This thesis investigates the design and evaluation of a control system, that is able to adapt quickly to changes in environment and steering characteristics. This type of controller is particularly suited for applications with wide-ranging working conditions such as those experienced by small motorised craft. A small motorised craft is assumed to be highly agile and prone to disturbances, being thrown off-course very easily when travelling at high speed 'but rather heavy and sluggish at low speeds. Unlike large vessels, the steering characteristics of the craft will change tremendously with a change in forward speed. Any new design of autopilot needs to be to compensate for these changes in dynamic characteristics to maintain near optimal levels of performance. This study identities the problems that need to be overcome and the variables involved. A self-organising fuzzy logic controller is developed and tested in simulation. This type of controller learns on-line but has certain performance limitations. The major original contribution of this research investigation is the development of an improved self-adaptive and predictive control concept, the Predictive Self-organising Fuzzy Logic Controller (PSoFLC). The novel feature of the control algorithm is that is uses a neural network as a predictive simulator of the boat's future response and this network is then incorporated into the control loop to improve the course changing, as well as course keeping capabilities of the autopilot investigated. The autopilot is tested in simulation to validate the working principle of the concept and to demonstrate the self-tuning of the control parameters. Further work is required to establish the suitability of the proposed novel concept to other control.

Real-time water quality monitoring is crucial due to land utilization increases which can negatively impact aquatic ecosystems from surface water runoff. Conventional monitoring methodologies are laborious, expensive, and spatio-temporally limited. Autonomous surface vehicles (ASVs), equipped with sensors/instrumentation, serve as mobile sampling stations that reduce labor and enhance data resolution. However, ASV autopilot navigational accuracy is affected by environmental forces (wind, current, and waves) that can alter trajectories of planned paths and negatively affect spatio-temporal resolution of water quality data. This study demonstrated a commercially available solar powered ASV equipped with a multi-sensor payload ability to operate autonomously to accurately and repeatedly maintain established A-B line transects under varying environmental conditions, where lateral deviation from a planned linear route was measured and expressed as cross-track error (XTE). This work provides a framework for development of spatial/temporal resolution limitations of ASVs for real-time monitoring campaigns and future development of in-situ sampling technologies.

A nonholonomic tracking controller is designed and adapted to work with both differential steering and Ackermann steering based platforms whose dynamics are represented using a unicycle model. The goal of this work is to find a relatively simple approach that offers a practical alternative to bulky and expensive algorithms, but still bolsters applicability where many other lightweight algorithms are too lax. The hope is that this alternative will offer a straightforward approach for groups interested in autonomous vehicle research but who do not have the resources or personnel to implement more complex solutions. In the first phase of this work, saturation constraints based on differential drive kinematics are added to ensure that the vehicle behaves intuitively and does not exceed user defined limitations. A new strategy for mapping commands back into a viable envelope is introduced, and the restrictions are accounted for using Lyapunov stability criteria. This stage of work is validated through simulation and experimentation. Following the development of differential drive methods, similar techniques are applied to Ackermann steering kinematic constraints. An additional saturation algorithm is presented, which likewise is accounted for using Lyapunov stability criteria. As with the differential case, the Ackermann design is validated through simulation and experimentation. Overall, the results presented in this work demonstrate that the developed algorithms show significant promise and offer a lightweight, practical solution to the problem of vehicle tracking control. / Master of Science / In this work, a position controller for ground vehicles is developed. The algorithm takes into account the constraints of both Ackermann and differential drive platforms. A simplistic model is used for the initial development of this control algorithm, and more rigid constraints are added based on the intended platform. The goal of this work is to find a relatively simple approach that offers a practical alternative to bulky and expensive algorithms, but still bolsters applicability where many other lightweight algorithms are too lax. The hope is that this alternative will offer a straightforward approach for groups interested in autonomous vehicle research, but who do not have the resources or personnel to implement more complex solutions. Throughout this work, we present the theoretical development as well as simulation and experiments to verify the efficacy of our approach. Overall, the results presented in this work demonstrate that the developed algorithms show significant promise and offer a lightweight, practical solution to the problem of vehicle tracking control.

