Design and Development of Current-Sensorless Peak Current Mode Controlled Switched Converters

Abstract

This thesis focuses on designing an observer-based controller with an accurate state space averaging (SSA) model for peak current mode (PCM) controlled non-isolated and isolated converters, including the duty ratio dynamics as a state variable. The previous methods of state estimation using SSA approach didn’t consider the dynamics of duty ratio. Hence, the accuracy of state estimation in peak current mode control (PCMC) was a matter of concern. Here, the investigations are done for ideal DC-DC converters, and then, the possible external disturbances and parameter uncertainties are considered to provide an accurate state and disturbance estimation. With the aid of well-established singular perturbation theories, the difficulties encountered in slow-fast switched converters (SCs) systems have been analysed and the design of an observer-based feedback controller has been performed. The various current mode control (CMC) techniques are reviewed and it is found that the PCMC is efficient and widely used techniques in power electronics systems due to their excellent transient responses and inherent current limiting capabilities. However, it has been found that the classical PCMC still have some inherent limitations and challenges, especially the subharmonic oscillations and noise sensitivity due to the use of current sensors. It is, therefore, necessary to address these issues and adopt an appropriate analysis technique to design a fast and robust PCMC under parameter uncertainty and input fluctuations. To deal with these problems, a well-established singular perturbation theory and the basic concepts of disturbance observer have been discussed. Then an observer-based PCMC technique for DC-DC buck converter in continuous conduction mode (CCM) is presented to eliminate the requirement of exact current sensing. This has been achieved by performing the successive time-scale decomposition of the composite slow-fast SCs system operating under the inner PCMC and then transforming it to a reduced slow subsystem model by neglecting the fast mode. Based on this reduced model, we design a full order state observer (FSO) and analyze the closed-loop system performances with the outer-loop integral controller. The state-space analysis technique with state augmentation is also used to obtain satisfactory steady-state performances and the natural mode of transient oscillations under different input fluctuations. We show that dynamic performances of the proposed control technique are not only in good agreement with the simulation results and confirm the theoretical analysis, but also exhibit very similar performances of classical PCM controllers having the full advantage of accurate current reference tracking without sensing the current at peaks. Thus, this new approach allows for simpler and more accurate state estimation. A significant contribution of this thesis is the proposed state estimation technique combined with an equivalent input disturbance (EID) estimator. This approach not only accurately estimates the system state and unknown bounded exogenous disturbances but also effectively eliminates the noise. It proves highly effective across a wide range of load, input, and parameter variations, enhancing system performance and robustness. Thus, a new robust controller is designed for multiscale PCMC to estimate states and reject the disturbances of uncertain SCs, e.g., buck and boost converters. This has been achieved by performing successive time-scale decomposition of PCMC and transforming them into the reduced-order subsystem model. Based on this reduced-order model, an FSO is designed and the closed-loop system with a PI controller and an EID estimator is analyzed to obtain desired transient and steady-state performances. Here, the Bellman-Gronwall lemma is employed to investigate the robustness analysis using the upper norm bounds of the uncertainties. The gains of the observer/controller are designed based on this stability condition. Experimental results showed that the developed method is quite effective and superior to the PI controller, and it can exhibit very similar performances to a classical PCMC without sensing the current at peaks. Unlike averaged current estimation techniques, this approach inherently acts as a current limiter to protect the SCs from overloads and reduces the impact of sensor noise when fast-scale duty-ratio dynamics are stable. Thus, this method allows us for robust and more accurate state estimation techniques than possible with previous methods and facilitates the use of advanced current-sensorless control concepts in many other uncertain SCs. The proposed technique is then implemented on an isolated flyback converter (FC). A similar observer-based feedback controller is designed based on the reduced system equation of a peak current mode controlled flyback converter (PCMC-FC) operating in CCM. Here, the reduced system dynamics have been achieved by performing time-scale decomposition of the slow-fast FC system, and an FSO is designed using the SSA technique. We show that dynamic performances of the proposed control technique are not only in good agreement with the simulation results and confirm the theoretical analysis but also exhibit very similar performances to classical pcm controllers having the full advantage of accurate current reference tracking without sensing the current at the peaks. In addition, the closed-loop system performances have been analysed for uncertain plant parameters under the variation of external inputs and reference voltage. The robustness analysis using Bellman-Gronwall’s lemma, and their experimental confirmations revealed that the proposed control approach can effectively handle significant input disturbances such as the wide load or input voltage variations with uncertain plant parameters. This new approach thus allows for both simpler and more accurate state estimation than that of the previous methods and allows the use of advanced control concepts in many other SCs and applications.