KEYWORDS: Control systems design, Control systems, Filtering (signal processing), System identification, Sensors, Actuators, Error analysis, Feedback control, Matrices, Electronic filtering
In this paper, an adaptive reconfigurable control system based on
extended Kalman filter approach and eigenstructure assignments is proposed. System identification is carried out using an extended Kalman filter (EKF) approach. An eigenstructure assignment (EA)
technique is applied for reconfigurable feedback control law
design to recover the system dynamic performance. The reconfigurable feedforward controllers are designed to achieve the steady-state tracking using input weighting approach. The proposed scheme can identify not only actuator and sensor variations, but
also changes in the system structures using the extended Kalman filtering method. The overall design is robust with respect to uncertainties in the state-space matrices of the reconfigured system. To illustrate the effectiveness of the proposed
reconfigurable control system design technique, an aircraft longitudinal vertical takeoff and landing (VTOL) control system is used to demonstrate the reconfiguration procedure.
In this paper, we have proposed diagnostic techniques using a
multilayered neural network where the weights in the network are
updated using node-decoupled extended Kalman filter (NDEKF)
training method. Sensor signals in both time domain and frequency
domain are analyzed to show the effectiveness of the NDEKF
algorithm in each domain. Comparisons of the NDEKF algorithm with
other popular neural network training algorithms such as extended
Kalman filter (EKF) and backpropagation (BP) will be discussed in
the paper through a system identification problem. First, the
simulation results reveal the comparison of outputs from actual
system and trained neural network. Secondly, the ability of
diagnosing a system with one normal condition and three known
fault conditions is demonstrated. Thirdly, the robustness of the
machine condition monitoring when the inputs to the system vary is
shown. The proposed technique works even when there is noise in
sensor signals as well.
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