectiveness and feasibility of the developed trajectory model and optimized adaptive control system.

Keywords:Autonomous robots, Control Systems, Optimization
Subjects:H Engineering > H660 Control Systems
H Engineering > H671 Robotics
Divisions:College of Science > School of Computer Science
ID Code:32539
Deposited On:02 Jul 2018 14:28

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