This thesis addresses the design of nonlinear navigation systems for autonomous vehicles, following two main approaches: Kalman filter based estimators, and Lyapunov theory based nonlinear observers. The proposed Kalman filter architectures are designed for accurate position and attitude estimation using low-cost sensor suites. An extended Kalman filter is adopted to merge a high accuracy inert...

This paper focusses on a novel discrete-time reduced order neural observer for nonlinear systems, which model is assumed to be unknown. This neural observer is robust in presence of external and internal uncertainties. The proposed scheme is based on a discrete-time recurrent high order neural network (RHONN) trained with an extended Kalman filter (EKF)-based algorithm, using a parallel configu...

This paper develops sufficient conditions for the existence of global exponential observers for two classes of nonlinear systems: (i) the class of systems with a globally asymptotically stable compact set, and (ii) the class of systems that evolve on an open set. In the first class, the derived continuous-time observer also leads to the construction of a robust global sampled-data exponential o...

We describe an adaptive observerlidentifier for single input-single output observable nonlinear systems that can be transformed to a certain observable canonical form. We provide sufficient conditions for stability of this observer. These conditions are in terms of the structure of the system and its canonical form, the boundedness of the parameter variations, and the sufficient richness of som...

This paper addresses the asymptotic output tracking problem for the model of an overhead crane. It is shown that the model is linearizable by dynamic state feedback when the full state is available for measurement. In the more realistic situation when the angle that the rope forms with respect to the vertical axis cannot be measured, two different nonlinear observers are used in order to estima...

A multiple model based observer/estimator for the estimation of parameters is used to reset the parameter estimation in a conventional Lyapunov based nonlinear adaptive controller. The advantage of combining both approaches is that the performance of the controller with respect to disturbances can be considerably improved while a reduced controller gain will increase the robustness of the appro...

Pramod Vachhani, Sridharakumar Narasimhan, and Raghunathan Rengaswamy Process fault diagnosis is the problem of identifying the causal origins of faults in a process, given current sensor data and a priori knowledge about the process behaviour under normal and abnormal conditions. Fault diagnosis is increasingly becoming important for process safety, improved product quality and process economi...