Comparison of different repetitive control architectures: synthesis and comparison. Application to VSI Converters

Repetitive control is one of the most used control approaches to deal with periodic references/disturbances. It owes its properties to the inclusion of an internal model in the controller that corresponds to a periodic signal generator. However, there exist many different ways to include this internal model. This work presents a description of the different schemes by means of which repetitive control can be implemented. A complete analytic analysis and comparison is performed together with controller synthesis guidance. The voltage source inverter controller experimental results are included to illustrative conceptual developmentsPeer ReviewedPostprint (published version

Sensored and sensorless speed control methods for brushless doubly fed reluctance motors

The study considers aspects of scalar V/f control, vector control and direct torque (and flux) control (DTC) of the brushless doubly fed reluctance machine (BDFRM) as a promising cost-effective alternative to the existing technological solutions for applications with restricted variable speed capability such as large pumps and wind turbine generators. Apart from providing a comprehensive literature review and analysis of these control methods, the development and results of experimental verification, of an angular velocity observerbased DTC scheme for sensorless speed control of the BDFRM which, unlike most of the other DTC-concept applications, can perform well down to zero supply frequency of the inverter-fed winding, have also been presented in the study

Continuous dynamic sliding mode control strategy of PWM based voltage source inverter under load variations

For closed-loop controlled DC-AC inverter system, the performance is highly influenced by load variations and online current measurement. Any variation in the load will introduce unwanted periodic error at the inverter output voltage. In addition, when the current sensor is in faulty condition, the current measurement will be imprecise and the designed feedback control law will be ineffective. In this paper, a sensorless continuous sliding mode control (SMC) scheme has been proposed to address these issues. The chattering effect due to the discontinuous switching nature of SMC has been attenuated by designing a novel boundary-based saturation function where the selection of the thickness of boundary is dependent to the PWM signal generation of the inverter. In order to remove the dependency on the current sensor, a particle swarm optimization(PSO) based modified observer is proposed to estimate the inductor current in which the observer gains are optimized using PSO by reducing the estimation errors cost function. The proposed dynamic smooth SMC algorithm has been simulated in MATLAB Simulink environment for 0.2-kVA DC-AC inverter and the results exhibit rapid dynamic response with a steady-state error of 0.4V peak-to-peak voltage under linear and nonlinear load perturbations. The total harmonic distortion (THD) is also reduced to 0.20% and 1.14% for linear and non-linear loads, respectively

A neural-network-based model predictive control of three-phase inverter with an output LC Filter

Model predictive control (MPC) has become one of the well-established modern control methods for three-phase inverters with an output $LC$ filter, where a high-quality voltage with low total harmonic distortion (THD) is needed. Although it is an intuitive controller, easy to understand and implement, it has the significant disadvantage of requiring a large number of online calculations for solving the optimization problem. On the other hand, the application of model-free approaches such as those based on artificial neural networks approaches is currently growing rapidly in the area of power electronics and drives. This paper presents a new control scheme for a two-level converter based on combining MPC and feed-forward ANN, with the aim of getting lower THD and improving the steady and dynamic performance of the system for different types of loads. First, MPC is used, as an expert, in the training phase to generate data required for training the proposed neural network. Then, once the neural network is fine-tuned, it can be successfully used online for voltage tracking purpose, without the need of using MPC. The proposed ANN-based control strategy is validated through simulation, using MATLAB/Simulink tools, taking into account different loads conditions. Moreover, the performance of the ANN-based controller is evaluated, on several samples of linear and non-linear loads under various operating conditions, and compared to that of MPC, demonstrating the excellent steady-state and dynamic performance of the proposed ANN-based control strategy

Improved finite control set model predictive control for distributed energy resource in islanded microgrid with fault-tolerance capability

In this paper, improved finite control set model predictive voltage control (FCS-MPVC) is proposed for the distributed energy resource (DER) in AC islanded microgrid (MG). Typically, AC MGs have two or more power electronic-based DERs, which have the ability to maintain a constant voltage at the point of common coupling (PCC) as well as perform power sharing among the DERs. Though linear controllers can achieve above-mentioned tasks, they have several restrictions such as slow transient response, poor disturbance rejection capability etc. The proposed control approach uses mathematical model of power converter to anticipate the voltage response for possible switching states in every sampling period. The proposed dual-objective cost function is designed to regulate the output voltage as well as load current under fault condition. Two-step horizon prediction technique reduces the switching frequency and computational burden of the designed algorithm. Performance of the proposed control technique is demonstrated through MATLAB/Simulink simulations for single distributed generator (DG) and AC MG under linear and non-linear loading conditions. The investigated work presents an excellent steady state performance, low computational overhead, better transient performance and robustness against parametric variations in contrast to classical controllers. Total harmonic distortion (THD) for linear and non-linear load is 0.89% and 1.4% respectively as illustrated in simulation results. Additionally, the three-phase symmetrical fault current has been successfully limited to the acceptable range.©2020 Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).fi=vertaisarvioitu|en=peerReviewed

