This paper presents an AC-DC converter system tailored for grid-to-vehicle (G2V) applications, aimed at facilitating efficient power flow while achieving a Unity power factor (UPF). The system employs a rectifier for AC-DC conversion, which effectively steps up a 230V AC input to a 380V DC output. This DC output can be further regulated using a buck converter to meet specific load requirements. A Proportional-Integral (PI) controller is implemented to oversee the voltage and current regulation, thereby minimizing harmonic distortion and enhancing the overall power factor. By actively managing the input voltage and current, the controller ensures that the system operates within desired parameters, thus optimizing power quality. Comprehensive simulation results validate the system’s performance, demonstrating its capability to maintain a UPF in G2V mode. The findings indicate significant reductions in total harmonic distortion (THD), reinforcing the system’s effectiveness in managing power quality. This AC-DC converter design not only enhances the efficiency of power flow in electric vehicle charging systems but also contributes to the stability of the grid by minimizing reactive power and harmonics. Overall, this work represents a significant advancement in converter technology for sustainable transportation and energy management

This chapter addresses a proper step-by-step mathematical procedure for implementing and designing the Type-2/3 compensators using the K-factor method that has not been previously described in any literature. This method offers a methodical and effective means of adjusting the K-factor and getting suitable values for compensator design. Synthesis of feedback compensators with any desired crossover frequency and phase margin is feasible using a few algebraic equations and the presented methodology. Type-2 compensators boost system damping to enhance closed-loop response, whereas Type-3 compensators insert a pole at the origin to eliminate steady-state error. This chapter examines the origin, use, advantages, disadvantages, and applications of the K-factor technique in the context of compensator design.

This article presents a promising self-learning-based robust control for output voltage tracking in DC–DC buck power converters, particularly for applications demanding high precision performance in face of large load uncertainties. The design involves a computationally simple online single hidden layer neural network, to rapidly estimate the unanticipated load changes and exogenous disturbances over a wide range. The controller is designed within a backstepping framework and utilizes the learnt uncertainty from the neural network for subsequent compensation, to eventually ensure an asymptotic stability of the tracking error dynamics. The results obtained feature a significant improvement of dynamic and steady-state performance concurrently for both output voltage and inductor current in contrast to other competent control strategies lately proposed in the literature for similar applications. Extensive numerical simulations and experimentation on a developed laboratory prototype are carried out to justify the practical applicability and feasibility of the proposed controller. Experimental results substantiate the claims of fast dynamic performance in terms of 94% reduction in the settling time, besides an accurate steady-state tracking for both output voltage and inductor current. Moreover, the close resemblance between computational and experimental results is noteworthy and unveils the immense potential of the proposed control system for technology transfer.

Since the capacitor current can reflect load variations faster than the peak inductor current, incorporating the capacitor current into control loops helps to improve the transient response of the dc-dc converter. In this paper, a coupled inductor single input dual output (CI-SIDO) buck converter is investigated under capacitor current ripple (CCR) control. A precise small-signal model for a CCR-controlled CI-SIDO buck converter operating in continuous conduction mode (CCM) is developed. The accurate small-signal model is obtained by substituting the derived CCR controller expressions in the CI-SIDO buck converter state-space model. The CCR controller equations specify the duty ratios as functions of the circuit variables namely the capacitor currents and the output voltages. It is observed that the CI-SIDO buck converter with the CCR controller exhibits instability when both or either of the duty ratios are greater than 0.7 or their sum is greater than 1. The results of the PLECS STANDALONE simulation validate the theoretical propositions as regards the switching instability of the controlled converter.

