東華大學圖書館 |

Inversion-Based Control Tools for High-Speed Precision Tracking/Transition in Emerging Applications.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Inversion-Based Control Tools for High-Speed Precision Tracking/Transition in Emerging Applications./
作者:	Wang, Haiming.
面頁冊數:	142 p.
附註:	Source: Dissertation Abstracts International, Volume: 75-01(E), Section: B.
Contained By:	Dissertation Abstracts International75-01B(E).
標題:	Mechanical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3597930
ISBN:	9781303462948

Inversion-Based Control Tools for High-Speed Precision Tracking/Transition in Emerging Applications.
Wang, Haiming.

Inversion-Based Control Tools for High-Speed Precision Tracking/Transition in Emerging Applications. - 142 p.

Source: Dissertation Abstracts International, Volume: 75-01(E), Section: B.

Thesis (Ph.D.)--Rutgers The State University of New Jersey - New Brunswick, 2013.

This dissertation work is motivated by the challenges in high-speed precision output tracking and transition in emerging applications, particularly for nonminimum-phase systems. Although fundamental performance limits to achieving precision output tracking with feedback control alone have been studied and quantified, for nonminimum-phase systems, exact output tracking cannot be achieved by using feedback control alone, due to the limits imposed by the nonminimum-phase zeros of the system. On the feedforward control side, the stable inversion theory solved the challenging output tracking problem and achieved exact tracking of a given desired output trajectory for nonminimum-phase systems (linear and nonlinear). The obtained solution, however, is noncausal and requires the entire desired trajectory to be known a priori. This noncausality constraint has been alleviated through the development of the preview-based inversion approach. Therefore, the stable-inversion framework provides an effective approach to output tracking of nonminimum-phase systems. Challenges, however, still exist in the existing stable-inversion theory for continuously more stringent control requirements. For example, the control problem of nonperiodic tracking-transition switching with preview for nonminimum-phase systems cannot be satisfactorily addressed by using the existing techniques. Another challenge in the existing stable-inversion approach is that as a feedforward control technique, it can be sensitive to the system dynamics uncertainties. Finally, the demanding online computation involved in the preview-based stable-inversion technique hinders the application of this approach in the presence of limited computation power. Therefore, these challenges, as magnified in applications of high-speed nano-manipulation and nano-fabrication, motivate the research work of this dissertation. First, the problem of nonperiodic tracking-transition switching with preview is considered. In the proposed preview-based optimal output tracking and transition (POOTT) approach, the optimal desired output trajectory for the transition sections is designed through direct minimization of the output energy, and the needed control input is obtained to maintain the smoothness of system state across all tracking-transition switching instants by using a preview-based stable-inversion approach. The needed preview time is quantified, and the recently-developed optimal preview-based inversion approach is also incorporated to minimize the amount of preview time. Secondly, a B-spline-decomposition (BSD)-based approach to output tracking with preview is developed for nonminimum-phase systems, that not only substantially reduces the dynamics uncertainty effect on tracking performance, but also minimizes the online demanding computation. The BSD approach is illustrated through simulation study of a nanomanipulation application using a nonminimum-phase piezo actuator model, and then further demonstrated by a 2D nanomanipulation in experiments using AFM. Finally, a multi-axis inversion-based iterative control (MAIIC) approach is developed to compensate for the dynamics coupling in multi-axis motion during high-speed nanofabrication. By using this advanced control technique, precision position control of the probe with respect to the sample substrate can be achieved during highspeed, large-range multi-axis nanofabrication. Particularly, the cross-axis dynamics coupling effect on the output tracking can be compensated for during the iterative learning process with no additional steps to learn the cross-coupling effect separately. The MAIIC approach is illustrated through experiments by implementing it to fabricate two Chinese characters pattern via mechanical scratching on a gold-coated silicon sample surface at high speed. The research work of this dissertation addresses the limits and further extends the inversion-based control techniques for high-speed precision tracking/transition in emerging applications, particularly, the challenges involved in output tracking with non-periodic tracking-transition switching, accounting for dynamics uncertainty and demanding computation requirements for nonminimum-phase systems, and cross-axis coupling in high-speed multi-axis motion. The experimental part of the work demonstrates and illustrates the efficacy of the proposed control techniques.

