167 research outputs found
Microprocessor based signal processing techniques for system identification and adaptive control of DC-DC converters
PhD ThesisMany industrial and consumer devices rely on switch mode power converters (SMPCs) to provide a reliable, well regulated, DC power supply. A poorly performing power supply can potentially compromise the characteristic behaviour, efficiency, and operating range of the device. To ensure accurate regulation of the SMPC, optimal control of the power converter output is required. However, SMPC uncertainties such as component variations and load changes will affect the performance of the controller. To compensate for these time varying problems, there is increasing interest in employing real-time adaptive control techniques in SMPC applications. It is important to note that many adaptive controllers constantly tune and adjust their parameters based upon on-line system identification. In the area of system identification and adaptive control, Recursive Least Square (RLS) method provide promising results in terms of fast convergence rate, small prediction error, accurate parametric estimation, and simple adaptive structure. Despite being popular, RLS methods often have limited application in low cost systems, such as SMPCs, due to the computationally heavy calculations demanding significant hardware resources which, in turn, may require a high specification microprocessor to successfully implement. For this reason, this thesis presents research into lower complexity adaptive signal processing and filtering techniques for on-line system identification and control of SMPCs systems.
The thesis presents the novel application of a Dichotomous Coordinate Descent (DCD) algorithm for the system identification of a dc-dc buck converter. Two unique applications of the DCD algorithm are proposed; system identification and self-compensation of a dc-dc SMPC. Firstly, specific attention is given to the parameter estimation of dc-dc buck SMPC. It is computationally efficient, and uses an infinite
impulse response (IIR) adaptive filter as a plant model. Importantly, the proposed method is able to identify the parameters quickly and accurately; thus offering an efficient hardware solution which is well suited to real-time applications. Secondly, new alternative adaptive schemes that do not depend entirely on estimating the plant parameters is embedded with DCD algorithm. The proposed technique is based on a simple adaptive filter method and uses a one-tap finite impulse response (FIR) prediction error filter (PEF). Experimental and simulation results clearly show the DCD technique can be optimised to achieve comparable performance to classic RLS algorithms. However, it is computationally superior; thus making it an ideal candidate technique for low cost microprocessor based applications.Iraq Ministry of Higher Educatio
Soft-Decision-Driven Sparse Channel Estimation and Turbo Equalization for MIMO Underwater Acoustic Communications
Multi-input multi-output (MIMO) detection based on turbo principle has been shown to provide a great enhancement in the throughput and reliability of underwater acoustic (UWA) communication systems. Benefits of the iterative detection in MIMO systems, however, can be obtained only when a high quality channel estimation is ensured. In this paper, we develop a new soft-decision-driven sparse channel estimation and turbo equalization scheme in the triply selective MIMO UWA. First, the Homotopy recursive least square dichotomous coordinate descent (Homotopy RLS-DCD) adaptive algorithm, recently proposed for sparse single-input single-output system identification, is extended to adaptively estimate rapid time-varying MIMO sparse channels. Next, the more reliable a posteriori soft-decision symbols, instead of the hard decision symbols or the a priori soft-decision symbols, at the equalizer output, are not only feedback to the Homotopy RLS-DCD-based channel estimator but also to the minimum mean-square-error (MMSE) equalizer. As the turbo iterations progress, the accuracy of channel estimation and the quality of the MMSE equalizer are improved gradually, leading to the enhancement in the turbo equalization performance. This also allows the reduction in pilot overhead. The proposed receiver has been tested by using the data collected from the SHLake2013 experiment. The performance of the receiver is evaluated for various modulation schemes, channel estimators, and MIMO sizes. Experimental results demonstrate that the proposed a posteriori soft-decision-driven sparse channel estimation based on the Homotopy RLS-DCD algorithm and turbo equalization offer considerable improvement in system performance over other turbo equalization schemes
Microprocessor based signal processing techniques for system identification and adaptive control of DC-DC converters