Uncertainty and disturbance estimator design to shape and reduce the output impedance of inverter

Power inverters are becoming more and more common in the modern grid. Due to their switching nature, a passive filter is installed at the inverter output. This generates high output impedance which limits the inverter ability to maintain high power quality at the inverter output. This thesis deals with an impedance shaping approach to the design of power inverter control. The Uncertainty and Disturbance Estimator (UDE) is proposed as a candidate for direct formation of the inverter output impedance. The selection of UDE is motivated by the desire for the disturbance rejection control and the tracking controller to be decoupled. It is demonstrated in the thesis that due to this fact the UDE filter design directly influences the inverter output impedance and the reference model determines the inverter internal electromotive force. It was recently shown in the literature and further emphasized in this thesis that the classic low pass frequency design of the UDE cannot estimate periodical disturbances under the constraint of finite control bandwidth. Since for a power inverter both the reference signal and the disturbance signal are of periodical nature, the classic UDE lowpass filter design does not give optimal results. A new design approach is therefore needed. The thesis develops four novel designs of the UDE filter to significantly reduce the inverter output impedance and maintain low Total Harmonic Distortion (THD) of the inverter output voltage. The first design is the based on a frequency selective filter. This filter design shows superiority in both observing and rejecting periodical disturbances over the classic low pass filter design. The second design uses a multi-band stop design to reject periodical disturbances with some uncertainty in the frequency. The third solution uses a classic low pass filter design combined with a time delay to match zero phase estimation of the disturbance at the relevant spectrum. Furthermore, this solution is combined with a resonant tracking controller to reduce the tracking steady-state error in the output voltage. The fourth solution utilizes a low-pass filter combined with multiple delays to increase the frequency robustness. This method shows superior performance over the multi-band-stop and the time delayed filter in steady-state. All the proposed methods are validated through extensive simulation and experimental results

Multi phase system for metal disc induction heating: modelling and RMS current control

This paper presents a multi phase induction system modelling for a metal disc heating and further industrial applications such as hot strip mill. An original architecture, with three concentric inductors supplied by three resonant current inverters leads to a reduced element system, without any coupling transformers, phase loop, mobile screens or mobile magnetic cores as it could be found in classical solutions. A simulation model is built, based on simplified equivalent models of electric and thermal phenomena. It takes into account data extracted from Flux2D® finite element software, concerning the energy transfer between the inductor currents and the piece to be heated. It is implemented in a versatile software PSim, initially dedicated to power electronic. An optimization procedure calculates the optimal supply currents in the inverters in order to obtain a desired power density profile in the work piece. The paper deals with The simulated and experimental results are compared in open-loop and closed loop. The paper ends with a current control method which sets RMS inductor currents in continuous and digital conditions

Application of Optimal Switching Using Adaptive Dynamic Programming in Power Electronics

In this dissertation, optimal switching in switched systems using adaptive dynamic programming (ADP) is presented. Two applications in power electronics, namely single-phase inverter control and permanent magnet synchronous motor (PMSM) control are studied using ADP. In both applications, the objective of the control problem is to design an optimal switching controller, which is also relatively robust to parameter uncertainties and disturbances in the system. An inverter is used to convert the direct current (DC) voltage to an alternating current (AC) voltage. The control scheme of the single-phase inverter uses a single function approximator, called critic, to evaluate the optimal cost and determine the optimal switching. After offline training of the critic, which is a function of system states and elapsed time, the resulting optimal weights are used in online control, to get a smooth output AC voltage in a feedback form. Simulations show the desirable performance of this controller with linear and nonlinear load and its relative robustness to parameter uncertainty and disturbances. Furthermore, the proposed controller is upgraded so that the inverter is suitable for single-phase variable frequency drives. Finally, as one of the few studies in the field of adaptive dynamic programming (ADP), the proposed controllers are implemented on a physical prototype to show the performance in practice. The torque control of PMSMs has become an interesting topic recently. A new approach based on ADP is proposed to control the torque, and consequently the speed of a PMSM when an unknown load torque is applied on it. The proposed controller achieves a fast transient response, low ripples and small steady-state error. The control algorithm uses two neural networks, called critic and actor. The former is utilized to evaluate the cost and the latter is used to generate control signals. The training is done once offline and the calculated optimal weights of actor network are used in online control to achieve fast and accurate torque control of PMSMs. This algorithm is compared with field-oriented control (FOC) and direct torque control based on space vector modulation (DTC-SVM). Simulations and experimental results show that the proposed algorithm provides desirable results under both accurate and uncertain modeled dynamics

Effective synchronization of a class of Chua's chaotic systems using an exponential feedback coupling

In this work a robust exponential function based controller is designed to synchronize effectively a given class of Chua's chaotic systems. The stability of the drive-response systems framework is proved through the Lyapunov stability theory. Computer simulations are given to illustrate and verify the method.Comment: 12 pages, 18 figure