13,941 research outputs found

    Robust Observer Design for Takagi-Sugeno Fuzzy Systems with Mixed Neutral and Discrete Delays and Unknown Inputs

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    A robust observer design is proposed for Takagi-Sugeno fuzzy neutral models with unknown inputs. The model consists of a mixed neutral and discrete delay, and the disturbances are imposed on both state and output signals. Delay-dependent sufficient conditions for the design of an unknown input T-S observer with time delays are given in terms of linear matrix inequalities. Some relaxations are introduced by using intermediate variables. A numerical example is given to illustrate the effectiveness of the given results

    A review of convex approaches for control, observation and safety of linear parameter varying and Takagi-Sugeno systems

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    This paper provides a review about the concept of convex systems based on Takagi-Sugeno, linear parameter varying (LPV) and quasi-LPV modeling. These paradigms are capable of hiding the nonlinearities by means of an equivalent description which uses a set of linear models interpolated by appropriately defined weighing functions. Convex systems have become very popular since they allow applying extended linear techniques based on linear matrix inequalities (LMIs) to complex nonlinear systems. This survey aims at providing the reader with a significant overview of the existing LMI-based techniques for convex systems in the fields of control, observation and safety. Firstly, a detailed review of stability, feedback, tracking and model predictive control (MPC) convex controllers is considered. Secondly, the problem of state estimation is addressed through the design of proportional, proportional-integral, unknown input and descriptor observers. Finally, safety of convex systems is discussed by describing popular techniques for fault diagnosis and fault tolerant control (FTC).Peer ReviewedPostprint (published version

    Zonotopic fault detection observer design for Takagi–Sugeno fuzzy systems

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    This paper considers zonotopic fault detection observer design in the finite-frequency domain for discrete-time Takagi–Sugeno fuzzy systems with unknown but bounded disturbances and measurement noise. We present a novel fault detection observer structure, which is more general than the commonly used Luenberger form. To make the generated residual sensitive to faults and robust against disturbances, we develop a finite-frequency fault detection observer based on generalised Kalman–Yakubovich–Popov lemma and P-radius criterion. The design conditions are expressed in terms of linear matrix inequalities. The major merit of the proposed method is that residual evaluation can be easily implemented via zonotopic approach. Numerical examples are conducted to demonstrate the proposed methodPeer ReviewedPostprint (author's final draft

    Observer-based fault diagnosis using multiple-model and LMI techniques

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    The ever-increasing complexity of technical processes requires a higher performance, safety and reliability. For this reason, fault detection and isolation (FDI), which consists of residual generation and residual evaluation, has received more attention in the last years. Most technical processes are represented by a nonlinear system; however it is possible to apply FDI techniques only for a few classes of nonlinear systems. In the last years, the idea of using an aggregation of local models (multiple-models), as a means to capture the global dynamic characteristics of nonlinear systems, has been successfully integrated in the field of FDI. These multiple-models have been used as an alternative for dealing with nonlinear systems. An advantage of using multiple-models for FDI is that the theory for linear systems can be used for nonlinear systems. This thesis mainly focuses on the design of robust FDI schemes for nonlinear systems using multiple-model approaches. The considered approaches are (i) the Takagi-Sugeno (TS) fuzzy model (ii) linear systems with polytopic uncertainty. Three robust FDI schemes based on TS fuzzy models are presented. The first scheme generalizes the linear unknown input observer to a class of nonlinear systems described by TS fuzzy models. The objective of this scheme is to decouple the unknown inputs for residual generation. The second scheme handles nonlinear systems affected by stochastic disturbances; this scheme minimizes the expected steady state estimation error using linear matrix inequality (LMI) techniques. The last one simultaneously enhances the robustness to unknown inputs without sacrificing the fault detection sensitivity. For linear systems with polytopic uncertainty, a robust fault detection filter is designed considering a reference model. The residuals can be evaluated with a threshold based on this filter. The effectiveness of each proposed robust FDI scheme is demonstrated with the help of four application examples

    Active fault tolerant control for nonlinear systems with simultaneous actuator and sensor faults

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    The goal of this paper is to describe a novel fault tolerant tracking control (FTTC) strategy based on robust fault estimation and compensation of simultaneous actuator and sensor faults. Within the framework of fault tolerant control (FTC) the challenge is to develop an FTTC design strategy for nonlinear systems to tolerate simultaneous actuator and sensor faults that have bounded first time derivatives. The main contribution of this paper is the proposal of a new architecture based on a combination of actuator and sensor Takagi-Sugeno (T-S) proportional state estimators augmented with proportional and integral feedback (PPI) fault estimators together with a T-S dynamic output feedback control (TSDOFC) capable of time-varying reference tracking. Within this architecture the design freedom for each of the T-S estimators and the control system are available separately with an important consequence on robust Lâ‚‚ norm fault estimation and robust Lâ‚‚ norm closed-loop tracking performance. The FTTC strategy is illustrated using a nonlinear inverted pendulum example with time-varying tracking of a moving linear position reference. Keyword

    Robust fault estimation for stochastic Takagi-Sugeno fuzzy systems

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    Nowadays, industrial plants are calling for high-performance fault diagnosis techniques to meet stringent requirements on system availability and safety in the event of component failures. This paper deals with robust fault estimation problems for stochastic nonlinear systems subject to faults and unknown inputs relying on Takagi-Sugeno fuzzy models. Augmented approach jointly with unknown input observers for stochastic Takagi-Sugeno models is exploited here, which allows one to estimate both considered faults and full system states robustly. The considered unknown inputs can be either completely decoupled or partially decoupled by observers. For the un-decoupled part of unknown inputs, which still influence error dynamics, stochastic input-to-state stability properties are applied to take nonzero inputs into account and sufficient conditions are achieved to guarantee bounded estimation errors under bounded unknown inputs. Linear matrix inequalities are employed to compute gain matrices of the observer, leading to stochastic input-to-state-stable error dynamics and optimization of the estimation performances against un-decoupled unknown inputs. Finally, simulation on wind turbine benchmark model is applied to validate the performances of the suggested fault reconstruction methodologies
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