Many industrial and consumer devices rely on switch mode power converters (SMPCs) to provide a reliable, well regulated, DC power supply. A poorly performing power supply can potentially compromise the characteristic behaviour, efficiency, and operating range of the device. To ensure accurate regulation of the SMPC, optimal control of the power converter output is required. However, SMPC uncertainties such as component variations and load changes will affect the performance of the controller. To compensate for these time varying problems, there is increasing interest in employing real-time adaptive control techniques in SMPC applications. It is important to note that many adaptive controllers constantly tune and adjust their parameters based upon on-line system identification. In the area of system identification and adaptive control, Recursive Least Square (RLS) method provide promising results in terms of fast convergence rate, small prediction error, accurate parametric estimation, and simple adaptive structure. Despite being popular, RLS methods often have limited application in low cost systems, such as SMPCs, due to the computationally heavy calculations demanding significant hardware resources which, in turn, may require a high specification microprocessor to successfully implement. For this reason, this thesis presents research into lower complexity adaptive signal processing and filtering techniques for on-line system identification and control of SMPCs systems. The thesis presents the novel application of a Dichotomous Coordinate Descent (DCD) algorithm for the system identification of a dc-dc buck converter. Two unique applications of the DCD algorithm are proposed; system identification and self-compensation of a dc-dc SMPC. Firstly, specific attention is given to the parameter estimation of dc-dc buck SMPC. It is computationally efficient, and uses an infinite impulse response (IIR) adaptive filter as a plant model. Importantly, the proposed method is able to identify the parameters quickly and accurately; thus offering an efficient hardware solution which is well suited to real-time applications. Secondly, new alternative adaptive schemes that do not depend entirely on estimating the plant parameters is embedded with DCD algorithm. The proposed technique is based on a simple adaptive filter method and uses a one-tap finite impulse response (FIR) prediction error filter (PEF). Experimental and simulation results clearly show the DCD technique can be optimised to achieve comparable performance to classic RLS algorithms. However, it is computationally superior; thus making it an ideal candidate technique for low cost microprocessor based applications.EThOS - Electronic Theses Online ServiceIraq Ministry of Higher EducationGBUnited Kingdo
Self-Interference Cancellation for Full-Duplex Underwater Acoustic Systems
This work develops and investigates self-interference (SI) cancellation (SIC) techniques for full-duplex (FD) underwater acoustic (UWA) systems. To enable the FD operation in UWA systems, a high level of SIC is required. The main approach used in this work is the digital cancellation based on adaptive filtering. A general structure of the digital canceller is proposed which addresses key factors affecting the SIC performance, including the power amplifier and pre-amplifier nonlinearities, up- and down-sampling effects. With the proposed structure, the SI can be effectively cancelled in time-invariant channels by classical recursive least-square (RLS) adaptive filters, e.g., the sliding-window RLS (SRLS), but the SIC performance degrades in time-varying channels. A new SRLS adaptive filter based on parabolic interpolation of the channel time variations is proposed, which improves the SIC performance at the expense of the high complexity. To reduce the complexity, while providing the high SIC, a new family of interpolating adaptive filters which combine the SRLS adaptive algorithm with Legendre polynomials (SRLS-L) is proposed. A sparse adaptive filter is further proposed to exploit the sparsity in the expansion coefficients of the Legendre polynomials. For interpolating adaptive filtering algorithms, the mean squared error is unsuitable for measuring the SIC performance due to the overfitting. Therefore, a new evaluation metric, SIC factor, is proposed. The SIC performance of the proposed adaptive filters is investigated and compared with that of the classical SRLS algorithm by simulation, water tank and lake experiments. Results indicate that the proposed adaptive filters significantly improve the SIC performance in time-varying scenarios, especially with high-order sparse SRLS-L adaptive filter. Furthermore, SIC schemes with multiple antennas are investigated to explore the possibility of achieving extra amount of SIC in acoustic domain and cancelling the fast-varying surface reflections by adaptive beamforming
System identification and adaptive current balancing ON/OFF control of DC-DC switch mode power converter
PhD ThesisReliability becomes more and more important in industrial application of Switch Mode Power
Converters (SMPCs). A poorly performing power supply in a power system can influence its operation
and potentially compromise the entire system performance in terms of efficiency. To maintain a high
reliability, high performance SMPC effective control is necessary for regulating the output of the SMPC
system. However, an uncertainty is a key factor in SMPC operation. For example, parameter variations
can be caused by environmental effects such as temperature, pressure and humidity. Usually, fixed
controllers cannot respond optimally and generate an effective signal to compensate the output error
caused by time varying parameter changes. Therefore, the stability is potentially compromised in this
case. To resolve this problem, increasing interest has been shown in employing online system
identification techniques to estimate the parameter values in real time. Moreover, the control scheme
applied after system identification is often called “adaptive control” due to the control signal selfadapting to the parameter variation by receiving the information from the system identification process.
