Control of the offshore wind turbine and its grid integration

This thesis investigates the way to reduce the maintenance cost and increase the life cycle of the offshore wind turbines, as in the offshore case maintenance is highly difficult and expensive. Firstly, we study the possibility to replace the vulnerable and expensive DC link capacitor in wind power integration system by the virtual infinite capacitor (VIC), which is a power electronic circuit functioning as a large filtering capacitor. We propose a control algorithm for the VIC. Before applying it to the wind power system, we firstly test it in a simple power factor compensator (PFC) as the output filter capacitor. The simulation results show the effective filtering performance of VIC in low-frequency range. Then, we validate it experimentally by directly injecting the DC voltage together with a 50 Hz ripples to the VIC. The VIC successfully eliminates the ripple and extracts the DC voltage at the output terminals. Besides, the experiment performances are highly consistent with the corresponding simulations, which demonstrates the possibility to use VIC to replace the DC-link capacitor in wind power integration system and use it in other industrial systems. Since the VIC mainly filters the ripple in low frequency range while the DC-link voltage usually includes ripples in two distinct frequency ranges, we further develop it into the parallel virtual infinite capacitor (PVIC), aiming to suppress the voltage ripple in a wider frequency range. The PVIC is applied to replace the DC-link capacitor in wind turbine grid integration system. The simulations are conducted under different grid conditions with turbulent wind input. The results show that the PVIC provides much better voltage suppression performance than the equivalent DC-link capacitor, which facilitates the power generation control under normal operations and reduces the risks of converter failure under grid faults. In this way, the PVIC proves to be a great solution to substitute the vulnerable DC-link voltage in the offshore wind turbine power integration system. The wind power conversion system from the generator to the grid is composed of a DC-link capacitor and two back-to-back power converters. Though the application of PVIC removes the fragile DC-link capacitor in the power conversion system, the power converters are also fragile and expensive. In addition, the existence of power converters decouples the generator with the grid, which hinders the direct inertia support and frequency regulations from wind turbines. It would be desirable to completely remove the whole power conversion system. Hydrostatic wind turbine (HWT) may provide a suitable solution. The HWT is a wind turbine using hydrostatic transmission (HST) to replace the original heavy and fragile gearbox. The HST can provide the ‘continuously variable gearbox ratio’ , which allows HWT to be connected to a synchronous generator (SG) and then directly to the grid. We propose a coordinated control scheme for the HWT. The simulations are conducted with turbulent wind under variable system loads. The results indicate that with the proposed coordinated control system, the HWT (without power converters) provides efficient frequency support to the grid, which shows it is a promising solution for the future offshore wind power system. Finally, we consider to further reduce the maintenance cost and improve the performance of the HWT by using a new and novel control algorithm called model-free adaptive control (MFAC). It is applied to both torque control and pitch control of the HWT. Their control performances are compared to some of the existing algorithms. The simulation results demonstrate that the MFAC controller has much better tracking and disturbance rejection performances than the existing algorithms which can increase the fatigue life of the wind turbine and reduce the maintenance cost

Power generation control of a hydrostatic wind turbine implemented by model-free adaptive control scheme

The hydrostatic wind turbine (HWT) is a type of wind turbine that uses hydrostatic transmission (HST) drivetrain to replace the traditional gearbox drivetrain. Without the fragile and expensive gearbox and power converters, HWT can potentially reduce the maintenance costs owing to the gearbox and power converter failures in wind power system, especially in offshore cases. We design an MFAC torque controller to regulate the pump torque of the HWT and compared to an H_inf torque controller. Then we design an MFAC pitch controller to stabilise the rotor speed of HWT and compared to a gain-scheduling proportional-integral (PI) controller and a gain-scheduling PI controller with anti-windup (PIAW). The results indicate that MFAC torque controller provides more effective tracking performance than the H_inf controller, and that MFAC pitch controller shows better rotor speed stabilisation performance in comparison with the gain-scheduling PI controller and PIAW

