자료유형  

 학위논문

Control Number  

0017165051

International Standard Book Number  

9798346583929

Dewey Decimal Classification Number  

330

Main Entry-Personal Name  

Yang, Haoguang.

Publication, Distribution, etc. (Imprint  

[S.l.] : Purdue University., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

168 p.

General Note  

Source: Dissertations Abstracts International, Volume: 86-05, Section: A.

General Note  

Advisor: Voyles, Richard M.

Dissertation Note  

Thesis (Ph.D.)--Purdue University, 2024.

Summary, Etc.  

요약Robotic software design and implementation have traditionally relied on human engineers to fine-tune parameters, optimize hardware utilization, and mitigate unprecedented situations. As we face more demanding and complex applications, such as distributed robotic fleets and autonomous driving, explicit fine-tuning of autonomous systems yields diminishing returns. To make autonomous systems smarter, a design-time and run-time framework is required to extract constraints from high-level human decisions, and self-adapt on-the-fly to maintain desired specifications. Specifically, for controllers that govern cyber-physical interactions, making them self-adaptive involves two challenges. Firstly, controller design methods have historically neglected computing hardware constraints that realize real-time execution. Hence, intensive manual tuning is required to materialize a controller prototype with balanced control performance and computing resource consumption. Secondly, precisely modeling the physical system dynamics at edge cases is difficult and costly. However, with modeling discrepancies, controllers fine-tuned at design time may fail at run time, causing safety concerns. While humans are inherently adept at reacting and getting used to unknown system dynamics, how to transfer this knowledge to robots is still unresolved. To address the two challenges, we propose a designer-augmenting framework for self-adaptive control systems. Our framework includes a resource/performance co-design tool and a model-free controller self-adaptation method for real-time control systems. Our resource/performance co-design tool automatically exploits the Pareto front of controllers, between real-time computing resource utilization and achievable control performance. The co-design tool simplifies the iterative partitioning and verification of controller performance and distributed resource budget, enabling human engineers to directly interface with high-level design decisions between quality and cost. Our controller self-adaptation method extracts objectives and tolerances from human demonstrations and applies them to real-time controller switching, allowing human experts to design fault mitigation behaviors directly through coaching. The objective extraction and real-time adaptation do not rely on prior knowledge of the plant, making them inherently robust against mismatch between the design reference model and the physical system. Only with the prerequisite of real-time schedulability under Worst-Case Execution Time (WCET), will the digital controller deliver the designed dynamics. To determine the real-time schedulability of controllers during the design-time iteration and run-time self-adaptation, we propose a novel estimate of WCET based on the Mixed Weibull distribution of profiling statistics and a linear composition model. Our hybrid approach applies to design-time estimation of arbitrary-scaled controllers, yielding results as accurate as a state-of-the-art method while being more robust under small profiling sample sizes. Finally, we propose a resource consolidator that accounts for real-time schedulable bounds to utilize available computing resources while preventing deadline misses efficiently. Our consolidator, formulated as a vector packing problem, exploits different parallelization techniques on a CPU/FPGA hybrid architecture to obtain the most compact allocation plan for a given controller complexity and throughput. By jointly considering all four aspects, our framework automates the co-optimization of controller performance and computing hardware requirements throughout the life cycle of a control system. As a result, the engineering time required to design and deploy a controller is significantly reduced, while the adaptivity of human engineers is extended to fault mitigation at run-time.

Subject Added Entry-Topical Term  

Sparsity.

Subject Added Entry-Topical Term  

Software.

Subject Added Entry-Topical Term  

Space exploration.

Subject Added Entry-Topical Term  

Decision making.

Subject Added Entry-Topical Term  

Controllers.

Subject Added Entry-Topical Term  

Robots.

Subject Added Entry-Topical Term  

Adaptation.

Subject Added Entry-Topical Term  

Distributed control systems.

Subject Added Entry-Topical Term  

Coaching.

Subject Added Entry-Topical Term  

Co-design.

Subject Added Entry-Topical Term  

Field programmable gate arrays.

Subject Added Entry-Topical Term  

Robotics.

Subject Added Entry-Topical Term  

Aerospace engineering.

Subject Added Entry-Topical Term  

Computer engineering.

Subject Added Entry-Topical Term  

Design.

Added Entry-Corporate Name  

Purdue University.

Host Item Entry  

Dissertations Abstracts International. 86-05A.

Electronic Location and Access  

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Control Number  

joongbu:658509