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Dynamically stable systems: Control,...

Ou, Yongsheng.

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Dynamically stable systems: Control, learning and input selection.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Dynamically stable systems: Control, learning and input selection./
作者:	Ou, Yongsheng.
面頁冊數:	130 p.
附註:	Source: Dissertation Abstracts International, Volume: 65-04, Section: B, page: 2019.
Contained By:	Dissertation Abstracts International65-04B.
標題:	Engineering, Electronics and Electrical. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3128317
ISBN:	0496755803

Dynamically stable systems: Control, learning and input selection.
Ou, Yongsheng.

Dynamically stable systems: Control, learning and input selection. - 130 p.

Source: Dissertation Abstracts International, Volume: 65-04, Section: B, page: 2019.

Thesis (Ph.D.)--The Chinese University of Hong Kong (People's Republic of China), 2004.

In this thesis, we propose to develop methodologies for autonomous control of dynamically stable systems in response to real-time inputs. The problem has significant impact in a variety of applications such as inverted pendulum systems, rolling disks, bicycles, biped robots, hopping robots. In this thesis, we use a single wheel robot-Gyrover, as a typical example and an experimental planform to study this class of systems deeply. This thesis contains two main topics: (1) based on the dynamic model analysis, design and investigate some nonlinear control approaches in balance control and tracking control for Gyrover, (2) study some important issues about building up an autonomous intelligent control strategy based on learning from human expert demonstrations.

ISBN: 0496755803Subjects--Topical Terms:

626636
Engineering, Electronics and Electrical.

Dynamically stable systems: Control, learning and input selection.
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245 1 0 $a Dynamically stable systems: Control, learning and input selection.
300 $a 130 p.
500 $a Source: Dissertation Abstracts International, Volume: 65-04, Section: B, page: 2019.
500 $a Adviser: Xang Sheng Yu.
502 $a Thesis (Ph.D.)--The Chinese University of Hong Kong (People's Republic of China), 2004.
520 $a In this thesis, we propose to develop methodologies for autonomous control of dynamically stable systems in response to real-time inputs. The problem has significant impact in a variety of applications such as inverted pendulum systems, rolling disks, bicycles, biped robots, hopping robots. In this thesis, we use a single wheel robot-Gyrover, as a typical example and an experimental planform to study this class of systems deeply. This thesis contains two main topics: (1) based on the dynamic model analysis, design and investigate some nonlinear control approaches in balance control and tracking control for Gyrover, (2) study some important issues about building up an autonomous intelligent control strategy based on learning from human expert demonstrations.
520 $a The single wheel, gyroscopically stabilized robot---Gyrover, is dynamically stable but statically unstable, with both first-order and second-order nonholonomic constraints. By investigating the dynamics of Gyrover, and analyzing the two classes of nonholonomic constraints of it, we proposed three control laws for balance control, point-to-point control and line tracking in Cartesian space. The experimental works verify the control laws.
520 $a Next, we discuss the problem of how human control strategy can be represented as a parametric model using a support vector machine (SVM), and how an SVM-based controller can be used for dynamically stable systems control. SVM approach has been implemented in a balance control of Gyrover. The experimental results that compare SVM with general ANN approaches clearly demonstrate the superiority of the SVM approach with regard to human control strategy learning.
520 $a Then, we study convergence for the class of intelligent controllers by learning human expert control skills using support vector machines (SVMs). We formulate conditions to verify that the closed-form learning control system is strongly stable under perturbations (SSUP), according to the discrete-time Lyapunov theory. One Chebychev-points-based estimation method is provided to evaluate the convergence quality of this class of SVM learning controllers. One of this class of learning controllers has been implemented in a balance control of a dynamically stable, statically unstable single wheel mobile robot---Gyrover. The experimental results verify the proposed convergence conditions and the theoretical discussions.
520 $a We present a new approach to mitigate the problem of limited training sample size, which can affect the learning accuracy, especially, when the input dimensionality is high. (Abstract shortened by UMI.)
590 $a School code: 1307.
650 4 $a Engineering, Electronics and Electrical. $3 626636
650 4 $a Artificial Intelligence. $3 769149
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710 2 0 $a The Chinese University of Hong Kong (People's Republic of China). $3 1265600
773 0 $t Dissertation Abstracts International $g 65-04B.
790 1 0 $a Yu, Xang Sheng, $e advisor
790 $a 1307
791 $a Ph.D.
792 $a 2004
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