A Robust Continuous-Time MPC of a DC–DC Boost Converter Interfaced With a Grid-Connected Photovoltaic System

The main function of the dc–dc converter in a grid-connected photovoltaic (PV) system is to regulate the terminal voltage of the PV arrays to ensure delivering the maximum power to the grid. The purpose of this paper is to design and practically implement a robust continuous-time model predictive control (CTMPC) for a dc–dc boost converter, feeding a three-phase inverter of a grid-connected PV system to regulate the PV output voltage. In CTMPC, the system behavior is predicted based on Taylor series expansion, raising concerns about the prediction accuracy in the presence of parametric uncertainty and unknown external disturbances. To overcome this drawback, a disturbance observer is designed and combined with CTMPC to enhance the steady-state performance in the presence of model uncertainty and unknown disturbance such as the PV current, which varies nonlinearly with the operating point. An interesting feature is that the composite controller reduces to a conventional PI controller plus a predictive term that allows further improvement of the dynamic performance over the whole operating range. The effectiveness of the proposed controller was tested numerically and validated experimentally with the consideration of the grid-connected PV inverter system and its controller

Composite Disturbance Filtering: A Novel State Estimation Scheme for Systems With Multi-Source, Heterogeneous, and Isomeric Disturbances

State estimation has long been a fundamental problem in signal processing and control areas. The main challenge is to design filters with ability to reject or attenuate various disturbances. With the arrival of big data era, the disturbances of complicated systems are physically multi-source, mathematically heterogenous, affecting the system dynamics via isomeric (additive, multiplicative and recessive) channels, and deeply coupled with each other. In traditional filtering schemes, the multi-source heterogenous disturbances are usually simplified as a lumped one so that the "single" disturbance can be either rejected or attenuated. Since the pioneering work in 2012, a novel state estimation methodology called {\it composite disturbance filtering} (CDF) has been proposed, which deals with the multi-source, heterogenous, and isomeric disturbances based on their specific characteristics. With the CDF, enhanced anti-disturbance capability can be achieved via refined quantification, effective separation, and simultaneous rejection and attenuation of the disturbances. In this paper, an overview of the CDF scheme is provided, which includes the basic principle, general design procedure, application scenarios (e.g. alignment, localization and navigation), and future research directions. In summary, it is expected that the CDF offers an effective tool for state estimation, especially in the presence of multi-source heterogeneous disturbances

Prädiktive Regelung und Finite-Set-Beobachter für Windgeneratoren mit variabler Drehgeschwindigkeit

This dissertation presents several model predictive control (MPC) techniques and finite-position-set observers (FPSOs) for permanent-magnet synchronous generators and doubly-fed induction generators in variable-speed wind turbines. The proposed FPSOs are novel ones and based on the concept of finite-control-set MPC. Then, the problems of the MPC techniques like sensitivity to variations of the model parameters and others are investigated and solved in this work.Die vorliegende Dissertation stellt mehrere unterschiedliche Verfahren der modellprädiktiven Regelung (MPC) und so genannte Finite-Position-Set-Beobachter (FPSO) sowohl für Synchrongeneratoren mit Permanentmagneterregung als auch für doppelt gespeiste Asynchrongeneratoren in Windkraftanlagen mit variabler Drehzahl vor und untersucht diese. Für die Beobachter (FPSO) wird ein neuartiger Ansatz vorgestellt, der auf dem Konzept der Finite-Control-Set-MPC basiert. Außerdem werden typische Eigenschaften der MPC wie beispielsweise die Anfälligkeit gegenüber Parameterschwankungen untersucht und kompensiert

Advances and Trends in Mathematical Modelling, Control and Identification of Vibrating Systems

