Optimal Step Size Technique for Frequency Domain and Partition Block Adaptive Filters for PEM based Acoustic Feedback Cancellation

The adaptive filtering approach has been commonly used to perform acoustic feedback cancellation (AFC) in digital hearing-aids due to its reliable performance and feasibility. Because the loudspeaker and microphone are close together in hearing aids, the corresponding signals are highly correlated, resulting in biased estimation if adaptive filters are used. This problem can be addressed with the help of the decorrelation prefilter by incorporating the Prediction Error Method (PEM) technique into AFC. Frequency-Domain Adaptive Filters (FDAF) are preferable over the time-domain implementation to achieve better performance in terms of convergence and computational complexity. In addition, Partition-Block Frequency-Domain Adaptive Filters (PBFDAF) offers low processing delay. However, because of their fixed step-size, there is a trade-off between initial convergence and steady-state misalignment in the widely used frequency-domain algorithms. While Variable Step-Size (VSS) algorithms can help with this issue, VSS techniques for frequency-domain algorithms have not been extensively studied in the context of PEM-AFC. Hence, in this paper, we presented an Optimal Step-Size (OSS) technique for both the FDAF-PEM_AFC and PBFDAF-PEM_AFC algorithms to simultaneously accomplish fast convergence and minimal steady-state error. A Feedback Path Change Detector (FPCD) was also incorporated into the proposed algorithms to address the problem of convergence in non-stationary feedback paths. The results of simulations show that the proposed algorithms are clearly superior, and they are encouraging

Control of feedback for assistive listening devices

Acoustic feedback refers to the undesired acoustic coupling between the loudspeaker and microphone in hearing aids. This feedback channel poses limitations to the normal operation of hearing aids under varying acoustic scenarios. This work makes contributions to improve the performance of adaptive feedback cancellation techniques and speech quality in hearing aids. For this purpose a two microphone approach is proposed and analysed; and probe signal injection methods are also investigated and improved upon

Real-time system identification and self-tuning control of DC-DC power converter using Kalman Filter approach

Ph. D. ThesisSwitch-mode power converters (SMPCs) are employed in many industrial and consumer devices. Due to the continuous reduction in cost of microprocessors, and improvements in the processing power, digital control solutions for SMPCs have become a viable alternative to traditional analogue controllers. However, in order to achieve high-performance control of modern DC-DC converters, using direct digital design techniques, an accurate discrete model of the converter is necessary. This model can be acquired by means of prior knowledge about the system parameters or using system identification methods. For the best performance of the designed controller, the system identification methods are preferred to handle the model uncertainties such as component variations and load changes. This process is called indirect adaptive control, where the model is estimated from input and output data using a recursive algorithm and the controller parameters are tuned and adjusted accordingly. In the parameter estimation step, Recursive Least Squares (RLS) method and its modifications exhibit very good identification metrics (fast convergence rate, accurate estimate, and small prediction error) during steady-state operation. However, in real-time implementation, the accuracy of the estimated model using the RLS algorithm is affected by measurement noise. Moreover, there is a need to continuously inject an excitation signal to avoid estimator wind-up. In addition, the computational complexity of RLS algorithm is high which demands significant hardware resources and hence increase the overall cost of the digital system. For these reasons, this thesis presents a robust parametric identification method, which has the ability to provide accurate estimation and computationally efficient self-tuning controller suitable for real-time implementation in SMPCs systems. This thesis presents two complete real-time solutions for parametric system identification and explicit self-tuning control for SMPCs. The first is a new parametric estimation method, based on a state of the art Kalman Filter (KF) algorithm to estimate the discrete model of a synchronous DC-DC buck converter. The proposed method can accurately identify the discrete coefficients of the DC-DC converter. This estimator possesses the advantage of providing an independent strategy for adaptation of each individual parameter; thus offering a robust and reliable solution for real-time parameter estimation. To improve the tracking performance of the proposed KF, an adaptive tuning technique is proposed. Unlike many other published schemes, this approach offers the unique advantage of updating the parameter vector coefficients at different rates. This thesis also validates the performance of the identification algorithm with time-varying parameters; such as an abrupt load change. Furthermore, the proposed method demonstrates robust estimation with and without an excitation signal, which makes it very well suited for real-time power electronic control applications. Additionally, the estimator convergence time is significantly shorter compared to many other schemes, such as the classical Exponentially weighted Recursive Least Square (ERLS) method. To design a computationally efficient self-tuning controller for DC-DC SMPCs, the second part of the thesis develops a complete package for real-time explicit self-tuning control. The novel partial update KF (PUKF) is introduced for real-time parameter estimation. In this approach, a significant complexity reduction is attained as the number of arithmetic operations are reduced, more specifically the computation of adaptation gains and covariance updates. The explicit self-tuning control scheme is constructed via integrating the developed PUKF with low complexity control algorithm such as Bányász/Keviczky PID controller. Experimental and simulation results clearly show an enhancement in the overall dynamic performance of the closed loop control system compared to the conventional PID controller designed based on a pre-calculated average model. Importantly, in this thesis, unlike a significant proportion of existing literature, the entire system identification, and closed loop control process is seamlessly implemented in real-time hardware, without any remote intermediate post processing analysis.Ministry of Higher Education, General Electricity Company of Liby

Retrospective Cost Adaptive Control with Concurrent Closed-Loop Identification

Retrospective cost adaptive control (RCAC) is a discrete-time direct adaptive control algorithm for stabilization, command following, and disturbance rejection. RCAC is known to work on systems given minimal modeling information which is the leading numerator coefficient and any nonminimum-phase (NMP) zeros of the plant transfer function. This information is normally needed a priori and is key in the development of the filter, also known as the target model, within the retrospective performance variable. A novel approach to alleviate the need for prior modeling of both the leading coefficient of the plant transfer function as well as any NMP zeros is developed. The extension to the RCAC algorithm is the use of concurrent optimization of both the target model and the controller coefficients. Concurrent optimization of the target model and controller coefficients is a quadratic optimization problem in the target model and controller coefficients separately. However, this optimization problem is not convex as a joint function of both variables, and therefore nonconvex optimization methods are needed. Finally, insights within RCAC that include intercalated injection between the controller numerator and the denominator, unveil the workings of RCAC fitting a specific closed-loop transfer function to the target model. We exploit this interpretation by investigating several closed-loop identification architectures in order to extract this information for use in the target model.PHDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138440/1/fsobolic_1.pd

Modeling, Control and Characterization of Aircraft Electric Power Systems

A study model of advanced aircraft electric power system (AAEPS) corresponding to B767 Aircraft is developed in the PSIM9 software environment. The performance characteristics of the system under consideration for large sharing of non-linear loads are studied. A comprehensive mathematical model describing system dynamics is derived where the GSSA technique is applied for reduced-order system approximation. The transient and steady-state performance of the hybrid PEM-FC/battery APU integrated to the aircraft electric network is analyzed while different loading scenarios are taken into account. In addition, dynamic bifurcation analysis is employed to characterize the systems stability performance under multi-parameters condition. Also, the power quality of the system is assessed under various loading configurations, and the effect of installing active/passive power filters (APF/PPF) on power quality of the system is investigated for a wide range of operating frequencies