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This paper presents a novel fuzzy-augmented model reference adaptive voltage regulation strategy for the DC–DC buck converters to enhance their resilience against random input variations and load-step transients. The ubiquitous proportional-integral-derivative (PID) controller is employed as the baseline scheme, whose gains are tuned offline via a pre-calibrated linear-quadratic optimization scheme. However, owing to the inefficacy of the fixed-gain PID controller against parametric disturbances, it is retrofitted with a model reference adaptive controller that uses Lyapunov gain adaptation law for the online modification of PID gains. The adaptive controller is also augmented with an auxiliary fuzzy self-regulation system that acts as a superior regulator to dynamically update the adaptation rates of the Lyapunov gain adaptation law as a nonlinear function of the system's classical error and its normalized acceleration. The proposed fuzzy system utilizes the knowledge of the system's relative rate to execute better self-regulation of the adaptation rates, which in turn, flexibly steers the adaptability and response speed of the controller as the error conditions change. The propositions above are validated by performing tailored hardware experiments on a low-power DC–DC buck converter prototype. The experimental results validate the improved reference tracking and disturbance rejection ability of the proposed control law compared to the fixed PID controller. [ABSTRACT FROM AUTHOR]

This paper proposed a fully integrated adaptive on-time (AOT) controlled buck converter with fast load transient. An adaptive on-time generator is presented to stabilize the output frequency. To enhance the light load efficiency, the converter could transfer from the pulse width modulation (PWM) to pulse skip modulation (PSM) as the load current decreases. The buck converter can switch between these two modulation modes adaptively with the assistance of a zero current detection circuit. Implemented in the TSMC 0.18 µm BCD (BiCMOS/DMOS) process, the proposed buck converter works with an input voltage ranging from 5.5 to 15 V, an output voltage ranging from 0.5 to 5 V, and an output load ranging up to 5 A. The experimental results show that based on the dual modulation adaptive on-time controlled mode, the transient recovery time from light to heavy load and from heavy load to light load is 13 µs and 15 µs, respectively. An overshot voltage of 57 mV and an undershot voltage of 53 mV are also achieved. [ABSTRACT FROM AUTHOR]

In this paper, a novel adaptive second-order sliding mode (2-SM) control approach, based on online zero-crossing detection, was proposed to solve the problems of the chattering and fixed control gain for buck converters with multi-disturbances. In modeling, the possible parameter perturbations and external disturbances of the converter system were contained. Instead of the traditional first-order sliding mode (1-SM), the twisting algorithm with 2-SM was adopted for the controller design, which could overcome the chattering problem and realize control continuity. Meanwhile, a novel adaptive mechanism was introduced to replace the conventional fixed control gain by time-varying control gain, the idea of which is to calculate the number of the zero-crossing points of the sliding surface online. As a result, the control magnitude of the improved controller could be reduced to a minimal admissible level, and the steady error of the output voltage could converge to the expected value. Furthermore, the robust stability of the converter system with multi-disturbances wads investigated. Comparative simulations and experiments validated the advantages of this paper as offering better robustness and control performance. [ABSTRACT FROM AUTHOR]

This paper demonstrates the viability and effectiveness of a novel adaptive control approach applied to power electronic converters. A methodology based on the formulation of a Lyapunov-based approach is showcased to represent the operation of a new adaptive controller for the regulation of two power converter topologies (Buck and Boost). The models of the Buck and Boost converter topologies include the parasitic parameters that represent the non-ideal components. The basic idea of the control approach is to demonstrate adaptive stabilization for the proposed non-linear system. The most important design specification to stabilize the system is to track the reference trajectory in such a way that the error on the output variable converges asymptotically to zero. This adaptation mechanism is explicitly designed so that the asymptotic stability of the equilibrium condition is guaranteed according to the Lyapunov theorem and sensitivity theory. The details of the design algorithm are explained in the paper. The proposed control approach has been compared to other Lyapunov-based control techniques proposed in literature for the same non-ideal converters. The results show that the proposed controller provides better level of robustness and performance than the other well-established Lyapunov based controllers. To verify the effectiveness of the controller in real time, a test bench has been set up with prototypes of both converters and the controller has been implemented using the Arduino microcontroller and the control system driven through the Matlab/Simulink platform. • Proposal of a novel adaptive control approach for DC–DC power electronic converters. • The controller is asymptotically stable according to Lyapunov. • Inclusion of the parasitic parameters in the converter models. • First application of the proposed control approach to DC–DC converters. • Successful performance of the proposed control proven by experimental results. [ABSTRACT FROM AUTHOR]

