Ph.D. Theses
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Browsing Ph.D. Theses by Subject "Adaptive control systems."
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Item Adaptive control of nonlinear systems using multiple identification models(Thesis (Ph.D.)-Bogazici University. Institute for Graduate Studies in Science and Engineering, 2007., 2007.) Cezayirli, Ahmet.; Cılız, Kemal.Adaptive control of nonlinear systems is considered in this study. Available methods in this field are reviewed first. Focusing on the minimum-phase, input-output linearizable and linearly parameterized nonlinear systems, direct and indirect adaptive controllers are developed for the cases of matched and unmatched uncertainties. A new methodology is proposed, which makes use of multiple identification models in order to improve the transient performance under large parametric uncertainties. Adaptation and switching mechanisms are developed based on the use of multiple Lyapunov functions and cost functions. The resulting closed loop systems are shown to be stable with the switching mechanisms. Combination of direct and indirect adaptive control schemes is also presented for a class of nonlinear systems which do not give rise to over-parametrization. The theoretical results obtained in this study are verified by computer simulations.Item Stochastic adaptive receding horizon controllers(Thesis (Ph.D)- Bogazici University. Institute for Graduate Studies in Science and Engineering, 1982., 1982.) Yaz, Engin.; Istefanopulos, Yorgo.In this thesis, the deterministic, stochastic and stochastic adaptive coritrolpossibilities based on the method of receding horizon is examiried. The receding horizon method assumes a fixed horizon length for feedback law calculation at each step. Therefore, the feedback law is optimal in one-step-ahead manner and the feedback gain is constant. The other advantages are of not having to choose the state penalization matrix and of replacing the solution of Riccati equation by a linear one. We alleviated some problems associated with the practical use of this method, such as calculation time and singular state transition matrices by some fast algorithms and non-zero set points by modification of the basic equations. Modelling the system in state space innovations representation or transforming it to this form if it is not modelled in innovations form originally, solves the problem of state reconstruction under noise effects. The overall design enjoys the separation property, that is, of having a separate design for control and estimation parts. In the case of some unknozn parameters in the system equations, our controller works using the state estimates, found by utilizing the parameter estimates, in the control law, and parameter estimates, found by using the state estimates, in the feedback gain calculation. This controller with this enforced certainty equivalence property enjoys many favorable characteristics such as refraining from the use of Riccati equation in control, matrix update equations for state and parameter estimation uncertainties, external perturbation signals to secure stability, and trial and error procedures in the choice of state penalization matrices. Moreover, the method is general enough to control with any prescribed control strength, multi-input, multi-output systems under noise effects, modelled in difference equation from with multi-parameter uncertainty.