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Experimental Comparison Of Two Sampled-Data Adaptive Control Algorithms For Rejecting Sinusoidal Disturbances, William Grayson Woods

Theses and Dissertations--Mechanical Engineering

We review two adaptive control algorithms that address the problem of rejecting sinusoids with known frequencies that act on an unknown asymptotically stable linear time-invariant system. We present modifications to the algorithms that address the problems of sensor noise and actuator saturation. We demonstrate the effectiveness of the algorithms and compare the performance of the algorithms via numerical simulation and experimental testing.

Discrete-Time Adaptive Control Algorithms For Rejection Of Sinusoidal Disturbances, Mohammadreza Kamaldar

Theses and Dissertations--Mechanical Engineering

We present new adaptive control algorithms that address the problem of rejecting sinusoids with known frequencies that act on an unknown asymptotically stable linear time-invariant system. To achieve asymptotic disturbance rejection, adaptive control algorithms of this dissertation rely on limited or no system model information. These algorithms are developed in discrete time, meaning that the control computations use sampled-data measurements. We demonstrate the effectiveness of algorithms via analysis, numerical simulations, and experimental testings. We also present extensions to these algorithms that address systems with decentralized control architecture and systems subject to disturbances with unknown frequencies.

Data-Driven Adaptive Reynolds-Averaged Navier-Stokes K - Ω Models For Turbulent Flow-Field Simulations, Zhiyong Li

Theses and Dissertations--Mechanical Engineering

The data-driven adaptive algorithms are explored as a means of increasing the accuracy of Reynolds-averaged turbulence models. This dissertation presents two new data-driven adaptive computational models for simulating turbulent flow, where partial-but-incomplete measurement data is available. These models automatically adjust (i.e., adapts) the closure coefficients of the Reynolds-averaged Navier-Stokes (RANS) k-ω turbulence equations to improve agreement between the simulated flow and a set of prescribed measurement data.

The first approach is the data-driven adaptive RANS k-ω (D-DARK) model. It is validated with three canonical flow geometries: pipe flow, the backward-facing step, and flow around an airfoil. For all 3 test …