اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدDesign of a capacitor-less low-dropout voltage regulator
95
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
A solution to the stability of capacitor-less low-dropout regulators with a 4pF Miller capacitor in Multi-level current amplifier is proposed. With the Miller compensation, a more than 50{deg}phase margin is guaranteed in full load. An extra fast transient circuit is adopted to reduce stable time and peak voltage. When the load changes from light to heavy, the peak voltage is 40mV and chip quiescent current is only 45uA.
قيم البحث
اقرأ أيضاً
The inherent stochasticity in many nano-scale devices makes them prospective candidates for low-power computations. Such devices have been demonstrated to exhibit probabilistic switching between two stable states to achieve stochastic behavior. Recen
tly, superparamagnetic nanomagnets (having low energy barrier EB $sim$ 1kT) have shown promise of achieving stochastic switching at GHz rates, with very low currents. On the other hand, voltage-controlled switching of nanomagnets through the Magneto-electric (ME) effect has shown further improvements in energy efficiency. In this simulation paper, we first analyze the stochastic switching characteristics of such super-paramagnetic nanomagnets in a voltage-controlled spintronic device. We study the influence of external bias on the switching behavior. Subsequently, we show that our proposed device leverages the voltage controlled stochasticity in performing low-voltage 8-bit analog to digital
This paper proposes a new equivalent circuit model for rechargeable batteries by modifying a double-capacitor model proposed in [1]. It is known that the original model can address the rate capacity effect and energy recovery effect inherent to batte
ries better than other models. However, it is a purely linear model and includes no representation of a batterys nonlinear phenomena. Hence, this work transforms the original model by introducing a nonlinear-mapping-based voltage source and a serial RC circuit. The modification is justified by an analogy with the single-particle model. Two parameter estimation approaches, termed 1.0 and 2.0, are designed for the new model to deal with the scenarios of constant-current and variable-current charging/discharging, respectively. In particular, the 2.0 approach proposes the notion of Wiener system identification based on maximum a posteriori estimation, which allows all the parameters to be estimated in one shot while overcoming the nonconvexity or local minima issue to obtain physically reasonable estimates. An extensive experimental evaluation shows that the proposed model offers excellent accuracy and predictive capability. A comparison against the Rint and Thevenin models further points to its superiority. With high fidelity and low mathematical complexity, this model is beneficial for various real-time battery management applications.
The topic of this paper is to use an intuitive model-based approach to design a networked controller for a recent benchmark scenario. The benchmark problem is to remotely control a two-wheeled inverted pendulum robot via W-LAN communication. The robo
t has to keep a vertical upright position. Incorporating wireless communication in the control loop introduces multiple uncertainties and affects system performance and stability. The proposed networked control scheme employs model predictive techniques and deliberately extends delays in order to make them constant and deterministic. The performance of the resulting networked control system is evaluated experimentally with a predefined benchmarking experiment and is compared to local control involving no delays.
The output impedance matrices of three-phase grid-connected voltage source converters (VSCs) are widely used in power system stability analysis. Regardless of how the impedance is modeled, there always exist coupling terms in the impedance matrix, wh
ich makes the system a multi-input- multi-output (MIMO) system. Some approximation approaches omit the coupling terms so that a three-phase system can be treated like a single-phase one, and the impedance-based stability criterion for a single-input-single-output (SISO) system is applicable. However, such handling may result in analytical errors or even incorrect conclusions in a mirror frequency coupled system. By introducing the concept of generalized- impedances, this letter proposes a new stability criterion based on a virtual SISO system, which can effectively handle the coupling terms. Further, the effects of the phase-locked-loop (PLL) parameters on system stability are studied based on the proposed criterion. The effectiveness of the proposed criterion is verified by a hardware-in-the-loop (HIL) simulation based on RT-LAB.
In this paper we propose a new computational method for designing optimal regulators for high-dimensional nonlinear systems. The proposed approach leverages physics-informed machine learning to solve high-dimensional Hamilton-Jacobi-Bellman equations
arising in optimal feedback control. Concretely, we augment linear quadratic regulators with neural networks to handle nonlinearities. We train the augmented models on data generated without discretizing the state space, enabling application to high-dimensional problems. We use the proposed method to design a candidate optimal regulator for an unstable Burgers equation, and through this example, demonstrate improved robustness and accuracy compared to existing neural network formulations.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
