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Nonlinear Control of Robotic Fish.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Nonlinear Control of Robotic Fish./
作者:	Castano, Maria L.
面頁冊數:	1 online resource (169 pages)
附註:	Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
Contained By:	Dissertations Abstracts International83-03B.
標題:	Electrical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28716436click for full text (PQDT)
ISBN:	9798538106400

Nonlinear Control of Robotic Fish.
Castano, Maria L.

Nonlinear Control of Robotic Fish. - 1 online resource (169 pages)

Source: Dissertations Abstracts International, Volume: 83-03, Section: B.

Thesis (Ph.D.)--Michigan State University, 2021.

Includes bibliographical references

In the past few decades, robots that propel and maneuver themselves like fish, known as robotic fish, have received substantial attention due to their efficiency, maneuverability, and lifelike features. Their agile locomotion can be partially attributed to their bio-inspired propulsion methods, which range from tail (caudal) and dorsal to paired pectoral fins. While these characteristics make robotic fish an attractive choice for a myriad of aquatic applications, their highly nonlinear, often under-actuated dynamics and actuator constraints present significant challenges in control design. The goal of this dissertation is to develop systematic model-based control approaches that guarantee closed-loop system stability, accommodate input constraints, and are computationally viable for robotic fish. We first propose a nonlinear model predictive control (NMPC) approach for path-following of a tail-actuated robotic fish, where the control design is based on an averaged dynamic model. The bias and the amplitude of the tail oscillation are treated as physical variables to be manipulated and are related to the control inputs via a nonlinear map. A control projection method is introduced to accommodate the inputs constraints while minimizing the optimization complexity in solving the NMPC problem. Both simulation and experimental results on a tail-actuated robotic fish support the efficacy of the proposed approach and its advantages over alternative approaches. Although NMPC is a promising candidate for tracking control, its computational complexity poses significant challenges in its implementation on resource-constrained robotic fish. We thus propose a backstepping-based trajectory tracking control scheme that is computationally inexpensive and guarantees closed-loop stability. We demonstrate how the control scheme can be synthesized to handle input constraints and establish via singular perturbation analysis the ultimate boundedness of three tracking errors (2D-position and orientation) despite the under-actuated nature of the robot. The effectiveness of this approach is supported by both simulation and experimental results on a tail-actuated robotic fish. We then turn our attention to pectoral fin-actuated robotic fish. Despite its benefits in achieving agile maneuvering at low swimming speeds, the range constraint of pectoral fin movement presents challenges in control. To overcome these challenges, we propose two different backstepping-based control approaches to achieve trajectory tracking and quick-maneuvering control, respectively. We first propose a scaling-based approach to develop a control-affine nonlinear dynamic average model for a pectoral fin-actuated robotic fish, which is validated via both simulation and experiments. The utility of the developed average dynamic model is then demonstrated via the synthesis of a dual-loop backstepping-based trajectory tracking controller. Cyclic actuation can often limit precise manipulation of the fin movements and the full exploitation of the maneuverability of pectoral fin-actuated robotic fish. To achieve quick velocity maneuvering control, we propose a dual-loop control approach composed of a backstepping-based controller in the outer loop and a fin movement-planning algorithm in the inner loop. Simulation results are presented to demonstrate the performance of the proposed scheme via comparison with a nonlinear model predictive controller.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798538106400Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

Marine robotsIndex Terms--Genre/Form:

542853
Electronic books.

Nonlinear Control of Robotic Fish.
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500 $a Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
500 $a Advisor: Tan, Xiaobo.
502 $a Thesis (Ph.D.)--Michigan State University, 2021.
504 $a Includes bibliographical references
520 $a In the past few decades, robots that propel and maneuver themselves like fish, known as robotic fish, have received substantial attention due to their efficiency, maneuverability, and lifelike features. Their agile locomotion can be partially attributed to their bio-inspired propulsion methods, which range from tail (caudal) and dorsal to paired pectoral fins. While these characteristics make robotic fish an attractive choice for a myriad of aquatic applications, their highly nonlinear, often under-actuated dynamics and actuator constraints present significant challenges in control design. The goal of this dissertation is to develop systematic model-based control approaches that guarantee closed-loop system stability, accommodate input constraints, and are computationally viable for robotic fish. We first propose a nonlinear model predictive control (NMPC) approach for path-following of a tail-actuated robotic fish, where the control design is based on an averaged dynamic model. The bias and the amplitude of the tail oscillation are treated as physical variables to be manipulated and are related to the control inputs via a nonlinear map. A control projection method is introduced to accommodate the inputs constraints while minimizing the optimization complexity in solving the NMPC problem. Both simulation and experimental results on a tail-actuated robotic fish support the efficacy of the proposed approach and its advantages over alternative approaches. Although NMPC is a promising candidate for tracking control, its computational complexity poses significant challenges in its implementation on resource-constrained robotic fish. We thus propose a backstepping-based trajectory tracking control scheme that is computationally inexpensive and guarantees closed-loop stability. We demonstrate how the control scheme can be synthesized to handle input constraints and establish via singular perturbation analysis the ultimate boundedness of three tracking errors (2D-position and orientation) despite the under-actuated nature of the robot. The effectiveness of this approach is supported by both simulation and experimental results on a tail-actuated robotic fish. We then turn our attention to pectoral fin-actuated robotic fish. Despite its benefits in achieving agile maneuvering at low swimming speeds, the range constraint of pectoral fin movement presents challenges in control. To overcome these challenges, we propose two different backstepping-based control approaches to achieve trajectory tracking and quick-maneuvering control, respectively. We first propose a scaling-based approach to develop a control-affine nonlinear dynamic average model for a pectoral fin-actuated robotic fish, which is validated via both simulation and experiments. The utility of the developed average dynamic model is then demonstrated via the synthesis of a dual-loop backstepping-based trajectory tracking controller. Cyclic actuation can often limit precise manipulation of the fin movements and the full exploitation of the maneuverability of pectoral fin-actuated robotic fish. To achieve quick velocity maneuvering control, we propose a dual-loop control approach composed of a backstepping-based controller in the outer loop and a fin movement-planning algorithm in the inner loop. Simulation results are presented to demonstrate the performance of the proposed scheme via comparison with a nonlinear model predictive controller.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Electrical engineering. $3 649834
650 4 $a Velocity. $3 3560495
650 4 $a Control algorithms. $3 3560702
650 4 $a Parameter identification. $3 3687410
650 4 $a Closed loop systems. $3 3554637
650 4 $a Swimming. $3 638392
650 4 $a Controllers. $3 3559217
650 4 $a Robots. $3 529507
650 4 $a Robotics. $3 519753
653 $a Marine robots
653 $a Modeling
653 $a Nonlinear control
653 $a Robotics
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690 $a 0771
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a Michigan State University. $b Electrical Engineering - Doctor of Philosophy. $3 2094234
773 0 $t Dissertations Abstracts International $g 83-03B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28716436 $z click for full text (PQDT)