A LOW-COST APPROACH TO DATA-DRIVEN FUZZY CONTROL OF SERVO SYSTEMS

Servo systems become more and more important in control systems applications in various fields as both separate control systems and actuators. Ensuring very good control system performance using few information on the servo system model (viewed as a controlled process) is a challenging task. Starting with authors’ results on data-driven model-free control, fuzzy control and the indirect model-free tuning of fuzzy controllers, this paper suggests a low-cost approach to the data-driven fuzzy control of servo systems. The data-driven fuzzy control approach consists of six steps: (i) open-loop data-driven system identification to produce the process model from input-output data expressed as the system step response, (ii) Proportional-Integral (PI) controller tuning using the Extended Symmetrical Optimum (ESO) method, (iii) PI controller parameters mapping onto parameters of Takagi-Sugeno PI-fuzzy controller in terms of the modal equivalence principle, (iv) closed-loop data-driven system identification, (v) PI controller tuning using the ESO method, (vi) PI controller parameters mapping onto parameters of Takagi-Sugeno PI-fuzzy controller. The steps (iv), (v) and (vi) are optional. The approach is applied to the position control of a nonlinear servo system. The experimental results obtained on laboratory equipment validate the approach

Flight control of very flexible unmanned aerial vehicles

This thesis aims to investigate the flight control of a very flexible ying wing model already developed in the literature. The model was derived from geometrically nonlinear beam theory using intrinsic degrees of freedom and linear unsteady aerodynamics, which resulted in a coupled structural dynamics, aerodynamics, and flight dynamics description. The scenarios of trajectory tracking and autonomous landing in the presence of wind disturbance are considered in control designs. Firstly, the aeroelastic and trajectory control of this very flexible ying wing model is studied. The control design employs a two-loop PI/LADRC (proportional integral/linear active disturbance rejection control) and H1 control scheme, based on a reduced-order linear model. The outer loop employs the PI/LADRC technique to track the desired flight paths and generate attitude commands to the inner loop, while the inner loop uses H1 control to track the attitude command and computes the corresponding control inputs. The particle swarm optimization algorithm is employed for parameter optimization in the H1 control design to enhance the control effectiveness and robustness. Simulation tests conducted on the full-order nonlinear model show that the designed aeroelastic and trajectory control system achieves good performance in aspects of tracking effectiveness and robustness against disturbance rejection. Secondly, the preview-based autonomous landing control of the very flexible ying wing model using light detection and ranging (Lidar) wind measurements is studied. The preview control system follows the above two-loop control structure and is also designed based on the reduced-order linear model. The outer loop emxv ploys the same LADRC and PI algorithms to track the reference landing trajectory and vertical speed, respectively. But the inner loop is extended to introduce Lidar wind measurements at a distance in front of the aircraft, employing H1 preview control to improve disturbance rejection performance during landing. Simulation results based on the full-order nonlinear model show that the preview-based landing control system is able to land the aircraft safely and effectively, which also achieves better control performance than a baseline landing control system (without preview) with respect to landing effectiveness and disturbance rejection. Finally, the data-driven flight control of the very flexible ying wing model using Model-Free Adaptive Control (MFAC) scheme to reduce the dependence of control design on system modeling is studied. A cascaded proportional-derivative MFAC (PD-MFAC) approach is proposed to accommodate the MFAC scheme in a flight control problem, which shows better control performance over the original MFAC algorithm. Based on the PD-MFAC approach, the data-driven flight control system is developed to achieve gust load alleviation and trajectory tracking. Simulation results based on the full-order nonlinear model show that the proposed data-driven flight control system is able to properly regulate all the rigid-body and flexible modes with better effectiveness and robustness (against disturbance rejection and modeling uncertainties), compared to a baseline H1 flight control system

Automatic Control and Routing of Marine Vessels

Due to the intensive development of the global economy, many problems are constantly emerging connected to the safety of ships’ motion in the context of increasing marine traffic. These problems seem to be especially significant for the further development of marine transportation services, with the need to considerably increase their efficiency and reliability. One of the most commonly used approaches to ensuring safety and efficiency is the wide implementation of various automated systems for guidance and control, including such popular systems as marine autopilots, dynamic positioning systems, speed control systems, automatic routing installations, etc. This Special Issue focuses on various problems related to the analysis, design, modelling, and operation of the aforementioned systems. It covers such actual problems as tracking control, path following control, ship weather routing, course keeping control, control of autonomous underwater vehicles, ship collision avoidance. These problems are investigated using methods such as neural networks, sliding mode control, genetic algorithms, L2-gain approach, optimal damping concept, fuzzy logic and others. This Special Issue is intended to present and discuss significant contemporary problems in the areas of automatic control and the routing of marine vessels

Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors

Deep Learning-Based, Passive Fault Tolerant Control Facilitated by a Taxonomy of Cyber-Attack Effects

In the interest of improving the resilience of cyber-physical control systems to better operate in the presence of various cyber-attacks and/or faults, this dissertation presents a novel controller design based on deep-learning networks. This research lays out a controller design that does not rely on fault or cyber-attack detection. Being passive, the controller’s routine operating process is to take in data from the various components of the physical system, holistically assess the state of the physical system using deep-learning networks and decide the subsequent round of commands from the controller. This use of deep-learning methods in passive fault tolerant control (FTC) is unique in the research literature. The proposed controller is applied to both linear and nonlinear systems. Additionally, the application and testing are accomplished with both actuators and sensors being affected by attacks and /or faults

Data-driven Adaptive Stabilizer for Unknown Nonlinear Dynamic MIMO Systems Using a Cognition-based Framework

This thesis focuses on a cognitive stabilizer concept which is an adaptive discrete control method based on a cognition-based framework. The aim of the cognitive stabilizer is to autonomously stabilize a specific class of unknown nonlinear multi-input-multi-output (MIMO) systems. The cognitive stabilizer is able to gain useful local knowledge of the unknown system and can autonomously define suitable control inputs to stabilize the system. The development of different kinds of adaptive, data-driven, and model-free controllers shows a clear tendency towards research on control methods with high autonomy. Here the term autonomy is used to describe the fact that the control approach/the related programming is organized such that the algorithm is able to handle the feedback design autonomously without instructions from outside the algorithm. Typical methods affected by this definition are adaptive control method, data-driven control method, and model-free control method. In this thesis, the state-of-the-art of them is reviewed. The main focus is given to the autonomy of the realized approaches. It can be concluded that the existing methods still show some open points achieving highly autonomous control. In order to address these open points, a framework similar to modeling approaches concerning the human cognition processes [Cac98] can be introduced in the engineering context, which is denoted as cognition-based framework. As stabilization control task is the most basic control task, the cognition-based framework for stabilization is established in this thesis. It is assumed, that the mathematical model of the system to be controlled is unknown and fully controllable, as well as the state vector can be measured. The cognitive stabilizer is realized based on the cognitive framework by its four main modules: (1) “perception and interpretation” using system identifier for the system local dynamic online identification and multi-step-ahead prediction; (2) “expert knowledge” relating to the stability criterion to guarantee the stability of the considered motion of the controlled system; (3) “planning” to generate a suitable control input sequence according to certain cost functions; (4) “execution” to generate the optimal control input in a corresponding feedback form. Each module can be realized using different methods. In this thesis, “perception and interpretation” is realized using neural networks, Gaussian process regression, or combined identifier. “Expert knowledge” consists of the data-driven quadratic stability criterion, the quadratic Lyapunov stability criterion with a certain Lyapunov function, and the uniform stability criterion. The modules “planning” and “execution” are realized together with exhaustive grid search method or direct input optimization using inverse model. The whole cognitive stabilizer is realized using the autonomous communication among each module. The cognitive stabilizer are tested using numerical examples or experimental results in this thesis. Pendulum system and Lorenz-system