This thesis proposes a general methodology for identifying and reconstructing sensor faults on dynamical processes. This identification theory provides a general framework for the problem of "observability with unknown inputs". Next, a framework for fault detection and isolation of sensors and actuators is proposed. The FDI sheme is based on bank of high-gain observers. A simulation study of a waste water treatment plant shows the effectiveness of the proposed approach.The second point evoked in the thesis is the observability of nonlinear dynamic systems and state estimation. The Extended Kalman Filter (EKF) is a widely used observer for such nonlinear systems. However, it suffers from the lack of theoretical justifications. The EKF, when applied to a system put in a normal form of observability, it acquires the property of global exponential convergence. Unfortunately, this latter observer (HG-EKF) is very sensitive to measurement noise. In order to combine the behaviors of the EKF (efficiency with respect to noise smoothing) and of the HG-EKF (reactivity to large estimation errors), (Boizot et al, 2010) proposed an adaptive high gain observer. This observer is applied to a MIMO nonlinear system of an Activated Sludge Process. A comparison study of the performances of the three observers under consideration is carried out. Results show a clearly better state estimation for the adaptive observer.
