Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 68 for “"Learning Control"”.
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The robust stability of iterative learning control
… of the long term robust stability of iterative learning control (ILC) systems engaged in trajectory tracking, using a robust stability theorem based on a biased version of the nonlinear gap metric. This is achieved through two main results: <br/><br/>The first concerns the establishment of a …
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Robustification in Repetitive and Iterative Learning Control
Repetitive Control (RC) and Iterative Learning Control (ILC) are control methods that specifically deal with periodic signals or systems with repetitive operations. They have wide applications in diverse areas from high-precision manufacturing to high-speed assembly, and nowadays these algorithms …
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A distributed hierarchical iterative learning control framework
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms
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Learning control applied to a model helicopter
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1994.
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Iterative learning control: algorithm development and experimental benchmarking
… area of experimental benchmarking of Iterative Learning Control (ILC) algorithms using two experimental facilities. ILC is an approach which is suitable for applications where the same task is executed repeatedly over the necessarily finite time duration, known as the trial length. The process …
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Fuzzy Model Reference Learning Control for Smart Lights
… of an adaptive and nonadaptive fuzzy control for a smart light experimental testbed. The objective is to accurately regulate the light level across the experimental testbed to a desired voltage reference value, and to test the performance of the fuzzy controllers under …
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Optimality and iterative learning control: duality and input prediction
… the use of optimal techniques within iterative learning control (ILC) applied to linear systems. Two different aspects are addressed: the first is the duality relationship existing between iterative learning control and repetitive control which allows the synthesis of controllers developed in …
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Development of the finite and infinite interval learning control theory
… centers on the theories of Finite Interval Learning (FIL) and Infinite Interval Learning (IIL) for nonlinear systems with deterministic uncertainties. Considering the existence of non-smooth nonlinearties in real systems, Iterative Learning Control (ILC) is extended to systems with …
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Reinforcement Learning Control for Mobile Robot Parking with Safety Constraints
… a hybrid framework that combines reinforcement learning (RL) with control barrier function (CBF)-based methods to achieve safe autonomous vehicle control, focusing on parking with obstacle avoidance. We apply Deep Deterministic Policy Gradient (DDPG) methods for continuous control and evaluate …
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Switched Q -Filters in Repetitive Control and Iterative Learning Control
Applications for periodic and repetitive systems which can benefit from the algorithms studied in this research are many. One of the most obvious applications is in robotic systems used in manufacturing. In this thesis research, the practicality of the various algorithms was demonstrated by …
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Deep reinforcement learning control of a 2D soft robotic arm
This thesis provides a deep reinforcement learning (DRL) based approach for the development of a control policy for a 2D soft robotic arm. The simulation is based on the SOFA framework, which is a real-time multi-physics simulation package capable of creating models and computing forces for …
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Model-based approaches for learning control from multi-modal data
Methods like deep reinforcement learning (DRL) have gained increasing attention when solving very general continuous control tasks in a model-free end-to-end fashion. However, there has been great difficulty in applying these algorithms to real-world systems due to poor sample efficiency and …
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Learning control for a class of discrete-time, nonlinear systems
Over the last few decades, control theory has developed to the level where reliable methods exist to achieve satisfactory performance on even the largest and most complex of dynamical systems. The application of these control methods, though, often require extensive modelling and design effort. …
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Learning control of bipedal dynamic walking robots with neural networks
… requirements for a dynamic walking robot. Learning and adaptation can improve stability and robustness. This thesis explores such an adaptation capability through the use of neural networks. Three neural network models (BP, CMAC and RBF networks) are studied. The RBF network is chosen as …
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A predictive learning control system for an energy conserving thermostat
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1981.
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Iterative learning control for improved tracking of fluid percussion injury device
… not execute the desired pressure profile. The controller used is a QCI-S3-IG Silver Sterling from Quick Silver Controls. A limitation innate to the controller was a 3-millisecond sampling of the input signal that proved challenging for developing fast, accurate FPI pulses with periods as fast …
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Learning-Control Model of a Multi-Level Multi-Goal Business Organization
Made available in DSpace on 2014-12-10T23:08:56Z (GMT). No. of bitstreams: 1 7317435.pdf: 9651267 bytes, checksum: d0bb876b85584eb447021892c0f5b6c4 (MD5) Previous issue date: 1972
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Enhancing Capabilities of Assistive Robotic Arms: Learning, Control, and Object Manipulation
… a variety of tasks and provide intuitive control mechanisms. We focus on developing techniques that allow these assistive robots to learn diverse tasks, manipulate different types of objects, and simplify user control of these complex, high-dimensional systems. This thesis is structured …
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Advances in iterative learning control with application to structural dynamic response reconstruction
Iterative learning control (ILC) is a repetitive control scheme that uses a learning capability to improve the tracking accuracy of a desired test system output over repeated test trials. ILC is sometimes used in response reconstruction on complex engineering structures, such as ground vehicles, …
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