The nonlinearities and unmodeled dynamics inevitably degrade the quality and reliability of power conversion, and as a result, pose big challenges on higher-performance voltage stabilization of dc–dc buck converters. The stability of such power electronic equipment is further threatened when feeding the nonideal constant power loads (CPLs) because of the induced negative impedance specifications. In response to these challenges, the advanced regulatory and technological mechanisms associated with the converters require to be developed to efficiently implement these interface systems in the microgrid configuration. This article addresses an intelligent proportional-integral based on sliding mode (SM) observer to mitigate the destructive impedance instabilities of nonideal CPLs with time-varying nature in the ultralocal model sense. In particular, in the current article, an auxiliary deep deterministic policy gradient (DDPG) controller is adaptively developed to decrease the observer estimation error and further ameliorate the dynamic characteristics of dc–dc buck converters. The design of the DDPG is realized in two parts: (i) an actor-network which generates the policy commands, while (ii) a critic-network evaluates the quality of the policy command generated by the actor. The suggested strategy establishes the DDPG-based control to handle for what the iPI-based SM observer is unable to compensate. In this application, the weight coefficients of the actor and critic networks are trained based on the reward feedback of the voltage error, by using the gradient descent scheme. Finally, to investigate the merits and implementation feasibility of the suggested method, some experimental results on a laboratory prototype of the dc–dc buck converter, which feeds a time-varying CPL, are presented.

A Novel Nonlinear Deep Reinforcement Learning Controller for DC–DC Power Buck Converters

Recently, a modularized smart grid (SG) architecture, entitled the Internet of Things (IoT) grid, is developed that accommodates the IoT technology into the dc–dc converters to build a programmable grid with a single voltage bus. This modern architecture can be established with low computing hardware that facilitates the control and management of the IoT-based grids. Due to the uncertainties originated from the integration of the IoT technology and power electronic converters, the deterministic methodologies are unable to precisely model the SG anymore. In response to these challenges, this article addresses a novel adaptive data-driven method based on the active disturbance rejection controller (ADRC) for the voltage regulation of an IoT-based dc–dc buck converter feeding constant power loads. In particular, a deep deterministic policy gradient (DDPG) with the actor-critic architecture is adopted for the online adjusting of the ADRC controller. The established DDPG takes into account the ADRC controller coefficients into the design objective and offers the ADRC controller with the online coefficient setting ability through the neural network learning. The IoT-based system is tested on a real-time testbed with the constrained application protocol protocol and IEEE 802.11 (Wi-Fi) network to assess the applicability of the suggested controller in the presence of network degradations. The impact of both packet loss and interfering traffic on the reduction performance of the DDPG adaptive ADRC controller is investigated, simultaneously. The supremacy of the suggested adaptive data-driven controllers is verified by a comprehensive comparative analysis with the state-of-the-art methodologies.

IoT-Based DC/DC Deep Learning Power Converter Control: Real-Time Implementation

This paper presents the design, simulation and realtime implementation of a digital PID controller for DC-DC buck converter. The developed controller is capable to drive the output voltage of the buck converter to track a desired voltage reference regardless of input voltage or load variations. The parameters of the digital PID controller are calculated based on the transfer function of the buck converter and Matlab PIDTOOl. An Arduino Uno is used to implement the developed controller. Numerical simulation and experimental validation are presented to demonstrate the effectiveness of the developed controller in terms of overshoot limitations and accuracy.

Design and Implementation of a Digital PID Controller for DC–DC Buck Converter

A technique to achieve output tracking for nonminimum phase nonlinear systems with nonhyperbolic internal dynamics is presented. The present paper integrates stable inversion techniques (that achieve exact-tracking) with approximation techniques (that modify the internal dynamics) to circumvent the nonhyperbolicity of the internal dynamics-this nonhyperbolicity is an obstruction to applying presently available stable inversion techniques. The theory is developed for nonlinear systems and the method is applied to a two-cart with inverted pendulum example.

Approximated Stable Inversion for Nonlinear Systems with Nonhyperbolic Internal Dynamics. Revised

This paper presents a novel form of feedback linearization control (FBL) of boost-type DC/DC converter: to reach highly accurate output voltage control. Integral action has been inserted into the block diagram of the control scheme. The state-space model of the boost converter is highly nonlinear. Accordingly, the design procedure of the controller is more complex. The paper presents the state-space modeling of the boost converter and details the design procedure of the nonlinear FBL controller step by step. The main goal of this paper is to highlight the importance of the error integrator in the FBL control loop. The proposed method has been tested by a numerical example and compared with an existing and validated two-loop controller. Both the dynamical and steady-state behavior of the examined boost converter performed better than the reference system. The steady-state error of the output voltage is almost eliminated, while the dynamical error decreased to 5% in comparison to the two-loop controller.

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A Novel Control Scheme Based on Exact Feedback Linearization Achieving Robust Constant Voltage for Boost Converter

A DC-DC converter with constant power loads (CPLs) is a type of strongly nonlinear system. CPLs have the characteristic of negative incremental impedance, which may cause system instability during disturbances if the system is not properly controlled. Additionally, parameter uncertainty renders the control problem of nonlinear systems more complex and difficult. In this paper, a feedback linearization adaptive control strategy is proposed to improve the stability of a buck converter with CPLs. Feedback linearization is adopted based on the inverse system method to linearize the large signal average model of the buck converter with CPLs, and a pseudo-linear system is obtained. Then, a model reference adaptive control technology is applied to design a controller for the pseudo-linear system. Finally, compared with PID control, simulation and experimental results show that the proposed control strategy can achieve faster start-up without overshoots and stronger robustness in restraining resistive loads and CPL disturbances.

Jiarong Wu

Papers

Feedback Linearization Adaptive Control for a Buck Converter with Constant Power Loads