Concerning the power quality issues in the power distribution network due to load uncertainties and improper impedance matching of the inductances, deep reinforced learning (DRL)-based inductively coupled DSTATCOM (IC-DSTATCOM) is proposed. First, by analyzing the impedance matching principle, the expression of source, load and filter current is derived with the help of inductive filtering transformer. And second, an individual DRL subnet structure is accumulated for each phase using mathematical equations to perform the better dynamic response. A 10-kVA, 230-V, 50-Hz prototype direct coupled distributed static compensator (DC-DSTATCOM) and IC-DSTATCOM experimental setup is buit to verify the experimental performance under uncertainties of loading. The IC-DSTATCOM is augmented better dynamic performance in terms of harmonics curtailment, improvement in power factor, load balancing, potential regulation, etc. The benchmark IEEE-519-2017, IEC-61727 and IEC-61000-1 grid code are used to evaluate the effectiveness of the simulation and experimental study. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

This article presents an intelligent adaptive neural control scheme to track the output speed trajectory in power converter?driven DC motor system. The proposed technique integrates an adaptive polynomial?neural network with a backstepping strategy to yield a robust control system for output tracking in DC motor. Such a unification of online neural network?based estimation and adaptive control, results in effective regulation of the output across a wide load torque uncertainties, besides yielding a promising transient and steady?state performance. The stability of the entire closed?loop system is ensured through Lyapunov stability criterion. The efficacy of the proposed strategy is revealed through an extensive experimental investigation under various operating points during start?up, step?reference tracking, and external step?load torque disturbances. The real?time experimentation is conducted on a laboratory prototype of power converter?driven DC motor of 200W, using dspace DS1104 control board with MPC8240 processor. The results obtained confirm an improvement in the transient response of the output speed by significantly reducing the settling time to and yielding a steady state behavior with no peak over/undershoots during load disturbances, in contrast to other similar works presented in the literature intended for same the application.

This work investigates the application of Artificial Bee Colony (ABC) optimization for the design of Type compensators utilizing the dual-loop control scheme. The proposed Type compensators integrate the ABC optimization for regulating the closed-loop operation of a DC-DC buck converter. Such an integration of ABC optimization, aids in effectively regulating the output voltage and inductor current, besides ensuring enhanced time domain criteria. The proposed dual-loop control scheme consists of a current loop and a voltage loop. The current loop regulates the inductor current and the voltage loop regulates the output voltage. The efficacy of the proposed method is revealed through extensive simulation and experimental investigation under start-up response, step perturbations in external load. The experimentation is conducted on a laboratory prototype using dspace DS1104 control board.

As the capacitor current can respond to load fluctuations more rapidly than the peak inductor current, incorporating the capacitor current into the current control loop enhances the transient response in DC-DC converters as well as ensures over-current protection and noise immunity. This paper presents a comprehensive small-signal model (SSM) for a capacitor current ripple controlled (CCR) coupled inductor single-input dual-output (CI-SIDO) buck converter. The complete SSM is derived by unifying the developed SSM of the CCR controller with the SSM of the considered power converter using state-space averaging technique. In CCR control, the amalgamation of the comparator and SR flip-flop is accountable for producing the duty cycle. The proposed SSM is of immense usefulness in designing the outer loop controller, deriving switching instability conditions, and analyzing the dynamic characteristics of the capacitor current controlled CI-SIDO buck converter. To evaluate the advantages of this current controller to CI-SIDO buck converter, a load regulation analysis using the SSM of CCR controller is provided and thereafter verified through simulations in MATLAB/Simulink. It is observed that the low frequency gain of the cross and self regulation transfer functions is substantially less signifying promising dynamic and load disturbance rejection capability of the CCR control driven CI-SIDO buck converter.

This article proposes a novel Zernike radial neural network based adaptive control architecture for closed-loop control of output DC voltage in DC–DC buck power converter. The proposed combination of novel Zernike radial neural network estimator and the adaptive backstepping controller effectively compensates for wide range of perturbations affecting the converter system, in an online manner. The closed loop stability of the DC–DC buck power converter with the proposed neuro-adaptive backstepping controller is shown using Lyapunov stability criterion. Numerical simulations are conducted to examine the effectiveness of the proposed controller under start-up response and step changes in the load, source voltage and reference output voltage. Furthermore, the simulation findings are validated by conducting extensive real-time investigation on a laboratory prototype, under a wide range of operating points. The results obtained show a significant improvement in the transient response of both output voltage and inductor current of the converter, relative to the relevant control methods proposed in the recent past.