This book introduces novel results on mathematical modelling, parameter identification, and automatic control for a wide range of applications of mechanical, electric, and mechatronic systems, where undesirable oscillations or vibrations are manifested. The six chapters of the book written by experts from international scientific community cover a wide range of interesting research topics related to: algebraic identification of rotordynamic parameters in rotor-bearing system using finite element models; model predictive control for active automotive suspension systems by means of hydraulic actuators; model-free data-driven-based control for a Voltage Source Converter-based Static Synchronous Compensator to improve the dynamic power grid performance under transient scenarios; an exact elasto-dynamics theory for bending vibrations for a class of flexible structures; motion profile tracking control and vibrating disturbance suppression for quadrotor aerial vehicles using artificial neural networks and particle swarm optimization; and multiple adaptive controllers based on B-Spline artificial neural networks for regulation and attenuation of low frequency oscillations for large-scale power systems. The book is addressed for both academic and industrial researchers and practitioners, as well as for postgraduate and undergraduate engineering students and other experts in a wide variety of disciplines seeking to know more about the advances and trends in mathematical modelling, control and identification of engineering systems in which undesirable oscillations or vibrations could be presented during their operation

Offset-free feedback linearisation control of a three-phase grid-connected photovoltaic system

In this study, a state feedback control law is combined with a disturbance observer to enhance disturbance rejection capability of a grid-connected photovoltaic (PV) inverter. The control law is based on input-output feedback linearisation technique, while the existing disturbance observer is simplified and adopted for the system under investigation. The resulting control law has a proportional-integral (PI)/almost PI-derivative-like structure, which is convenient for real-time implementation. The objective of the proposed approach is to improve the DC-bus voltage regulation, while at the same time control the power exchange between the PV system and the grid. The stability of the closed-loop system under the composite controller is guaranteed by simple design parameters. Both simulation and experimental results show that the proposed method has significant abilities to initiate fast current control and accurate adjustment of the DC-bus voltage under model uncertainty and external disturbance

Fault-tolerant load reduction control for large offshore wind turbines

Offshore wind turbines suffer from asymmetrical loading (blades, tower etc.), leading to enhanced structural fatigue. As well as asymmetrical loading different types of faults (pitch system faults etc.) can occur simultaneously, causing degradation of load mitigation performance and enhanced fatigue. Individual pitch control (IPC) provides an important method to achieve mitigation of rotor asymmetric loads, but this may be accompanied by a resulting enhancement of pitch movement leading to increased possibility of pitch system faults, which negative effects on IPC performance.This thesis focuses on combining the fault tolerant control (FTC) techniques with load reduction strategies by a more intelligent pitch control system (i.e. collective pitch control and IPC) for offshore wind turbines in a system level to reduce the operation & maintenance costs and improve the system reliability. The scenario of load mitigation is analogous to the FTC problem because the action of rotor/tower bending can be considered as a fault effect. The essential concept is to attempt to account for all the "fault effects" in the rotor and tower systems which can weaken the effect of bending moment reduction through the use of IPC.Motivated by the above, this thesis focuses on four aspects to fill the gap of the combination between FTC and IPC schemes. Firstly, a preview control system using model predictive control with future wind speed is proposed, which could be a possible alternative to using LiDAR technology when using preview control for load reduction. Secondly, a multivariable IPC controller for both blade and tower load mitigation considering the inherent couplings is investigated. Thirdly, appropriate control-based fault monitoring strategies including fault detection and fault estimation FE-based FTC scheme are proposed for several different pitch actuator/sensor faults. Furthermore, the combined analysis of an FE-based FTC strategy with the IPC system at a system level is provided and the robustness of the proposed strategy is verified

