Abstract-The paper describes an identification technique of switching system. The considered system is represented as a weighted sum of local models. To estimate the switching times, a change detection technique is applied. It provides the weights associated to the local models. The Markov parameters of these models are identified by a subspace method. This calculation can yield similar local models which are merged. The procedure of parameter identification an models merging is repeated until convergence. The performance of the approach is investigated on a simulation example.
Robust switching detection and active mode recognition are addressed for switched nonlinear systems. The nonlinear modes are represented by Takagi-Sugeno (TS) models where the weighting functions are supposed to be online computable. The proposed switching detection technique and the active mode recognition method lie on indicator signals that are generated using a data-driven projection technique. The main advantage of this method is that it does not need the knowledge of local model parameter values. Only input-output data and the weighting functions of the TS model are used to generate these indicator signals. A numerical example is provided to illustrate the proposed approach.
This paper is concerned with Fault Detection and Isolation (FDI) and more specifically it focuses on a parameterfree residual generation method. The residual signals are obtained by projecting the measured signals onto the kernel of an extended input matrix, which depends on the structure of the system model. The method was not easily applicable in real-world applications due to a high computational complexity. In that paper, fault indicators are constructed differently, using kernels properties, to avoid this complexity problem. A simulated electromechanical actuator example is taken to illustrate the applicability of the method.
This paper proposes a data projection method (DPM) to detect a mode switching and recognize the current mode in a switching system. The main feature of this method is that the precise knowledge of the system model, i.e., the parameter values, is not needed. One direct application of this technique is fault detection and identification (FDI) when a fault produces a change in the system dynamics. Mode detection and recognition correspond to fault detection and identification, and switching time estimation to fault occurrence time estimation. The general principle of the DPM is to generate mode indicators, namely, residuals, using matrix projection techniques, where matrices are composed of input and output measured data. The DPM is presented in detail, and properties of switching detectability (fault detectability) and discernability between modes (fault identifiability) are characterized and discussed. The great advantage of this method, compared with other techniques in the literature, is that it does not need the model parameter values and thus can be applied to systems of the same type without identifying their parameters. This is particularly interesting in the design of generic embedded fault diagnosis algorithms.
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