are considered as simulation examples. Both are benchmark examples for the nonlinear dynamic control design. The cognitive stabilizer is experimentally implemented and evaluated to a threetank-system. All the numerical examples and experimental results demonstrate the successful application of the proposed methods.Das Thema dieser Arbeit ist ein kognitives Stabilisierungsverfahren, das basierend auf einem kognitionsbasierten Framework ein adaptives diskretes Regelungsverfahren darstellt. Ziel des kognitiven Stabilisierungsverfahrens ist es, eine spezifische Klasse von unbekannten, nichtlinearen, Mehrgrößensystemen autonom zu stabilisieren. Das kognitive Stabilisierungsverfahren ist in der Lage, relevante lokale Informationen über das unbekannte System zu erlangen. Es kann autonom geeignete Steuergrößen definieren, um das System zu stabilisieren. Die Entwicklung von verschiedenen adaptiven, datenbasierten und modellfreien Reglern zeigte bereits die Tendenz der Erforschung von Regelungsmethoden mit hoher Autonomie. Der Begriff Autonomie wird hier verwendet, um die Tatsache zu beschreiben, dass das Regelungsverfahren bzw. die dazugehörige Programmierung so durchgeführt wird, dass der zugehörige Algorithmus den Rückführungsentwurf autonom ohne Einwirkungen von außerhalb des Algorithmus festlegen kann. Typische Methoden, die von dieser Definition beeinflusst werden sind die adaptive Regelungsmethode, die datenbasierte Regelungsmethode oder die modellfreie Regelungsmethode, deren Stand der Forschung in dieser Arbeit zusammengefasst wird. Der Hauptfokus liegt dabei auf der Autonomie der realisierten Verfahren. Es kann gezeigt werden, dass die existierenden Methoden immer noch einige offene Probleme aufweisen, um eine hohe autonome Regelung zu erreichen. Um diese offenen Probleme weiterzuentwickeln, kann ein Framework in den Ingenieurskontext eingeführt werden, das den Modellierungsverfahren bezüglich der menschlichen Kognitionsprozesse [Cac98] ähnelt und als kognitives Framework bezeichnet werden kann. Da Stabilisierungsaufgaben die elementarsten Regelungsaufgaben sind, wird in dieser Arbeit ein kognitionsbasiertes Framework zur Stabilisierung entwickelt. Zunächst wird angenommen, dass das mathematische Modell des zu regelnden Systems unbekannt, vollständig steuerbar und der Zustandsvektor messbar ist. Der kognitive Stabilisierungsregler wird basierend auf dem kognitiven System durch seine vier Hauptmodule realisiert: (1) ”Wahrnehmung und Interpretation“ durch einen Systemidentifikator zur Echtzeit-Identifikation der lokalen Systemdynamik und Mehr-Schritt-Vorhersage; (2) ”Expertenwissen“ bezogen auf das Stabilitätskriterium um die Stabilität der betrachteten Bewegung des geregelten Systems zu garantieren; (3) ”Planung“ um eine geeignete Eingangsgrößensequenz nach bestimmten Gütefunktionen zu erzeugen; (4) ”Ausführung“ um die optimalen Steuergrößen in eine entsprechende Rückführungsform zu generieren. Jedes Modul kann durch verschiedene Methoden realisiert werden. In dieser Arbeit wird das Modul ”Wahrnehmung und Interpretation“ durch neuronale Netzwerke, Gauß-Prozess-Regression oder einen kombinierten Identifikator umgesetzt. Das Modul ”Expertenwissen“ besteht aus dem datenbasierten quadratischen Stabilitätskriterium, dem quadratischen Lyapunov Stabilitätskriterium mit einer bestimmten Lyapunov-Funktion und dem gleichmäßigen Stabilitätskriterium. Die Module ”Planung“ und ”Ausführung“ werden zusammen durch das inverse Modell mit dem vollständigen ”Grid-Search“-Verfahren oder direkter Steuergrößenoptimierung realisiert. Die gesamte kognitive Stabilisierungsmethode wird durch die autonome Kommunikation zwischen jedem Modul realisiert. Die kognitive Stabilisierungsmethode wird in dieser Arbeit durch numerische Beispiele oder experimentelle Ergebnisse getestet. Zwei Simulationsbeispiele (Pendel-System sowie Lorenz-System) werden betrachtet. Beide sind Benchmarkbeispiele für den nichtlinearen dynamischen Regelungsentwurf. Die kognitive Stabilisierungsmethode wird experimentell auf das Drei-Tank-System angewendet und die entsprechenden Ergebnisse werden bewertet. Die numerischen Beispiele sowie die experimentelle Umsetzung zeigen die erfolgreiche Anwendung des dargestellten Verfahrens