In this work, the problem of Fault Detection and Isolation (FDI) and Fault Tolerant Control (FTC) of wind turbines is addressed. Fault detection is based on the use of interval observers and unknown but bounded description of the noise and modeling errors. Fault isolation is based on analyzing the observed fault signatures on-line and matching them with the theoretical ones obtained using structural analysis and a row-reasoning scheme. Fault tolerant control is based on the use of virtual sensors/actuators to deal with sensor and actuator faults, respectively. More precisely, these FTC schemes, that have been proposed previously in state space form, are reformulated in input/output form. Since an active FTC strategy is used, the FTC module uses the information from the FDI module to replace the real faulty sensor/actuator by activating the corresponding virtual sensor/actuator. Virtual actuators/sensors require additionally a fault estimation module to compensate the fault. In this work, a fault estimation approach based on batch least squares is used. The performance of the proposed FDI and FTC schemes is assessed using the proposed fault scenarios considered in the wind turbine benchmark proposed at IFAC SAFEPROCESS 2009. Satisfactory results have been obtained in both FDI and FTC.

No abstract

SUMMARYThe paper presents a robust fault estimation approach for a class of non-linear discrete-time systems. In particular, two sources of uncertainty are present in the considered class of systems, i.e., an unknown input and an exogenous external disturbance. Thus, apart from simultaneous state and fault estimation, the objective is to decouple the effect of an unknown input while minimizing the influence of the exogenous external disturbance within the H∞ framework. The resulting design procedure guarantees that a prescribed disturbance attenuation level is achieved with respect to the state and fault estimation error while assuring the convergence of the observer. The core advantage of the proposed approach is its simplicity by reducing the fault estimation problem to matrix inequalities formulation. In addition, the design conditions ensure the convergence of the observer with guaranteed H∞ performance. The effectiveness of the proposed approach is demonstrated by its application to a Twin Rotor MIMO System.

No abstract

No abstract

Abstract-This paper proposes a reference model approach for the trajectory tracking of a four wheeled omnidirectional mobile robot. In particular, the error model is brought to a quasi-Linear Parameter Varying (LPV) form suitable for designing an errorfeedback controller. It is shown that, if polytopic techniques are used to reduce the number of constraints from infinite to finite, a solution within the standard LPV framework could not exist due to a singularity that appears in the possible values of the input matrix. Adding a switching component to the controller allows to solve this problem. Moreover, a switching LPV virtual actuator is added to the control loop in order to obtain fault tolerance within the fault-hiding paradigm, keeping the stability and some desired performances under the effect of actuator faults without the need of retuning the nominal controller. The effectiveness of the proposed approach is shown and proved through simulation and experimental results.

SUMMARYIn this paper a non-linear adaptive feedback-linearizing control is designed for a biological wastewater treatment model. The adaptive control structure is based on the non-linear model of the process and combined with a joint observer estimator which plays the role of the software sensor for the on-line estimation of biological states and parameter variables of interest of the bioprocess. The performances of both estimation and control algorithms are illustrated by simulation results. They demonstrate e!ectiveness and signi"cant robustness against measurement noises and kinetic parameter jumps.

Abstract-In this paper, the problem of fault diagnosis of a wind farm is addressed using interval nonlinear parameter varying (NLPV) parity equations. Fault detection is based on the use of parity equations assuming unknown but bounded description of the noise and modeling errors. The fault detection test is based on checking the consistency between the measurements and the model by finding if the formers are inside the interval prediction bounds. The fault isolation algorithm is based on analyzing the observed fault signatures on-line, and matching them with the theoretical ones obtained using structural analysis. Finally, the proposed approach is tested using the wind farm benchmark proposed in the context of the wind farm FDI/FTC competition.