Dans cette thèse, on s'est attaché à résoudre un certain nombre de problèmes qui apparaissent lorsqu'on traite des problèmes concrets de contrôle: phénomènes à plusieurs échelles de temps, discontinuités de la commande lors du basculement d'un correcteur à un autre, nécessité de concevoir un nombre limité de correcteurs différents malgré une gamme très importante des produits traités. Pour illustrer concrètement les résultats obtenus, nous nous sommes appuyés sur un exemple industriel concret, le contrôle de guidage de bande durant le processus de laminage dans un laminoir à chaud. D'abord, nous proposons une solution convexe au problème de commande optimale linéaire quadratique pour les systèmes linéaires à deux échelles de temps en temps discret. Ensuite, nous établissons des conditions suffisantes, formulées sous la forme d'inégalités matricielles linéaires, qui permettent de vérifier la stabilité d'un système à commutation à deux échelles de temps et de synthétiser des correcteurs stabilisants. Nous proposons aussi dans ce travail une méthode pour minimiser les discontinuités sur la commande dans le cadre des systèmes à commutation. Dans le contexte du contrôle de guidage de bande pour un laminoir à chaud, nous ne pouvons pas négliger l'influence des paramètres incertains, qui sont dus principalement au fait que ce genre de système traite une gamme de produits très large. Donc, dans la synthèse du correcteur, nous prenons en compte ces variations en divisant l'ensemble des produits en plusieurs familles et en synthétisant un correcteur différent pour chaque famille / This Ph.D. thesis deals with a certain number of problems arising in practical implementation of control systems: multi time scale phenomena, sudden modifications on the system dynamics, discontinuities on the control signal due to controller switchings, the need of design a limited number of controllers in spite of a wide variation on the physical parameters. In order to illustrate the validity of the obtained results, we resort to a real problem concerning the steel production framework, the robust steering control of a hot strip finishing mill. First, a convex solution of the linear quadratic control design for discrete two time scale systems is proposed. Hence, we address the stability problem of two time scale switched systems. We show that stability of the slow and fast switched subsystems under arbitrary switching rules does not imply the stability of the corresponding two time scale switched system in the singular perturbation form. An additional constraint, independent of the value of the singular parameter and of the switching rule, is provided in terms of linear matrix inequalities. We also introduce a bumpless transfer method for switched systems aiming at reducing the discontinuities on the control signal. Dwell time conditions assessing the asymptotic stability of the closed loop switched system are established. The practical contribution of this thesis, the robust steering control design, exploits most of previous results. The objective is to guarantee the stability of the hot strip mill system and improve the quality of the rolled products

For a wide range of people with limited upper- and lower-body mobility, interaction with robots remains a challenging problem. Due to various health conditions, they are often unable to use standard joystick interface, most of wheelchairs are equipped with. To accommodate this audience, a number of alternative human-machine interfaces have been designed, such as single switch, sip-and-puff, brain-computer interfaces. They are known as low throughput interfaces referring to the amount of information that an operator can pass into the machine. Using them to control a wheelchair poses a number of challenges. This thesis makes several contributions towards the design of robotic wheelchairs controlled via low throughput human-machine interfaces: (1) To improve wheelchair motion control, an adaptive controller with online parameter estimation is developed for a differentially driven wheelchair. (2) Steering control scheme is designed that provides a unified framework integrating different types of low throughput human-machine interfaces with an obstacle avoidance mechanism. (3) A novel approach to the design of control systems with low throughput human-machine interfaces has been proposed. Based on the approach, position control scheme for a holonomic robot that aims to probabilistically minimize time to destination is developed and tested in simulation. The scheme is adopted for a real differentially driven wheelchair. In contrast to other methods, the proposed scheme allows to use prior information about the user habits, but does not restrict navigation to a set of pre-defined points, and parallelizes the inference and motion reducing the navigation time. (4) To enable the real time operation of the position control, a high-performance algorithm for single-source any-angle path planning on a grid has been developed. By abandoning the graph model and introducing discrete geometric primitives to represent the propagating wave front, we were able to design a planning algorithm that uses only integer addition and bit shifting. Experiments revealed a significant performance advantage. Several modifications, including optimal and multithreaded implementations, are also presented.

The diploma thesis, which deals with the usage of tractors with tracked chassis in the agricultural primary production, presents in a literature overview the design of the chassis of wheeled tractors, which is supplemented with front axle suspension systems. Main theoretical part of the overview describes the construction of the tracked chassis. There are mentioned and described all units of significant producers of tracked tractors. Further, the work describes the design of suspension of tracked tractors and the possibility of control of the tracked chassis. The theoretical part ends with a chapter focusing on semi-tracked tractors. Practical part shows the methodology and results of the measuring by use of the tracked tractors in the agricultural company annual production, focused on the utilization of each Tractor-set. Thesis is completed by characteristic of the general agriculture company with the analysis of the investments and costs.

