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Control of Nonlinear Systems using Local Approximation Methods
Brablc, Martin ; Bugeja, Marvin (oponent) ; Grepl, Robert (vedoucí práce)
This thesis deals with the development of an adaptive control algorithm for a specific class of electromechanical actuators, based on the feedforward compensation principle using an inverse dynamic model. The control algorithm's adaptability originates in a mechanism of learning the inverse dynamic model. This thesis focuses on using local approximation methods for an online inverse model learning. The outcome of this thesis is a summary of the analysis, simulations and actual experiments, which tested the possibilities of using the local approximation methods for adaptive control purposes in real environment.
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Hybrid Method for Modelling and State Estimation of Dynamic Systems
Brablc, Martin ; Blaha, Petr (oponent) ; Bugeja, Marvin (oponent) ; Grepl, Robert (vedoucí práce)
This Doctoral thesis deals with the development of a new hybrid method for the dual estimation of states and parameters of nonlinear dynamic systems based on the idea of local linear models, which uses the estimation of the uncertainty of the model parameters to automatically adjust the parameters of the Kalman filter (KF), thus greatly simplifying its deployment and adjustment in practical applications. In the first part, the dissertation summarises the current state of knowledge in the field of dynamic systems, simultaneous estimation, KF and modelling of nonlinear dynamic systems. Then, in two separate chapters, it discusses the modification of KF for situations where inaccurate model parameters are the dominant influence causing process noise, and the modification of the Receptive field weighted regression (RFWR) method so that it can be used for dual estimation. Finally, the paper describes the developed hybrid method composed of modified RFWR and KF algorithms called Receptive field dual estimation - (RFDE) and demonstrates its performance on simulation and real data.
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Control of Nonlinear Systems using Local Approximation Methods
Brablc, Martin ; Bugeja, Marvin (oponent) ; Grepl, Robert (vedoucí práce)
This thesis deals with the development of an adaptive control algorithm for a specific class of electromechanical actuators, based on the feedforward compensation principle using an inverse dynamic model. The control algorithm's adaptability originates in a mechanism of learning the inverse dynamic model. This thesis focuses on using local approximation methods for an online inverse model learning. The outcome of this thesis is a summary of the analysis, simulations and actual experiments, which tested the possibilities of using the local approximation methods for adaptive control purposes in real environment.
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