MRAC + H∞ fault tolerant control for linear parameter varying systems

Presentado al SysTol 2010 celebrado en Niza del 6 al 8 de octubre.Two different schemes for Fault Tolerant Control (FTC) based on Adaptive Control, Robust Control and Linear Parameter Varying (LPV) systems are proposed. These schemes include a Model Reference Adaptive Controller for an LPV system (MRAC-LPV) and a Model Reference Adaptive Controller with a H¿ Gain Scheduling Controller for an LPV system (MRAC-H¿ GS-LPV). In order to compare the performance of these schemes, a Coupled-Tank system was used as testbed in which two different types of faults (abrupt and gradual) with different magnitudes and different operating points were simulated. Results showed that the use of a Robust Controller in combination with an Adaptive Controller for an LPV system improves the FTC schemes because this controller was Fault Tolerant against sensor fault and had an accommodation threshold for actuator fault magnitudes from 0 to 6Peer Reviewe

Neural MRAC based on modified state observer

A new model reference adaptive control design method with guaranteed transient performance using neural networks is proposed in this thesis. With this method, stable tracking of a desired trajectory is realized for nonlinear system with uncertainty, and modified state observer structure is designed to enable desired transient performance with large adaptive gain and at the same time avoid high frequency oscillation. The neural network adaption rule is derived using Lyapunov theory, which guarantees stability of error dynamics and boundedness of neural network weights, and a soft switching sliding mode modification is added in order to adjust tracking error. The proposed method is tested by different theoretical application problems simulations, and also Caterpillar Electro-Hydraulic Test Bench experiments. Satisfying results show the potential of this approach --Abstract, page iv

ZAAWANSOWANE METODY STEROWANIA PROCESEM SPALANIA PYŁU WĘGLOWEGO

The paper describes the selected methods of adaptive control of the pulverized coal combustion process overview with various types of prognostic models. It was proposed to use a class of control methods that are relatively well established in industrial practice. The presented approach distinguishes the use of an additional source of information in the form of signals from an optical diagnostic system and models based on selected deep structures of recurrent networks. The research aim is to increase the efficiency of the combustion process in the power boiler, taking into account the EU emission standards, leading in consequence to sustainable energy and sustainable environmental engineering.W artykule opisano wybrane metody adaptacyjnego sterowania przeglądem procesu spalania pyłu węglowego z wykorzystaniem określonych modeli prognostycznych. Zaproponowano użycie metod, które są stosunkowo dobrze znane w praktyce przemysłowej. Przedstawione podejście wyróżnia wykorzystanie dodatkowego źródła informacji w postaci sygnałów z optycznego systemu diagnostycznego i modeli opartych na strukturach sieci głębokich. Badania mają na celu zwiększenia efektywności procesu spalania w kotle energetycznym, z uwzględnieniem norm emisji UE, prowadząc w konsekwencji do zrównoważonej energii i zrównoważonej inżynierii środowiska

Comparison of Adaptive Control Architectures for Flutter Suppression

A study is conducted to derive and implement a state feedback model reference adaptive control (MRAC) solutions for a 2-D aeroelastic nonlinear system and in evaluating the robustness of different control strategies to damage leading to the deterioration of the structural stiffness characteristics. The standard MRAC, a modified MRAC and the adaptive controller are the three model reference adaptive control solutions analyzed. The standard direct MRAC solution serves as the threshold to assess whether or not the more complex algorithms are an effective improvement to it

Development of adaptive control methodologies and algorithms for nonlinear dynamic systems based on u-control framework

Inspired by the U-model based control system design (or called U-control system design), this study is mainly divided into three parts. The first one is a U-model based control system for unstable non-minimum phase system. Pulling theorems are proposed to apply zeros pulling filters and poles pulling filters to pass the unstable non-minimum phase characteristics of the plant model/system. The zeros pulling filters and poles pulling filters derive from a customised desired minimum phase plant model. The remaining controller design can be any classic control systems or U-model based control system. The difference between classic control systems and U-model based control system for unstable non-minimum phase will be shown in the case studies.Secondly, the U-model framework is proposed to integrate the direct model reference adaptive control with MIT normalised rules for nonlinear dynamic systems. The U-model based direct model reference adaptive control is defined as an enhanced direct model reference adaptive control expanding the application range from linear system to nonlinear system. The estimated parameter of the nonlinear dynamic system will be placement as the estimated gain of a customised linear virtual plant model with MIT normalised rules. The customised linear virtual plant model is the same form as the reference model. Moreover, the U-model framework is design for the nonlinear dynamic system within the root inversion.Thirdly, similar to the structure of the U-model based direct model reference adaptive control with MIT normalised rules, the U-model based direct model reference adaptive control with Lyapunov algorithms proposes a linear virtual plant model as well, estimated and adapted the particular parameters as the estimated gain which of the nonlinear plant model by Lyapunov algorithms. The root inversion such as Newton-Ralphson algorithm provides the simply and concise method to obtain the inversion of the nonlinear system without the estimated gain. The proposed U-model based direct control system design approach is applied to develop the controller for a nonlinear system to implement the linear adaptive control. The computational experiments are presented to validate the effectiveness and efficiency of the proposed U-model based direct model reference adaptive control approach and stabilise with satisfied performance as applying for the linear plant model

Adaptive Control and Identification Using One Neural Network for a Class of Plants with Uncertainties

This paper proposes a new neural adaptive control methodthat can perform adaptive control and identification for a class ofcontrolled plants with linear and nonlinear uncertainties. This methoduses a single neural network for both control and identification, and asufficient condition of the local asymptotic stability is derived. Then, inorder to illustrate the applicability of the proposed method, it is applied tothe torque control of a flexible beam that includes linear and nonlinearstructural uncertainties

Adaptive Control

Adaptive control has been a remarkable field for industrial and academic research since 1950s. Since more and more adaptive algorithms are applied in various control applications, it is becoming very important for practical implementation. As it can be confirmed from the increasing number of conferences and journals on adaptive control topics, it is certain that the adaptive control is a significant guidance for technology development.The authors the chapters in this book are professionals in their areas and their recent research results are presented in this book which will also provide new ideas for improved performance of various control application problems