In system identification, the Recursive Least Square (RLS) algorithm has been widely used because it
is well understood and easy to implement. However, despite the popularity of RLS, the high
computational cost and slow convergence speed are the main restrictions for use in SMPC applications.
For this reason, this research presents an alternative algorithm to RLS; Fast Affline Projection (FAP).
Detailed mathematical analysis proves the superior computational efficiency of this algorithm.
Moreover, simulation and experiment result verify this unique adaptive algorithm has improved
performance in terms of computational cost and convergence speed compared with the conventional
RLS methods. Finally, a novel adaptive control scheme is designed for optimal control of a DC-DC
buck converter during transient periods. By applying the proposed adaptive algorithm, the control signal
can be successfully employed to change the ON/OFF state of the power transistor in the DC-DC buck
converter to improve the dynamic behaviour. Simulation and experiment result show the proposed
adaptive control scheme significantly improves the transient response of the buck converter, particularly
during an abrupt load change conditio
Modelling and Adaptive Control of a DC-DC Buck Converter
With the advancement of electronic industry the requirement of low power supply is essential as numerous industrial and commercial devices rely on power converters for regulated and reliable DC power source. The demands of DC-DC converters are increasing exponentially because of their high efficiency, small size as well as simple architecture. The complexity in modelling of DC –DC converter mainly depends on its usage and its sophistication as it ranges from simple analogue design for low cost application to digital and self-adaptive model for better performance. This paper comprises of method for obtaining the small signal model of DC-DC buck converter by linearizing it using state space averaging technique. Both state space as well as non- linear model of Buck converter is the simulated in MATLAB and desired response is observed. This paper also discuss the methods of design and implementation of controller for Buck converter .The purpose of the compensation is to modify the dynamic characteristics of the converter in order to satisfy the performance specifications of the Buck converter. The performance specifications of the converter are maximum peak overshoot, settling time and steady state requirements and should be stated precisely so that the optimal control of the converter can be obtained. In this research we are interested in two approaches that are commonly used in the digitally controlled design of buck converter, the pole-zero matching approach, which provides a simple discrete time difference equation, and the systematic pole placement method. This thesis also focuses on a new alternative adaptive schemes that do not depend entirely on estimating the plant parameters is embedded with LMS algorithm. The proposed technique is based on a simple adaptive filter method and uses a one-tap finite impulse response (FIR) prediction error filter (PEF).Simulation results clearly show the LMS technique can be optimized to achieve comparable performance to classic algorithms. However, it is computationally superior; thus making it an ideal candidate technique for low cost microprocessor based applications
Mitigation of DC Current Injection in Transformerless Grid-Connected Inverters
PhD ThesisWith a large number of small-scale PV plants being connected to the utility grid, there is increasing
interest in the use of transformerless systems for grid-connected inverter photovoltaic applications.
