In this paper, we consider fault detection, isolation and reconstruction problem for descriptor systems with actuator faults and sensor faults, respectively. When actuator faults exist in the system, the fault detection and isolation (FDI) problem is solved through an unknown input observer regarding remaining faults excluded a specified fault as unknown inputs. Whereas, in existing sensor faults, the fault detection is only achieved by the unknown input observer and residual signals. Since the derivative signal of sensor fault is generated in the error dynamics between the actual system and the derived observer. The main objective of this work attempts the reconstruction of the faults. The reconstruction can be achieved by sliding mode observer including feedforward injection map and compensation signal. Finally, the isolation problem of sensor faults is solved by reconstructing all of the faults.

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Steer-by-Wire system (SbW), in which the conventional mechanical linkages between the steering wheel and the front wheel are removed, is suited to active steering control, improving vehicle stability, dynamics and maneuverability. And SbW is implemented to autonomous steering control to assist the driver. However, the SbW vehicle contains unsolved important problems about fault tolerant function. For example, it is the detection of sensor fault and multiplicative fault simultaneously. Fault detection and isolation (FDI) is essential in fault-tolerant problems, and conventional FDI for SbW was based on Kalman filter. But this method has weak robustness and cannot detect sensor fault and multiplicative fault simultaneously. We propose a novel model-based fault detection and isolation method using sliding mode observer in the SbW vehicle, which contains measurement of sensor fault and multiplicative fault. The effectiveness of the proposed method is verified by simulations.

This paper is concerned with learning and optimization of different basis function networks in the aspect of structure adaptation and parameter tuning. Basis function networks include the Volterra polynomial, Gaussian radial, B-spline, fuzzy, recurrent fuzzy, and local Gaussian basis function networks. Based on creation and evolution of the type constrained sparse tree, a unified framework is constructed, in which structure adaptation and parameter adjustment of different basis function networks are addressed using a hybrid learning algorithm combining a modified probabilistic incremental program evolution (MPIPE) and random search algorithm. Simulation results for the identification of nonlinear systems show the feasibility and effectiveness of the proposed method.

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