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Learning and Optimization Methods for Robust Control of Hard Disk Drives and Geometric Control of Fully Actuated Mechanical Systems
Learning and Optimization Methods for Robust Control of Hard Disk Drives and Geometric Control of Fully Actuated Mechanical Systems
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152732
- ISBN
- 9798384449683
- DDC
- 621
- 서명/저자
- Learning and Optimization Methods for Robust Control of Hard Disk Drives and Geometric Control of Fully Actuated Mechanical Systems
- 발행사항
- [Sl] : University of California, Berkeley, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 139 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-04, Section: B.
- 주기사항
- Advisor: Horowitz, Roberto.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Berkeley, 2024.
- 초록/해제
- 요약The use of Machine Learning (ML) and optimization for control applications in enhancing performance, replicating expert behaviors, and addressing other complex challenges has emerged as a focal point in contemporary research. This has become possible due to ML techniques' ability to find patterns, approximate complex functions and make decisions from data. Although promising, learning-based controllers often encounter difficulties in maintaining stability guarantees due to their reliance solely on data. This data is often noisy or incomplete, which may lead to unpredicted and in many cases unstable system responses under previously unseen scenarios. A typical ML problem is formulated by designing a loss function and the parameters that minimize the loss are used to obtain the optimal model. For control applications, a constraint on the stability needs to be imposed both during training and inference. But, most ML models do not impose such conditions as hard constraints and only encourage through soft constraints in the loss function. These constraints might not be satisfied during inference and the ML control laws can destabilize the system.This dissertation addresses the challenge of designing stabilizing controllers utilizing ML and optimization techniques for two distinct classes of systems. In the first part, we explore designing controllers through Neural Network potential functions for fully actuated mechanical systems that evolve on manifolds with well-defined dynamics in the state space. The control laws incorporate the concept of invariance for data efficient training and easy transferability between robots with similar kinematic structure. The design methodology will be emphasized on an application to variable impedance control of mechanical manipulators. In the second part, we discuss the robust control of Multi-Input Single Output (MISO) systems, with a particular focus on Multi-Actuator Hard Disk Drives (HDDs). A design methodology in frequency domain based on available frequency response data to ensure stability and robustness against disturbances and model uncertainties is presented. An unsupervised ML technique to cluster the plant transfer functions and frequency responses into subgroups is presented. Clustering helps us design common controllers within each cluster to both maintain robustness and improve performance. Lastly, identification methods for obtaining dynamical models of disturbance processes with colored noises and necessary filters that satisfy Strictly Positive Real (SPR) conditions for stability of adaptive control algorithms are presented. Throughout the dissertation, we will delve into the theoretical underpinnings of these methodologies, complemented by simulation results that highlight significant improvements in system responsiveness and efficiency achieved through these innovative control strategies.This dissertation shows that integrating learning-based mechanisms into mechanical system controllers is both feasible and effective. It also offers guidance for future research to address the challenges of these technologies. By combining theoretical analysis and simulation studies, this work demonstrates how data-driven approaches can improve control systems.
- 일반주제명
- Mechanical engineering
- 일반주제명
- Robotics
- 일반주제명
- Electrical engineering
- 키워드
- Machine Learning
- 키워드
- Mechatronics
- 기타저자
- University of California, Berkeley Mechanical Engineering
- 기본자료저록
- Dissertations Abstracts International. 86-04B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520250211152732
■006m o d
■007cr#unu||||||||
■020 ▼a9798384449683
■035 ▼a(MiAaPQ)AAI31491025
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a621
■1001 ▼aPotu Surya Prakash, Nikhil.
■24510▼aLearning and Optimization Methods for Robust Control of Hard Disk Drives and Geometric Control of Fully Actuated Mechanical Systems
■260 ▼a[Sl]▼bUniversity of California, Berkeley▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a139 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-04, Section: B.
■500 ▼aAdvisor: Horowitz, Roberto.
■5021 ▼aThesis (Ph.D.)--University of California, Berkeley, 2024.
■520 ▼aThe use of Machine Learning (ML) and optimization for control applications in enhancing performance, replicating expert behaviors, and addressing other complex challenges has emerged as a focal point in contemporary research. This has become possible due to ML techniques' ability to find patterns, approximate complex functions and make decisions from data. Although promising, learning-based controllers often encounter difficulties in maintaining stability guarantees due to their reliance solely on data. This data is often noisy or incomplete, which may lead to unpredicted and in many cases unstable system responses under previously unseen scenarios. A typical ML problem is formulated by designing a loss function and the parameters that minimize the loss are used to obtain the optimal model. For control applications, a constraint on the stability needs to be imposed both during training and inference. But, most ML models do not impose such conditions as hard constraints and only encourage through soft constraints in the loss function. These constraints might not be satisfied during inference and the ML control laws can destabilize the system.This dissertation addresses the challenge of designing stabilizing controllers utilizing ML and optimization techniques for two distinct classes of systems. In the first part, we explore designing controllers through Neural Network potential functions for fully actuated mechanical systems that evolve on manifolds with well-defined dynamics in the state space. The control laws incorporate the concept of invariance for data efficient training and easy transferability between robots with similar kinematic structure. The design methodology will be emphasized on an application to variable impedance control of mechanical manipulators. In the second part, we discuss the robust control of Multi-Input Single Output (MISO) systems, with a particular focus on Multi-Actuator Hard Disk Drives (HDDs). A design methodology in frequency domain based on available frequency response data to ensure stability and robustness against disturbances and model uncertainties is presented. An unsupervised ML technique to cluster the plant transfer functions and frequency responses into subgroups is presented. Clustering helps us design common controllers within each cluster to both maintain robustness and improve performance. Lastly, identification methods for obtaining dynamical models of disturbance processes with colored noises and necessary filters that satisfy Strictly Positive Real (SPR) conditions for stability of adaptive control algorithms are presented. Throughout the dissertation, we will delve into the theoretical underpinnings of these methodologies, complemented by simulation results that highlight significant improvements in system responsiveness and efficiency achieved through these innovative control strategies.This dissertation shows that integrating learning-based mechanisms into mechanical system controllers is both feasible and effective. It also offers guidance for future research to address the challenges of these technologies. By combining theoretical analysis and simulation studies, this work demonstrates how data-driven approaches can improve control systems.
■590 ▼aSchool code: 0028.
■650 4▼aMechanical engineering
■650 4▼aRobotics
■650 4▼aElectrical engineering
■653 ▼aData driven control
■653 ▼aGeometric control
■653 ▼aKinesthetic teaching
■653 ▼aMachine Learning
■653 ▼aMechatronics
■690 ▼a0548
■690 ▼a0771
■690 ▼a0544
■71020▼aUniversity of California, Berkeley▼bMechanical Engineering.
■7730 ▼tDissertations Abstracts International▼g86-04B.
■790 ▼a0028
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17163625▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