플라즈마 식각 장치를 위한 적응모델예측제어기의 설계

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 화학생물공학부(에너지환경 화학융합기술전공), 2019. 2. 이윤우.The semiconductor etching process, which is one of the most critical processes in the manufacturing of semiconductors and one that comprises numerous steps, requires higher sophistication as 10 nm semiconductors are mass produced. Currently, the semiconductor etching process is mostly done by physical and/or chemical etching with plasma. In addition, the plasma etching is getting increasingly popular with the miniaturization of the process to a scale of less than 10 nm. The result of a plasma etching process is represented in the form of an etch profile which is determined by the plasma variables. Therefore, the performance of the process depends on these variables, and it is essential to measure and control them in real time. Although research on the control of plasma etching processes has been actively carried out, the plasma etching process strongly relies on the experience and skill level of seasoned engineers at the industry level. This is because a plasma-based system is very complicated and sensitive, and has a time-varying characteristics. However, even though previous studies show excellent results, they employed invasive diagnostic tools, and have single variable control schemes where a counter change of another plasma variable during control actions for other variables might occur due to the highly interactive plasma characteristics. Moreover, they did not consider the time-varying characteristics of plasma-based systems. Therefore, this thesis proposes a multivariable control strategy which can cope with interaction effects and a design of an adaptive model predictive controller which maintains its performance wherein systems vary with time. At first, the plasma variables which are considered as controlled variables were selected as the electron density and the electron temperature. This is because one of the etch profile, especially etch rate, can be expressed as functions of those plasma variables and the variables can be measured by the optical emission spectroscopy which is a non-invasive diagnostic tool. The plasma variables were paired with instrumental variables through singular value decomposition and relative gain array for constructing the optimal multivariable system model. Two parallel proportional integral derivative (PID) controllers were designed based on the output errors then conducted plasma variable control simulations. Through the simulations, the exist of interaction between the variables was obviously verified. For resolving the interaction effectively, decoupler controllers were applied to the PID controllers. As it performed the control experiment of the Ar plasma electron density and electron temperature excellently, the possibility of multivariable control of plasma-based system is demonstrated. In spite of the meaningful control results using the PID controllers, there are obvious limitations in relation to the exquisiteness and to the characteristics of decoupler controllers as it highly dependent to the accuracy of the system model. In order to maintain performance even in the case of a system change, an advanced control strategy is required and model predictive control can be an alternative. Therefore, a model predictive controller was designed where the Bayesian optimization is used as tuning method for the maximization of the exquisiteness. The controller verified its capability as it conducted Ar plasma electron density control in a drift-free system. However, the