ISBN: 9781303462948Subjects--Topical Terms:

649730
Mechanical engineering.

Inversion-Based Control Tools for High-Speed Precision Tracking/Transition in Emerging Applications.
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245 1 0 $a Inversion-Based Control Tools for High-Speed Precision Tracking/Transition in Emerging Applications.
300 $a 142 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-01(E), Section: B.
500 $a Adviser: Qingze Zou.
502 $a Thesis (Ph.D.)--Rutgers The State University of New Jersey - New Brunswick, 2013.
520 $a This dissertation work is motivated by the challenges in high-speed precision output tracking and transition in emerging applications, particularly for nonminimum-phase systems. Although fundamental performance limits to achieving precision output tracking with feedback control alone have been studied and quantified, for nonminimum-phase systems, exact output tracking cannot be achieved by using feedback control alone, due to the limits imposed by the nonminimum-phase zeros of the system. On the feedforward control side, the stable inversion theory solved the challenging output tracking problem and achieved exact tracking of a given desired output trajectory for nonminimum-phase systems (linear and nonlinear). The obtained solution, however, is noncausal and requires the entire desired trajectory to be known a priori. This noncausality constraint has been alleviated through the development of the preview-based inversion approach. Therefore, the stable-inversion framework provides an effective approach to output tracking of nonminimum-phase systems. Challenges, however, still exist in the existing stable-inversion theory for continuously more stringent control requirements. For example, the control problem of nonperiodic tracking-transition switching with preview for nonminimum-phase systems cannot be satisfactorily addressed by using the existing techniques. Another challenge in the existing stable-inversion approach is that as a feedforward control technique, it can be sensitive to the system dynamics uncertainties. Finally, the demanding online computation involved in the preview-based stable-inversion technique hinders the application of this approach in the presence of limited computation power. Therefore, these challenges, as magnified in applications of high-speed nano-manipulation and nano-fabrication, motivate the research work of this dissertation. First, the problem of nonperiodic tracking-transition switching with preview is considered. In the proposed preview-based optimal output tracking and transition (POOTT) approach, the optimal desired output trajectory for the transition sections is designed through direct minimization of the output energy, and the needed control input is obtained to maintain the smoothness of system state across all tracking-transition switching instants by using a preview-based stable-inversion approach. The needed preview time is quantified, and the recently-developed optimal preview-based inversion approach is also incorporated to minimize the amount of preview time. Secondly, a B-spline-decomposition (BSD)-based approach to output tracking with preview is developed for nonminimum-phase systems, that not only substantially reduces the dynamics uncertainty effect on tracking performance, but also minimizes the online demanding computation. The BSD approach is illustrated through simulation study of a nanomanipulation application using a nonminimum-phase piezo actuator model, and then further demonstrated by a 2D nanomanipulation in experiments using AFM. Finally, a multi-axis inversion-based iterative control (MAIIC) approach is developed to compensate for the dynamics coupling in multi-axis motion during high-speed nanofabrication. By using this advanced control technique, precision position control of the probe with respect to the sample substrate can be achieved during highspeed, large-range multi-axis nanofabrication. Particularly, the cross-axis dynamics coupling effect on the output tracking can be compensated for during the iterative learning process with no additional steps to learn the cross-coupling effect separately. The MAIIC approach is illustrated through experiments by implementing it to fabricate two Chinese characters pattern via mechanical scratching on a gold-coated silicon sample surface at high speed. The research work of this dissertation addresses the limits and further extends the inversion-based control techniques for high-speed precision tracking/transition in emerging applications, particularly, the challenges involved in output tracking with non-periodic tracking-transition switching, accounting for dynamics uncertainty and demanding computation requirements for nonminimum-phase systems, and cross-axis coupling in high-speed multi-axis motion. The experimental part of the work demonstrates and illustrates the efficacy of the proposed control techniques.
590 $a School code: 0190.
650 4 $a Mechanical engineering. $3 649730
650 4 $a Systems science. $3 3168411
650 4 $a Nanotechnology. $3 526235
690 $a 0548
690 $a 0790
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710 2 $a Rutgers The State University of New Jersey - New Brunswick. $b Mechanical and Aerospace Engineering. $3 3168583
773 0 $t Dissertation Abstracts International $g 75-01B(E).
790 $a 0190
791 $a Ph.D.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3597930