A novel neuro-adaptive control scheme is proposed in the context of angular velocity tracking in DC–DC buck converter driven permanent magnet DC motor system. The controller builds upon the idea of backstepping and consists of a fast single hidden layer Hermite neural network (HNN) module equipped with on-board (adaptive) learning to counteract the unknown non-linear time-varying load torque and to ensure nominal tracking performance. The HNN has a simple structure and exhibits promising speed and accuracy in estimating dynamic variations in the unknown load torque apart from being computationally efficient. The proposed method guarantees a rapid recovery of nominal angular velocity tracking under parametric and non-parametric uncertainties. In order to verify the performance of the proposed neuro-adaptive speed controller, extensive experimentation has been conducted in the laboratory under various real-time scenarios. Results are obtained for start-up, time-varying angular velocity tracking and under the influence of highly non-linear unknown load torque. The performance metrics such as peak undershoot/overshoot and settling time are computed to quantify the transient response behaviour. The results clearly substantiate theoretical propositions and demonstrate an enhanced dynamic speed tracking under a wide operating regime, thus confirming the suitability of proposed method for fast industrial applications.

A novel actuator failure compensation scheme is proposed for affine nonlinear uncertain systems (not necessarily Lipschitz) subject to persistent/intermittent actuator faults/failures unknown in time, magnitude and pattern. The proposed control methodology satisfies the nonlinear separation principle through a modular backstepping control. The controller is then augmented with multiple estimation models to estimate failure induced parametric uncertainties and unknown system parameters. The output transient performance at start up and post-failure instances, is improved on account of a two layer adaptation which enhances the convergence speed and accuracy of parameter estimates. The proposed fault tolerant control (FTC) method yields a faithful accommodation of uncertain finite as well as infinite/intermittent/persistent actuator failures while ensuring satisfactory output transient and steady state performances. Further, compared to existing multiple model based adaptive fault tolerant control design for nonlinear systems, the proposed methodology circumvents the issues of stability due to switching between different models and utilizes a minimum number of estimation models for parameter estimation without compromising on the output performance. Consequently, the computational burden is also reduced. Compared to multiple model adaptive control based FTC strategies proposed earlier which assume finite actuator failures and Lipschitz nonlinear system, the proposed method is applicable to both Lipschitz and non-Lipschitz nonlinear systems affected by intermittent actuator failures. Using the concepts from stability analysis in random nonlinear impulsive systems, the L? and L2 bounds on tracking error for all future time are derived in the case of intermittent/persistent actuator failures obtained using the proposed fault-tolerant controller. The improvement of output transient performance in the proposed control scheme in comparison with controller with single identifier, is theoretically proved and quantified.

DC-DC step-down/buck converters are prominent part of DC power supply system. The dynamics of DC-DC step down converter are nonlinear in nature and are largely influenced from both parametric and external load perturbations. Under its closed loop operation, obtaining a precise output voltage tracking besides satisfactorily inductor current response is a challenging control objective. In this regard, this article proposes a novel Laguerre neural network estimation technique for the approximation of unknown and uncertain load function, followed by its subsequent compensation in the adaptive backstepping controller. A detailed design of the proposed estimator and adaptive backstepping controller along with closed loop asymptotic stability have been presented. Further, the proposed control mechanism is evaluated through extensive numerical simulations while subjecting the converter to input voltage, reference voltage and load resistance perturbations. Furthermore, the results are verified by testing the proposed controller on a laboratory prototype with DSP based TM320F240 controller board. The transient performance metrics such as settling time and peak overshoot/undershoot are evaluated and compared against adaptive backstepping control and PID control methods. Finally, the analysis of results reveals that the proposed control methodology for DC-DC step down converter offers a faster transient output voltage tracking with smooth and satisfactory inductor current response over a wide operating range.