performance of it deteriorated in control simulations of time-varying systems and in a control experiment performed in a system where O2 plasma was injected into an Ar plasma system inducing the high nonlinearity. Therefore, a more advanced control strategy which can overcome the difficulty was required. In an adaptive control method, once the information from the system is entered into the adjustment mechanism part, the part makes a decision to deliver it to the controller. Then the controller is modified in accordance with the decision and releases the optimal control action. The typical adaptive control algorithm, which is the recursive least squares algorithm, was used in this thesis. Using the algorithm with Kalman filter interpretation, the time-delay effect which comes from the plasma etching reactor can be considered. The recursive model parameter estimator utilizing this algorithm was tuned by Bayesian optimization. When the recursive model parameter estimator detects changes of the system model parameters in real time and transmits it to the model predictive controller, the controller calculates the optimal manipulated variable based on the modified model. The adaptive model predictive controller performed the same simulations and experiment as those performed by the model predictive controller. Unlike the model predictive controller, the proposed controller performed control excellently even when the system changes over time. Numerically, it showed the improved control ability by 24.7% and 30.4% in terms of the mean absolute percentage error and the number of deviated sample, respectively compared to the conventional model predictive controller. These results demonstrate that the adaptive model predictive controller designed in this theses is invaluable for plasma-based system control and is the effective controller for the plasma etching reactor. It is expected to make a significant contribution to plasma-based processes that require high sophistication and flexibility.수 많은 공정으로 이루어진 반도체 제조 공정 내에서 가장 큰 비중을 차지하고 있는 반도체 식각 공정은 최근 10 nm급 반도체의 양산이 이뤄짐에 따라 식각의 높은 정교성이 요구되고 있다. 반도체 식각 공정은 현재 산업계에선 플라즈마를 이용하여 물리적, 화학적 식각을 일으키는 방법을 통해 이루어지고 있으며, 공정이 10 nm 급 이하 스케일로 미세화된 후로 이 방법이 더욱 각광 받고 있다. 공정의 결과는 식각 프로필을 기준으로 결정되는 데 이 식각 프로필이 플라즈마 변수들에 크게 의존함이 입증됨에 따라 이 변수들을 실시간으로 측정하고 제어하는 것이 공정의 핵심이 되었다. 그동안 플라즈마 변수 제어에 관한 연구들이 활발히 진행되어 왔으나 아직까지 산업계에선 그 이론들을 바로 활용하지 못하고 경험 많은 엔지니어의 감에 의존하고 있다. 그 이유는 근본적으로 시스템이 매우 복잡하고 예민할 뿐 아니라 시간에 따라 변하는 특성을 갖고 있기 때문이다. 그러나 이전의 연구들은 훌륭한 결과를 보였음에도 불구하고, 플라즈마 시스템에 직접적으로 영향을 미치는 침투성 센서를 이용했거나, 플라즈마 변수들과 장치 변수들이 서로 복잡하게 얽혀 있어 야기되는 상호작용을 간과할 수밖에 없는 단변수 제어를 수행한 데에 그치고 있다. 게다가 외란 때문에 발생되는 시간에 따라 변하는 특성을 고려하지 못하고 있다. 따라서, 본 학위논문에서는 변수간 상호작용을 최소화하는 다변수 제어 전략과 시스템이 시간에 따라 변하는 상황에서도 성능이 악화되지 않는 적응모델예측제어기의 설계를 제안한다. 먼저, 전자 밀도와 전자 온도가 제어 대상이 되는 플라즈마 변수로 선정되었다. 이는 식각 프로필, 특히 식각률이 이 변수들에 대한 함수로 표현될 수 있으며, 이 변수들은 침투성 센서인 광학적 발광 분광법을 통해 측정될 수 있기 때문이다. 그 다음에, 최적의 다변수 시스템 정의를 위해 특이치 분석과 상대이득배열을 이용하여 가장 효과적으로 제어를 수행할 수 있는 장치 변수 선정이 이루어졌다. 이를 바탕으로 두 개의 병렬로 연결된 비례적분미분제어기를 설계, 아르곤 플라즈마 전자 밀도와 전자 온도의 제어 시뮬레이션을 수행하였다. 해당 시뮬레이션에서 변수들 간 상호 작용이 확연함을 입증하였으며 이를 효과적으로 해결하기 위해 디커플러 제어기가 비례적분미분제어기에 결합되었다. 이 제어기는 아르곤 플라즈마의 전자 밀도와 전자 온도 제어를 훌륭하게 수행함으로써 다변수 플라즈마 시스템의 제어 가능성을 분명하게 입증하였다. 다변수 플라즈마 시스템의 제어 가능성이 입증 됐음에도 불구하고, 이 제어 전략은 비례적분미분제어기의 정교성 측면에서의 한계와 디커플러 제어기의 시스템 모델에 대한 높은 의존도 특성으로 인한 한계가 존재한다. 시스템이 변하는 상황에서도 성능을 유지하기 위해선 더욱 수준 높은 제어 전략이 요구되며, 모델예측제어가 그 대안이 될 수 있다. 모델예측제어기의 설계는 제어의 정교성을 극대화 시키기 위해 베이시안 최적화 기법을 통해 이루어졌다. 이 모델예측제어기는 인위적인 외란이 적용되지 않은 순수 아르곤 플라즈마 시스템에서의 전자 밀도 제어를 훌륭하게 수행함으로써 그 성능을 입증하였다. 