This paper presents a state‐dependent self‐tuning fractional control strategy for a DC‐DC buck converter in order to enhance its output voltage regulation and disturbance attenuation capability. The proposed control scheme primarily employs a ubiquitous proportional‐integral‐derivative (PID) controller, where gains are optimally selected using a linear‐quadratic state‐space tuning approach. The optimal PID controller is then augmented with fractional‐order integral and derivative operators in order to improve the controller's degrees‐of‐freedom as well as the system's overall time‐domain performance. The fractional controller's robustness against bounded exogenous disturbances, contributed by the input fluctuations and load‐step transients, is further enhanced by adaptively modulating the fractional‐orders of the integro‐differential operators as a smooth nonlinear function of controlled‐variable's error‐dynamics. An online dynamic adjustment law, comprising of a zero‐mean Gaussian function of error and its derivative, is used to individually update the two fractional orders after every sampling interval. The error derivative is evaluated by measuring the output capacitor's current in order to compensate the noise injected by parasitic impedance. The other controller parameters are tuned via particle‐swarm‐optimization algorithm. The proposed self‐adaptive control strategy renders rapid transits, minimum transient recovery time, and minimal fluctuations around steady state in the response. Its efficacy is validated through hardware in‐the‐loop experiments conducted on a buck converter prototype. [ABSTRACT FROM AUTHOR]

The Navy's future and near-term high-energy sensors and energy weapons will consume a large portion of the resources of the intended ship platform. Many of these new systems will have extreme dynamic power profiles, including both periodic and aperiodic characteristics. These dynamics can cause sudden changes in power at the prime power system that can be stressing to platform systems, both to the generators and prime movers as well as other loads sharing the common distribution bus. This paper presents the use of a new adaptive power system (APS) to mitigate the negative impacts levied on the platforms resulting from large dynamic loads. A notional size of the hardware required to implement the APS design is presented along with simulation results verifying the concept. [ABSTRACT FROM AUTHOR]

The existing results mainly focus on the full-information control for converters with constant power load (CPL). More precisely, it is assumed that all of the states of the systems should be measured. There are no theoretical results on an adaptive sensorless control scheme for the converters feeding CPL with the guaranteed stability. Note that a sensorless control will facilitate the decrease of the overall cost and fault rate as well as the increase of the reliability of the systems. In this paper, the sensorless control problem for DC–DC buck converter with unknown CPL is addressed. The main contribution of this paper is to design a reduced-order generalized parameter estimation-based observer to simultaneously reconstruct the unmeasurable inductor current and unknown power load of the system. Borrowing the dynamic regressor extension and mixing technique, its main idea is to transform the state observation into the parameter estimation problem. Besides, the finite-time convergence of the observer can be ensured. It is noted that the buck converter is only regarded as an application example. As a matter of fact, this observer can be extended to a large class of converters with CPLs. Then, introducing the observed terms into an existing full-information controller, in a certainty equivalent manner, an adaptive sensorless control scheme is achieved. Finally, the performance of the designed controller is assessed via simulation and experimental results. • A reduced-order state observer is designed for buck converter with unknown CPL. • This observer design can be extended to a large class of power converters with CPLs. • An adaptive sensorless controller is proposed for the system. • Simulation and experimental results of the designed controller are presented. [ABSTRACT FROM AUTHOR]

A loss-adaptive self-oscillating buck converter is proposed for high-efficiency and low-cost LED driving. It consists of a self-oscillating unit composed of bipolar junction transistors (BJTs)-type self-oscillating unit, a loss-adaptive BJT driving unit, and a low-loss peak-current sensing unit. In this paper, its operation principle including a loss-adaptive BJT-driving scheme and a low-loss peak-current sensing method is introduced. For experimental verification, a prototype LED driver was implemented with some cheap components and devices for a 24-V lighting system to drive up to six LEDs. The test results show that the prototype LED driver could successfully start up itself and operate highly efficiently in steady state. In order to improve the performance of the proposed buck converter, a useful pulse-width modulation (PWM) LED dimming function is discussed for the future study. [ABSTRACT FROM PUBLISHER]

This paper demonstrates Robust Adaptive Control (RAC) approach using a technique for system identification applied to a Buck (step-down) converter by PWM (Pulse width modulation) in the presence of input voltage variations, load variations, parametric variations and high variance noises. Maintaining stability margins and closed-loop regulation within a pre-determined range of operating condition is the main objective behind designing feedback loops. The proposed control technique omits the need for mathematical model of the system by using a black-box identification procedure. Due to the various disturbing factors on a DC–DC converter, Minimum Degree Pole Placement (MDPP) scheme is proposed as an adaptive control technique, which is optimized with an Improved Exponential Regressive Least identification (IERLS) algorithm. Additionally, to hinder the impact and the uncertainty caused by high variance noise, a robust identification algorithm is introduced. Finally, capability of the proposed method is examined in different operating conditions through simulations and experimental results. • The estimation of parameters values is reached more accurately. • An Identification strategy is presented which handles parametric variations. • Different disturbances are covered by adaptive controller. • The impact of high variance noises is determined by a novel robust estimator. [ABSTRACT FROM AUTHOR]