Coupling Matrix Extraction of Microwave Filters by Using One-Dimensional Convolutional Autoencoders

The tuning of microwave filter is important and complex. Extracting coupling matrix from given S-parameters is a core task for filter tuning. In this article, one-dimensional convolutional autoencoders (1D-CAEs) are proposed to extract coupling matrix from S-parameters of narrow-band cavity filter and apply this method to the computer-aided tuning process. The training of 1D-CAE model consists of two steps. First, in the encoding part, one-dimensional convolutional neural network (1D-CNN) with several convolution layers and pooling layers is used to extract the coupling matrix from the S-parameters during the microwave filters’ tuning procedure. Second, in the decoding part, several full connection layers are employed to reconstruct the S-parameters to ensure the accuracy of extraction. The S-parameters obtained by measurement or simulation exist with phase shift, so the influence of phase shift must be removed. The efficiency of the presented method in this article is validated by a sixth-order cross-coupled filter simulation model tuning example.

Implementing an Extended Kalman Filter for SoC Estimation of a Li-Ion Battery with Hysteresis: A Step-by-Step Guide

The growing share of renewable energies in power production and the rise of the market share of battery electric vehicles increase the demand for battery technologies. In both fields, a predictable operation requires knowledge of the internal battery state, especially its state of charge (SoC). Since a direct measurement of the SoC is not possible, Kalman filter-based estimation methods are widely used. In this work, a step-by-step guide for the implementation and tuning of an extended Kalman filter (EKF) is presented. The structured approach of this paper reduces efforts compared with empirical filter tuning and can be adapted to various battery models, systems, and cell types. This work can act as a tutorial describing all steps to get a working SoC estimator based on an extended Kalman filter.

Particle Swarm Optimization Based Adaptive Kalman Filters for Fault Diagnosis of Reaction Wheels

There has been an increasing interest in fault diagnosis in recent years, as a result of the growing demand for higher performance, efficiency, reliability and safety in control systems. A faulty sensor or actuator may cause process performance degradation, process shut down, or a fatal accident. Quick fault detection and isolation can help avoid abnormal event progression and minimize the quality and productivity offsets. In space systems specifically, space and power are limited in the satellites, which means that hardware redundancy is not very practical. If actuator faults occur, analytical redundancy techniques should be employed to determine if, where, and how the fault(s) occurred. To do so, different approaches have been developed and studied and one of the wellknown approaches in the literature is using the Kalman Filter as an observer for the purpose of parameter estimation and fault detection. The gains for the filter should be selected and the selection of the process and measurement noise statistics, commonly referred to as “filter tuning,” is a major implementation issue for the Kalman filter. This process can have a significant impact on the filter performance. In practice, Kalman filter tuning is often an ad-hoc process involving a considerable amount of time for trial and error to obtain a filter with desirable –qualitative or quantitative- performance characteristics. This thesis focuses on presenting an algorithm for automation of the selection of the gains using an evolutionary swarm intelligence based optimization algorithm (Particle Swarm) to minimize the residuals of the estimated parameters. The methodology can be applied to any filter or controller but in this thesis, an Adaptive Unscented Kalman Filter parameter estimation applied to a reaction wheel unit is used for the purpose of performance evaluation of the proposed methodology.

Particle Swarm Optimization Based Adaptive Kalman Filters for Fault Diagnosis of Reaction Wheels

There has been an increasing interest in fault diagnosis in recent years, as a result of the growing demand for higher performance, efficiency, reliability and safety in control systems. A faulty sensor or actuator may cause process performance degradation, process shut down, or a fatal accident. Quick fault detection and isolation can help avoid abnormal event progression and minimize the quality and productivity offsets. In space systems specifically, space and power are limited in the satellites, which means that hardware redundancy is not very practical. If actuator faults occur, analytical redundancy techniques should be employed to determine if, where, and how the fault(s) occurred. To do so, different approaches have been developed and studied and one of the wellknown approaches in the literature is using the Kalman Filter as an observer for the purpose of parameter estimation and fault detection. The gains for the filter should be selected and the selection of the process and measurement noise statistics, commonly referred to as “filter tuning,” is a major implementation issue for the Kalman filter. This process can have a significant impact on the filter performance. In practice, Kalman filter tuning is often an ad-hoc process involving a considerable amount of time for trial and error to obtain a filter with desirable –qualitative or quantitative- performance characteristics. This thesis focuses on presenting an algorithm for automation of the selection of the gains using an evolutionary swarm intelligence based optimization algorithm (Particle Swarm) to minimize the residuals of the estimated parameters. The methodology can be applied to any filter or controller but in this thesis, an Adaptive Unscented Kalman Filter parameter estimation applied to a reaction wheel unit is used for the purpose of performance evaluation of the proposed methodology.