하지만, 시스템이 시간에 따라 변하는 상황을 모사한 시뮬레이션과 산소 플라즈마가 아르곤 플라즈마 시스템에 주입되어 시스템 변화를 야기시키는 상황에서 수행된 제어 실험에서 제어기의 성능이 확연히 악화됨을 확인할 수 있었다. 따라서 이를 극복할 수 있는 더욱 발전된 제어 전략이 요구되었다. 적응 제어 기법은 시스템에서 얻어진 정보를 조절 메커니즘 부분으로 보내 실시간으로 제어기의 수정 사항을 결정하여 이를 바탕으로 제어를 수행하는 기법이다. 본 학위논문에서는 대표적인 적응 제어 알고리즘인 순환형 최소자승법 알고리즘을 활용하였다. 이 알고리즘에 칼만 필터 해석을 접목시킴에 따라, 플라즈마 식각 장치로부터 비롯되는 시간 지연의 효과를 고려할 수 있게 하였다. 이 알고리즘이 탑재된 순환형 모델 파라미터 추정기는 베이시안 최적화 기법을 통해 튜닝되었다. 순환형 모델 파라미터 추정기가 실시간으로 감지하는 모델 파라미터의 변화를 모델예측제어기에 전달하면 수정된 모델을 기반으로 제어기는 최적의 조절 변수를 계산한다. 이렇게 설계된 적응모델예측제어기는 앞서 모델예측제어기가 수행한 것과 동일한 시뮬레이션과 실험을 수행하였다. 모델예측제어기와 달리 적응모델예측제어기는 시간에 따라 시스템이 변하는 상황에서도 훌륭한 제어를 수행하였으며, 평균절대오차율을 기준으로 했을 때 기존의 모델예측제어기보다 24.7%의 향상된 제어 성능을 보여주었다. 이 결과는 본 학위논문에서 제안하고 있는 적응모델예측제어기가 시스템의 변화가 빈번한 플라즈마 시스템에서의 제어에 매우 가치 있음과 더불어 플라즈마 식각 장치에 유효한 제어기라는 것을 반증한다. 이 결과가 플라즈마 기반 시스템을 대상으로 하는 모든 제어 공정의 발전에 크게 이바지할 것을 기대하는 바이다.Abstract i Contents v List of Figures viii List of Tables xii CHAPTER 1. Introduction 1 1.1. Research motivation 1 1.2. Research objectives 4 1.3. Description of the equipment used in this thesis 5 1.4. Outline of the thesis 9 CHAPTER 2. Design of Multi-Input Multi-Output Controller for Plasma-based System 10 2.1. Introduction 10 2.2. Theoretical backgrounds 13 2.2.1. Estimation of plasma variables from optical emission spectroscopy 13 2.2.2. The influence of oxygen in plasma etching reactor 16 2.2.3. Singular value decomposition and condition number 18 2.2.4. Relative gain array method 21 2.2.5. Multi-loop control system 23 2.3. MIMO control results in the Ar plasma system 31 2.3.1. Variable selection and pairing 31 2.3.2. Disturbance rejection control results for SISO systems 37 2.3.3. Simulation of multi-loop control scheme and decoupling control scheme 41 2.3.4. Set-point tracking control experiment of multi-loop controller with decoupler controllers 58 2.4. Concluding remarks 62 CHAPTER 3. Disturbance Rejection Control of Plasma Electron Density by Model Predictive Controller 64 3.1. Introduction 64 3.2. Model predictive control 68 3.2.1. Concept of model predictive control 68 3.2.2. Description of model predictive control algorithm 71 3.2.2.1. State estimation algorithm 71 3.2.2.2. Optimization algorithm 76 3.3. Design of model predictive controller for Ar plasma system 78 3.3.1. System identification of Ar plasma system 78 3.3.2. Optimal MPC weight parameters from integral squared error based Bayesian optimization 80 3.3.3. Experimental results of Ar plasma electron density control 84 3.4. Disturbance rejection control using model predictive controller 86 3.4.1. Development of time-varying system model for control simulation 86 3.4.2. Design of model predictive controller for disturbance rejection control 91 3.4.3. Experimental result of disturbance rejection control in Ar/O2 plasma system 101 3.5. Concluding remarks 104 CHAPTER 4. Design of Adaptive Model Predictive Controller for Plasma Etching Reactor 106 4.1. Introduction 106 4.2. Recursive model parameter estimation 112 4.2.1. Recursive least squares algorithm with forgetting factor 113 4.2.2. Recursive least squares algorithm with Kalman filter interpretation 116 4.3. Adaptive model predictive control algorithm 119 4.4. Time-varying system control using adaptive model predictive controller 123 4.4.1. Initial system identification (Scaling method) 123 4.4.2. Design of adaptive model predictive controller for time-varying system 125 4.4.3. Set-point tracking control results in drifted system 143 4.5. Concluding remarks 152 CHAPTER 5. Conclusion 154 5.1. Summary of contributions 154 5.2. Future work 157 Nomenclature 159 References 167 Abstract in Korean (국문초록) 174Docto