Hybrid modeling and control of mechatronic systems using a piecewise affine dynamics approach

This thesis investigates the topic of modeling and control of PWA systems based on two experimental cases of an electrical and hydraulic nature with varying complexity that were also built, instrumented and evaluated. A full-order model has been created for both systems, including all dominant system dynamics and non-linearities. The unknown parameters and characteristics have been identi ed via an extensive parameter identi cation. In the following, the non-linear characteristics are linearized at several points, resulting in PWA models for each respective setup. Regarding the closed loop control of the generated models and corresponding experimental setups, a linear control structure comprised of integral error, feed-forward and state-feedback control has been used. Additionally, the hydraulic setup has been controlled in an autonomous hybrid position/force control mode, resulting in a switched system with each mode's dynamics being de ned by the previously derived PWA-based model in combination with the control structure and respective mode-dependent controller gains. The autonomous switch between control modes has been de ned by a switching event capable of consistently switching between modes in a deterministic manner despite the noise-a icted measurements. Several methods were used to obtain suitable controller gains, including optimization routines and pole placement. Validation of the system's fast and accurate response was obtained through simulations and experimental evaluation. The controlled system's local stability was proven for regions in state-space associated with operational points by using pole-zero analysis. The stability of the hybrid control approach was proven by using multiple Lyapunov functions for the investigated test scenarios.publishedVersio

Robust sensor fault detection and isolation of gas turbine engines

An effective fault detection and isolation (FDI) technology can play a crucial role in improving the system availability, safety and reliability as well as reducing the risks of catastrophic failures. In this thesis, the robust sensor FDI problem of gas turbine engines is investigated and different novel techniques are developed to address the effects of parameter uncertainties, disturbances as well as process and measurement noise on the performance of FDI strategies. The efficiencies of proposed techniques are investigated through extensive simulation studies for the single spool gas turbine engine that is previously developed and validated using the GSP software. The gas turbine engine health degradation is considered in various forms in this thesis. First, it is considered as a part of the engine dynamics that is estimated off-line and updated periodically for the on-board engine model. Second, it is modeled as the time-varying norm-bounded parameter uncertainty that affects all the system state-space matrices and third as an unknown nonlinear dynamic that is approximated by the use of a dynamic recurrent neural network. In the first part of the thesis, we propose a hybrid Kalman filter (HKF) scheme that consists of a single nonlinear on-board engine model (OBEM) augmented with piecewise linear (PWL) models constituting as the multiple model (MM) based estimators to cover the entire engine operating regime. We have integrated the generalized likelihood ratio (GLR)-based method with our MM-based scheme to estimate the sensor fault severity under various single and concurrent fault scenarios. In order to ensure the reliability of our proposed HKF-based FDI scheme during the engine life cycle, it is assumed that the reference baselines are periodically updated for the OBEM health parameters. In the second part of the thesis, a novel robust sensor FDI strategy using the MM-based approach is proposed that remains robust with respect to both time-varying parameter uncertainties and process and measurement noise. The scheme is composed of robust Kalman filters (RKF) that are constructed for multiple PWL models. The parameter uncertainty is modeled by using a time-varying norm bounded admissible structure that affects all the PWL state space matrices. The robust Kalman filter gain matrices are designed by solving two algebraic Riccati equations (ARE) that are expressed as two linear matrix inequality (LMI) feasibility conditions. The main objective is to propose a robust filter that satisfies the overall performance requirements and is not affected by system perturbations. The requirements include a quadratically stable filter that ensures bounded estimation error variances having predefined values. In the third part of the thesis, a novel hybrid approach is proposed to improve the robustness of FDI scheme with respect to different sources of uncertainties. For this purpose, a dynamic recurrent neural network (DRNN) is designed to approximate the gas turbine engine uncertainty due to the health degradations. The proposed DRNN is trained offline by using the extended Kalman filter (EKF) algorithm for an engine with different levels of uncertainty, but with healthy sensors. The convergence of EKF-based DRNN training algorithm is also investigated. Then, the trained DRNN with the fixed parameters and topology is integrated with our online model-based FDI algorithm to approximate the uncertainty terms of the real engine. In this part, the previously proposed HKF and RKF are integrated with the trained DRNN to construct the hybrid FDI structure

Algorithmes et architectures pour la commande et le diagnostic de systèmes critiques de vol

Flight-Critical Systems such as Electromechanical Actuators driven by Engine Control Units (ECU) or Flight Control Units (FCU) are designed and developed regarding drastic safety requirements. In this study, an actuator control and monitoring ECU architecture based on analytic redundancy is proposed. In case of fault occurrences, material redundancies in avionic equipment allow certaincritical systems to reconfigure or to switch into a safe mode. However, material redundancies increase aircraft equipment size, weight and power (SWaP). Monitoring based on dynamical models is an interesting way to further enhance safetyand availability without increasing the number of redundant items. Model-base dfault detection and isolation (FDI) methods [58, 26, 47] such as observers and parity space are recalled in this study. The properties of differential flatness for nonlinear systems [80, 41, 73] and endogenous feedback linearisation are used with nonlinear diagnosis models. Linear and nonlinear observers are then compared with an application on hybrid stepper motor (HSM). A testing bench was specially designed to observe in real-time the behaviour of the diagnosis models when faults occur on the stator windings of a HSM.Les systèmes critiques de vol tels que les actionneurs électromécaniques ainsi que les calculateurs de commande moteur (ECU) et de vol (FCU),sont conçus en tenant compte des contraintes aéronautiques sévères de sureté defonctionnement. Dans le cadre de cette étude, une architecture calculateur pourla commande et la surveillance d’actionneurs moteur et de surfaces de vol est proposée et à fait l’objet d’un brevet [13]. Pour garantir ces mesure de sureté, les ECU et FCU présentent des redondances matérielles multiples, mais engendrent une augmentation de l’encombrement, du poids et de l’énergie consommée. Pour ces raisons, les redondances à base de modèles dynamiques, présentent un atout majeur pour les calculateurs car elles permettent dans certains cas de maintenir les exigences d’intégrité et de disponibilité tout en réduisant le nombre de capteurs ou d’actionneurs. Un rappel sur les méthodes de diagnostic par générateurs de résidus et estimateurs d’états [58, 26, 47] est effectué dans cette étude. Les propriétés de platitude différentielle et la linéarisation par difféomorphisme et bouclage endogène [80, 41, 73] permettent d’utiliser des modèles linéaires équivalents avec les générateurs de résidus. Un banc d’essai a été conçu afin de valider les performances des algorithmes de diagnostic