In this study, a novel identification technique, that is high-gain observer-based identification approach, is proposed for systems with bounded process and measurement noises. For system parameters with abnormal changes, an adaptive change detection and parameter identification algorithm is next presented. The presented technique and algorithm are finally applied to the parameter identification of the gas turbine engine by using the recorded input data from the engine test-bed. The identified parameters and the response curves are desirable. The simulations have proved the effectiveness of the proposed procedure compared with the previous identification approach.

For systems with both state and input time delays, a novel state and sensor fault observer is proposed in this paper to estimate system states and sensor faults simultaneously. In this design, a descriptor system approach and a linear matrix inequality technique are adopted, where the considered sensor fault may be in any form, even unbounded. Unbounded sensor faults will make the system fail unavoidably; it is indispensable to derive a reliable control scheme against sensor failures. Using the estimated state and sensor fault, a reliable observer-based controller is proposed, which makes the system work well nomatter whether sensor faults occur or not. The present approaches are next extended to the case for systems with multiple time delays. Finally, a simulation example of the network of three cascaded reactors is used to illustrate the design procedure and demonstrate the efficiency of the present techniques.

For dynamic systems with actuator faults, sensor faults, input disturbances and measurement noises, a novel high-gain estimation technique is presented in this paper to estimate system states, actuator faults and sensor faults simultaneously. The key idea is to represent faults as auxiliary system states so that a descriptor system representation can be formulated. By using the estimated state and fault signals,a fault-tolerant controller design approach is developed, where the closed-loop dynamic system can still be guaranteed to operate normally when actuator and sensor faults occur. It has been shown that in the proposed design framework,the actuator fault, sensor fault, input disturbance and measurement noise can appear simultaneously and can be allowed to be in different bounded forms. Finally, the proposed algorithm is applied to the simulation study of a three-shaft gas turbine system and desired results have been obtained.

For descriptor systems with measurement output noises (input disturbances may exist at the same time), a new descriptor estimator technique is developed. The necessary and sufficient condition for the existence of the present estimator is derived, and a systematic design approach is addressed. The effect of uncertainties is decoupled completely, and the asymptotic estimates of the descriptor system state and the output noise are obtained simultaneously. Furthermore, a normal state/disturbance estimator is also given. For a class of nonlinear descriptor systems with both output noises and input uncertainties, a nonlinear descriptor estimator is derived by using the proposed design approach, together with the linear matrix inequality technique. The existence and convergence of the nonlinear descriptor estimator is proven. The asymptotic estimates of the descriptor nonlinear system state and the output noise are obtained at the same time. The present estimators are applied to the sensor fault diagnosis, and hence the sensor fault can be estimated asymptotically. Finally, two numerical examples are included to illustrate the proposed design procedures and applications.

Doubly coprime factorization plays an important role in dealing with multivariable control problems using a factorization approach. In this study, based on an arbitrary and properly stabilizing controller,a general formula of doubly coprime factorization is obtained for use in descriptor systems. Detailed results have been derived on bridging the factorization approaches previously vailable in the literature.

In this note, we first point out that the main results by Wang and Balas regarding the doubly co-prime fractional representations for generalized dynamical systems have severe limitation in their applications, that is, the doubly coprime factorization obtained by Wang and Balas cannot characterize the parameterization of all properly stabilizing controllers when a system is singular, therefore, truly generalized. To remedy those results, we have developed two corresponding modified doubly co-prime factorizations that will parameterize all properly stabilizing controllers for single input or single output cases. In addition, the new results can characterize the parameterization of all causal properly stabilizing controllers in those cases. Finally, the extension to the multi-input and multi-output case is discussed.

In this study, a new design approach of observers with multiple integrals is presented for descriptor systems, which allows us to decouple or attenuate measurement output disturbances. New design schemes of pure integral observers and proportional integral observers are both investigated, and the measurement noise and the input disturbance are also taken into account simultaneously in this design. In particular, a systematic design procedure of causal or normal multiple-integral observer is addressed, which can be implemented conveniently by Matlab software.The considered disturbance may be unbounded,such that the present design approach is suitable for more general cases. Moreover,the system state, together with the disturbance vector and its finite times derivatives can be estimated simultaneously, the present design then has great potentials in control applications, e.g., diagnosis or observer-based control. The design approaches have nice estimation robustness and can be applied to normal systems directly. Finally, the simulation results show the excellent tracking performance compared to the known results for descriptor systems.

For state-space systems with unknown process disturbances, a robust state-space observer is designed to simultaneously estimate the system state, the finitely times derivatives of the fault,and the fault signal. The considered fault signal can be allowed to be unbounded provided its finitely times derivative is bounded. A numerical example is given to illustrate the design procedure. Vehicle lateral dynamic system plays an important role in the vehicle maneuverability, stability and driving safety. An application example with the real data for the vehicle lateral dynamic system is finally provided to demonstrate the efficiency of the present estimation technique.

For descriptor systems with both input faults and measurement faults, proportional, multiple-integral, and derivative (PMID) observer technique is presented to simultaneously estimate the system states, the fault signals and the finite times derivatives of the faults. For descriptor systems with input faults, input disturbances (or modeling errors) and output faults, a robust PMID observer is designed to simultaneously estimate the system states, the fault signals, the finite times derivatives of the faults, and attenuate the input disturbances (or modeling errors) successfully. The derivative gain is selected to make the observer error dynamics internally proper, and the proportional gain and the multiple-integral gains are chosen such that the error dynamics are internally stable and the effect of the input disturbances (or modeling errors) are prevailed over. The considered faults are allowed to be unbounded, thus the fault-tolerant control scheme is another interest of this study. The separation property between the proposed observer gains and the state-feedback gain is proven. Using the linear matrix inequality (LMI) technique, an PMID observer-based fault-tolerant control scheme is addressed. In the framework of the proposed fault-tolerant design, the closed-loop plant is guaranteed to be internally proper stable, the action of the faults to the plant is removed completely, and the effect of the input disturbance (or modeling error) to the plant output is also attenuated as desired. Finally, a numerical example is given to illustrate the design procedures, and simulations show the satisfactory tracking and fault-tolerant control performance.

The problem of robust observer-based fault detection is investigated for systems with bounded stochastic distributions. By constructing an auxiliary augmented stochastic descriptor system,proportional and derivative descriptor observer is developed to solve the fault detection. The system input and the probability density function are used in this observer design. The derivative gain is chosen to attenuate the output uncertainties, and the free parameters of the proportional gain are selected to generate an optimally robust residual signal for fault detection. It is shown that this residual signal is insensitive to the model uncertainties, input disturbances and output noises, but sensitive to system faults.