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

Dynamic modelling and control of dual active bridge bi-directional DC-DC converters for smart grid applications

The Smart Grid needs energy storage to cope with the highly volatile energy generated by renewable energy sources. Power converters that employ DAB bi-directional DC-DC converters are commonly used to transfer this stored energy to and from the Smart Grid. To maintain grid stability and ensure good transient performance, fast and accurate control of these converters is required. The aim of this thesis is to design a high performance closed-loop regulator for a DAB converter that can achieve a very fast transient response. To achieve this goal, a dynamic representation of the DAB converter dynamics is derived based on the significant harmonics present in the converter switching signals. It is then identified in this work that deadtime can have a significant effect on converter dynamics, so a series of closed form expressions that predict the effect of deadtime across all operating conditions were derived. The prediction is used to extend the harmonic model, achieving a first order, two-input, small-signal state space model that was verified in simulation and then matched to an experimental DAB converter. This new harmonic model was used to investigate the performance limits of a closed loop P+I (Proportional + Integral) voltage regulator for the DAB converter, and several enhancements to maximise its performance were developed. First, it was found that the controller gains are limited by transport delay, which is inherent to the digital implementation of the controller. Accounting for this delay allowed the maximum possible controller gains to be calculated. Second, it was found that the plant gain changes significantly with operating point, so controller gains are recalculated dynamically across the entire operating range to maintain consistent operation. Third, load current was found to act as a disturbance to the system, severely compromising performance. A feed-forward disturbance rejection algorithm was developed and applied to the closed loop regulator to resolve this problem. The new regulator was tested in a Smart Grid AC load application, where the DAB converter was used as a DC supply for a H-bridge DC-AC inverter. The excellent voltage regulation achieved by the new closed loop controller significantly reduced the output capacitance required to maintain the DAB output voltage under both steady-state and transient conditions. This result offers the potential to eliminate the traditional electrolytic capacitor used in these applications, with associated size, cost and lifetime benefits. All ideas in this thesis were verified on a 1kW prototype DAB bi-directional DC-DC converter

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

Polynomial Curve Slope Compensation for Peak-Current-Mode-Controlled Power Converters

Linear ramp slope compensation (LRC) and quadratic slope compensation (QSC) are commonly implemented in peak-current-mode-controlled dc-dc converters in order to minimize subharmonic and chaotic oscillations. Both compensating schemes rely on the linearized state-space averaged model (LSSA) of the converter. The LSSA ignores the impact that switching actions have on the stability of converters. In order to include switching events, the nonlinear analysis method based on the Monodromy matrix was introduced to describe a complete-cycle stability. Analyses on analog-controlled dc-dc converters applying this method show that system stability is strongly dependent on the change of the derivative of the slope at the time of switching instant. However, in a mixed-signal-controlled system, the digitalization effect contributes differently to system stability. This paper shows a full complete-cycle stability analysis using this nonlinear analysis method, which is applied to a mixed-signal-controlled converter. Through this analysis, a generalized equation is derived that reveals for the first time the real boundary stability limits for LRC and QSC. Furthermore, this generalized equation allows the design of a new compensating scheme, which is able to increase system stability. The proposed scheme is called polynomial curve slope compensation (PCSC) and it is demonstrated that PCSC increases the stable margin by 30% compared to LRC and 20% to QSC. This outcome is proved experimentally by using an interleaved dc-dc converter that is built for this work

A RISC-V Vector Processor With Simultaneous-Switching Switched-Capacitor DC-DC Converters in 28 nm FDSOI

This work demonstrates a RISC-V vector microprocessor implemented in 28 nm FDSOI with fully integrated simultaneous-switching switched-capacitor DC-DC (SC DC-DC) converters and adaptive clocking that generates four on-chip voltages between 0.45 and 1 V using only 1.0 V core and 1.8 V IO voltage inputs. The converters achieve high efficiency at the system level by switching simultaneously to avoid charge-sharing losses and by using an adaptive clock to maximize performance for the resulting voltage ripple. Details about the implementation of the DC-DC switches, DC-DC controller, and adaptive clock are provided, and the sources of conversion loss are analyzed based on measured results. This system pushes the capabilities of dynamic voltage scaling by enabling fast transitions (20 ns), simple packaging (no off-chip passives), low area overhead (16%), high conversion efficiency (80%-86%), and high energy efficiency (26.2 DP GFLOPS/W) for mobile devices

Energy-aware MPC co-design for DC-DC converters

In this paper, we propose an integrated controller design methodology for the implementation of an energy-aware explicit model predictive control (MPC) algorithms, illustrat- ing the method on a DC-DC converter model. The power consumption of control algorithms is becoming increasingly important for low-power embedded systems, especially where complex digital control techniques, like MPC, are used. For DC-DC converters, digital control provides better regulation, but also higher energy consumption compared to standard analog methods. To overcome the limitation in energy efficiency, instead of addressing the problem by implementing sub-optimal MPC schemes, the closed-loop performance and the control algorithm power consumption are minimized in a joint cost function, allowing us to keep the controller power efficiency closer to an analog approach while maintaining closed-loop op- timality. A case study for an implementation in reconfigurable hardware shows how a designer can optimally trade closed-loop performance with hardware implementation performance

Analysis of CLL voltage-output resonant converters using describing functions

A new ac equivalent circuit for the CLL voltage output resonant converter is presented, that offers improved accuracy compared with traditional FMA-based techniques. By employing describing function techniques, the nonlinear interaction of the parallel inductor, rectifier and load is replaced by a complex impedance, thereby facilitating the use of ac equivalent circuit analysis methodologies. Moreover, both continuous and discontinuous rectifier-current operating conditions are addressed. A generic normalized analysis of the converter is also presented. To further aid the designer, error maps are used to demonstrate the boundaries for providing accurate behavioral predictions. A comparison of theoretical results with those from simulation studies and experimental measurements from a prototype converter, are also included as a means of clarifying the benefits of the proposed techniques

Measurements, Models, Systems and Design

531 s.