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In this brief, a fault detection and isolation (FDI) method is developed for aircraft engines by utilizing nonlinear adaptive estimation techniques. The fault diagnosis method follows a general architecture developed in previous papers, where a fault detection estimator is used for fault detection, and a bank of nonlinear adaptive fault isolation estimators are employed to determine the particular fault type/location. Each isolation estimator is designed based on the functional structure of a particular fault type under consideration. The general FDI architecture is applied to a realistic nonlinear aircraft engine model recently developed by NASA Researchers. In addition, the robustness of the fault diagnostic method with respect to normal engine health degradation is enhanced by adaptive thresholds and adaptive approximation techniques, hence accurate diagnostic performance is maintained while the engine continues to degrade over its lifetime. Some representative simulation results are given to show the effectiveness of the robust nonlinear FDI method. [ABSTRACT FROM PUBLISHER]

Abstract: In this study, an adaptive robust tracking control scheme for a nanomechanical beam is proposed. The mathematical nanobeam model takes into consideration the presence of parametric uncertainties, and external disturbances, and fringing field effects and intermolecular forces, are also incorporated. The proposed backstepping controller is enhanced with an adaptive dynamic surface technique and an control scheme. The overall effectiveness of the designed controller is checked and illustrated with the presented results. These results show that the desired transient output tracking performance is achieved, and that the closed loop system exhibits good robustness to system uncertainties. Particular attention has been paid, so as to identify any relevance between the variations in the controller''s design parameter and the system''s output response and performance. [Copyright &y& Elsevier]

In this article on-line parameter estimation of dynamical systems is addressed in the context of model-based networked control systems (MB-NCSs). Stability conditions that are robust to parameter uncertainties and lack of feedback for extended intervals of time are presented. The updated model is used to control the real system the next time feedback information is unavailable. Additionally, new estimation models are proposed that offer better convergence properties than typical state-space parameter estimation methods; common assumptions such as availability of persistently exciting inputs and estimation of only a canonical form of the system are relaxed. The implementation of upgraded models in MB-NCSs results in better usage of the network by allowing longer intervals without the need for measurement updates. [ABSTRACT FROM AUTHOR]

The generalized projective synchronization GPS( of chaotic systems with uncertain parameter noise and external disturbance is discussed. Based on the adaptive technique, a response system is constructed, and a novel adaptive controller is designed to guarantee the GPS between the driveresponse systems, and to eliminate the effect of external disturbance and parameters noise on GPS. The conclusion is proved theoretically, and corresponding numerical simulations are provided to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a global task coordinate frame (TCF) (GTCF)-based integrated direct/indirect adaptive robust contouring controller for linear-motor-driven biaxial systems that achieves both stringent contouring performance and accurate parameter estimations. In contrast to the past research works those use various locally defined TCFs “attached” to the desired contour to approximately calculate the contouring error for feedback controller designs, this paper first formulates the contouring control problem using a recently developed GTCF, in which calculation of the contouring error is rather accurate and not affected by the curvature of the desired contour. A physical-model-based indirect-type parameter-estimation algorithm is then synthesized to obtain accurate online estimates of unknown physical model parameters. An integrated direct/indirect adaptive robust controller with dynamic-compensation-type fast adaptation is also constructed to preserve the excellent transient and steady-state performance of the direct adaptive robust control (ARC) designs. Comparative experimental results show that the proposed GTCF-based integrated direct/indirect ARC algorithm not only achieves the best contouring performance but also possesses rather accurate estimations of physical parameters. [ABSTRACT FROM PUBLISHER]

To overcome the signal-to-interference-and-noise ratio (SINR) performance degradation in the presence of large steering vector mismatches, we propose an iterative robust Capon beamformer (IRCB) with adaptive uncertainty level. The approach iteratively estimates the actual steering vector (SV) based on conventional robust Capon beamformer (RCB) formulation that uses an uncertainty sphere to model the mismatch between the actual and presumed SV. At each iteration, the adaptive uncertainty algorithm self-adjusts the uncertainty sphere according to the estimated mismatch SV. This estimation is derived based on the geometrical interpretation of the mismatch and can be expressed as a simple closed-form expression as a function of the presumed SV and the signal-subspace projection. The other variant of the proposed algorithm that uses a flat ellipsoid to model the mismatch is also proposed. Simulation results show that the proposed approaches offer better interference suppression capability and achieve higher output SINR, as compared to other diagonal-loading-based approaches. [ABSTRACT FROM AUTHOR]

We develop a new adaptive gain-scheduling control scheme for continuous-time linear systems with polytopic uncertainties. The gain-scheduled control law is proposed as a convex sum of a fixed set of controller gains, exploiting the polytopic representation of the system uncertainty, which is not possible with classical robust control results in the literature. To realize this scheme, an adaptation law is proposed to adaptively provide the tuning parameter for the gain-scheduling implementation. The admissible domain of the stabilizing control feedback gains, defined by the fixed set of controller gains, can be determined offline by solving a set of linear matrix inequality constraints over a scalar line search. Using Lyapunov-based arguments, the proposed design conditions and the adaptation law ensure that all closed-loop signals are bounded. In particular, if the uncertain parameters are not time-varying, then the system states asymptotically converge to the origin. Theoretical arguments and appropriate numerical illustrations are provided to demonstrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]