Compared to transformer-coupled solutions, transformerless systems offer a typical efficiency
increase of 1-2%, reduced system size and weight, and reductions in cost. However, the removal
of the transformer has technical implications. In addition to the loss of galvanic isolation, DC
current injection into the grid is a potential risk. Whilst desirable, the complete mitigation of DC
current injection via conventional current control methods is known to be particularly challenging, and
there are remaining implementation issues in previous studies. For this reason, this thesis aims to
minimize DC current injection in grid-connected transformerless PV inverter systems.
The first part of the thesis reviews the technical challenges and implementation issues in published
DC measurement techniques and suppression methods. Given mathematical models, the
performance of conventional current controllers in terms of DC and harmonics mitigation is
analyzed and further confirmed in simulations and experiments under different operating
conditions. As a result, the second part of the thesis introduces two DC suppression methods, a DC
voltage mitigation approach and a DC link current sensing technique. The former method uses a
combination of a passive attenuation circuit and a software filter stage to extract the DC voltage
component, which allows for further digital control and DC component mitigation at the inverter
output. It is proven to be a simple and highly effective solution, applicable for any grid-connected
PV inverter systems. The DC link sensing study then investigates a control-based solution in which
the dc injection is firstly accurately determined via extraction of the line frequency component
from the DC link current and then mitigated with a closed loop. With an output current
reconstruction process, this technique provides robust current control and effective DC suppression
based on DC link current measurement, eliminating the need for the conventional output current
sensor. Results from rated simulation models and a laboratory grid-connected inverter system are
presented to demonstrate the accurate and robust performance of the proposed techniques.
This thesis makes a positive contribution in the area of power quality control in grid-connected
inverters, specifically mitigating the impact of DC injection into the grid which has influences on
the network operating conditions and the design and manufacture of the PV power converter itsel
Channel Estimation for Massive MIMO Systems
Massive multiple input multiple output (MIMO) systems can significantly improve the channel
capacity by deploying multiple antennas at the transmitter and receiver. Massive MIMO
is considered as one of key technologies of the next generation of wireless communication
systems. However, with the increase of the number of antennas at the base station, a large
number of unknown channel parameters need to be dealt with, which makes the channel
estimation a challenging problem. Hence, the research on the channel estimation for massive
MIMO is of great importance to the development of the next generation of communication
systems. The wireless multipath channel exhibits sparse characteristics, but the traditional
channel estimation techniques do not make use of the sparsity. The channel estimation
based on compressive sensing (CS) can make full use of the channel sparsity, while use
fewer pilot symbols. In this work, CS channel estimation methods are proposed for massive
MIMO systems in complex environments operating in multipath channels with static and
time-varying parameters. Firstly, a CS channel estimation algorithm for massive MIMO
systems with Orthogonal Frequency Division Multiplexing (OFDM) is proposed. By exploiting
the spatially common sparsity in the virtual angular domain of the massive MIMO
channels, a dichotomous-coordinate-decent-joint-sparse-recovery (DCD-JSR) algorithm is
proposed. More specifically, by considering the channel is static over several OFDM symbols
and exhibits common sparsity in the virtual angular domain, the DCD-JSR algorithm can
jointly estimate multiple sparse channels with low computational complexity. The simulation
results have shown that, compared to existing channel estimation algorithms such as the
distributed-sparsity-adaptive-matching-pursuit (DSAMP) algorithm, the proposed DCD-JSR
algorithm has significantly lower computational complexity and better performance. Secondly, these results have been extended to the case of multipath channels with time-varying
parameters. This has been achieved by employing the basis expansion model to approximate
the time variation of the channel, thus the modified DCD-JSR algorithm can estimate the
channel in a massive MIMO OFDM system operating over frequency selective and highly
mobile wireless channels. Simulation results have shown that, compared to the DCD-JSR
algorithm designed for time-invariant channels, the modified DCD-JSR algorithm provides
significantly better estimation performance in fast time-varying channels
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