Time and state dependent parameterized model reference adaptive control

Okumuş, Zeynep.
Unknown external disturbances or nonlinear dynamics, could affect both the stability and the performance of the of the air vehicles adversely. Unavoidable structure of this reality occurrance in real life applications, led the researches to design adaptive control which could eliminate the deficiencies of the nominal controller. So that, the main aim of the total controller, to satisfy the robustness and performance of the controller could be established. Model following controller, and the model reference adaptive controllers are the ones which determine a reference model, and satisfy the system model responses track the reference model. The performance of the model reference adaptive controller depends on the success in prediction of the uncertainties mentioned above. The prediction is done, by the multiplication of basis functions and adaptive weight coefficients. The convergence of the predicted weights to their true values means accurate uncertainty prediction. Concurrent adaptive learning, eliminates PE restriction for parameter convergence, enhances the performance of the model reference adaptive controller. Data storage in concurrent adaptive learning is based on singular value maximizing. Chebsyhev polynomials and Fourier series are used as time dependent basis functions, and state dependent function for the state dependent basis functions, in uncertainty prediction. The adaptive weight update law is defined for both types of uncertainties, depending on the Lyapunov stability theorem. The time and state dependent model reference adaptive controller gives the best reference model tracking results, compared to the ones which use solely time or state dependent basis functions, and the related weight update laws.
Citation Formats
Z. Okumuş, “Time and state dependent parameterized model reference adaptive control,” Thesis (M.S.) -- Graduate School of Natural and Applied Sciences. Aerospace Engineering., Middle East Technical University, 2019.