Novel Direct and Accurate Identification of Kalman Filter for General Systems Described by a Box-Jenkins Model

A novel robust Kalman filter (KF)-based controller is proposed for a multivariable system to accurately track a specified trajectory under unknown stochastic disturbance and measurement noise. The output is a sum of uncorrelated signal, disturbance and measurement noise. The system model is observable but not controllable while the signal one is controllable and observable. An emulator-based two-stage identification is employed to obtain a robust model needed to design the robust controller. The system and KF are identified and the signal and output error estimated. From the identified models, minimal realizations of the signal and KF, the disturbance model and whitening filter are obtained using balanced model reduction techniques. It is shown that the signal model is a transfer matrix relating the system output and the KF residual, and the residual is the whitened output error. The disturbance model is identified by inverse filtering. A feedback-feedforward controller is designed and implemented using an internal model of the reference driven by the error between the reference and the signal estimate, the feedforward of reference and output error. The successful evaluation of the proposed scheme on a simulated autonomously-guided drone gives ample encouragement to test it later, on a real one

Fault Detection and Isolation

Fault diagnosis of a class of linear multiple‐input and multiple‐output (MIMO) systems is developed here. An emulator‐based scheme is proposed to detect and isolate faults in a system formed by interconnected subsystems. Emulators, which are hardware or software devices, are connected to the input and measurement outputs in cascade with the subsystems whose faults are to be diagnosed. The role of an emulator is to induce variations in cascade combination of the nominal fault‐free subsystem so as to mimic the actual perturbations that may occur in the subsystem during the offline identification phase. The emulator‐generated data are employed in the reliable identification of the nominal system, the associated Kalman filter, and a map that relates the emulator parameters to the feature vector. In the operational stage, the Kalman filter residual is used to detect a fault in the system; the emulator parameter that has varied is estimated, and using the emulator‐feature vector map, the faulty subsystem is isolated. The main contributions of this work are accurate and reliable identification of the system, the fault diagnosis of multivariable systems using feature vector-emulator map fault diagnosis of multivariable systems, and the establishment of the key properties of the Kalman filter for fault detection. The proposed scheme was successfully evaluated on a number of simulated as well as physical systems

Detection and Localization of a Failure in a Pipeline Using a Kalman Filter: An Intelligent Integrated Approach Powered by Bayesian Classification

An integrated approach, based on the fusion of Model-Based Approach (MBA) and Model-Free Approaches (MFA) and powered by Bayesian classification, is proposed to ensure high probability of correct estimation of leakage detection and localization with low false alarm probability to prevent disastrous consequences to the economy and environment. To ensure mathematical tractability, the nonlinear model is better approximated using linear parameter-varying (LPV) model at various operating points indicated by scheduling variables. Flows at various pipeline sections are measured and transmitted wirelessly to a monitoring station. If there is a difference in the flows across a section, it indicates a leakage, and a drone is then sent to determine the exact location of the leakage. The pipeline trajectory is accurately estimated by a human operator. Using the input and the trajectory output, termed signal, an Autonomous Kalman filter (AKF) is designed to ensure accurate tracking of the desired trajectory. The emulator-generated data are used to identify the system, complement historical data to MFA, and develop the classifier fusion. The leakage is sequentially diagnosed by judiciously selecting the most appropriate approach (MFA or MBA) to ensure a fast and accurate diagnosis. The proposed scheme was evaluated on a physical system