<p dir="ltr">Path-tracking control is an integral part of motion planning in autonomous vehicles, in which the vehicle's lateral and longitudinal positions are controlled by a control system that will provide acceleration and steering angle commands to ensure accurate tracking of longitudinal and lateral movements in reference to a pre-defined trajectory. Extensive research has been conducted to address the growing need for efficient algorithms in this area. In this dissertation, a scenario and machine learning-based data-driven control approach is proposed for a path-tracking controller. Firstly, a Deep Reinforcement Learning model is developed to facilitate the control of longitudinal speed. A Deep Deterministic Policy Gradient algorithm is employed as the primary algorithm in training the reinforcement learning model. The main objective of this model is to maintain a safe distance from a lead vehicle (if present) or track a velocity set by the driver. Secondly, a lateral steering controller is developed using Neural Networks to control the steering angle of the vehicle with the main goal of following a reference trajectory. Then, a path-planning algorithm is developed using a hybrid A* planner. Finally, the longitudinal and lateral control models are coupled together to obtain a complete path-tracking controller that follows a path generated by the hybrid A* algorithm at a wide range of vehicle speeds. The state-of-the-art path-tracking controller is also built using Model Predictive Control and Stanley control to evaluate the performance of the proposed model. The results showed the effectiveness of both proposed models in the same scenario, in terms of velocity error, lateral yaw angle error, and lateral distance error. The results from the simulation show that the developed hybrid A* algorithm has good performance in comparison to the state-of-the-art path planning algorithms.</p>

Cost efficient operation and maintenance strategies are crucial for reducing cost of wind energy. Since the regime change from feed-in tariffs to an auction-based bidding system for capacity in most European wind projects, levelized cost of energy is challenged constantly. Therefore, new technologies such as new controllers are developed to improve operation and to increase profit. Previous research studies demonstrated the advantage of increased power output of wake redirection control. However, understanding and quantifying the impact of wind farm control technologies on operation and maintenance strategies is inevitable to evaluate the economic feasibility of such new technologies. Thus, an event-based O&amp;M simulation tool has been developed. Besides general modules, such as the wind turbine model, the weather forecasting model and a model for simulating corrective and planned maintenance, the developed tool also takes wake effects into account. This allows considering different power productions for each individual turbine and a failure rate distribution within the wind farm which is based on altering loads on the different components. Both aspects are driven by changes in operation when applying a new controller technology. Exemplarily, the economic feasibility of a closed-loop active wake steering control has been analysed. Main achievements of this study are the possibility to quantify the impact of the active wake steering control on O&amp;M related KPIs. Results show that additional loads caused by applying yaw-misalignment and redirecting wake, lead to an increase in OPEX. However, the achieved energy production gain and thus related additional revenue exceeds additional cost in the case study. Nonetheless, the study reveals that the profitability of the controller is highly dependent on the electricity price which can be acquired during the wind farm’s lifetime.

Suspension systems and wheels play a critical role in vehicle dynamics performance of a car in areas such as ride comfort and handling. Lumped parameter models (LPMs) are commonly used for assessing the performance of vehicle suspension systems. However, there is a lack of clarity with regard to the relative capabilities of different LPM configurations. A comprehensive comparative study of three most commonly used LPMs of increasing complexity has been carried out in the current work. The study reported here has yielded insights into the capabilities of the considered LPMs in predicting response time histories which may be used for assessing ride comfort. A shortcoming of available suspension system models appears to be in representation of harsh situations such as jounce movement which cause full compression of springs leading to ‘jerks’ manifested as high values of rate of change of acceleration of sprung mass riding on a wheel. In the current research work, a modified nonlinear quarter-car model is proposed to account for the contact force that results in jerk-type response. The numerical solution algorithm is validated through the simulation of an impact test on a car McPherson strut in a Drop Weight Impact Testing Tower developed in CAR Laboratory, CPDM. This is followed by a detailed comparison of HCM and QCM to examine their suitability for such analysis. For decades, wheel bearings in vehicles have been designed using simplified analytical approaches based on Hertz contact theory and test data. In the present work, a hybrid approach has been developed for assessing the load bearing capacity of a wheel ball bearing set. According to this approach, the amplitude of dynamic wheel load can be obtained from a lumped parameter analysis of a suspension system, which can then be used for detailed static finite element analysis of a wheel bearing system. The finite element modelling approach has been validated by successfully predicting the load bearing capacity of an SKF ball bearing set for an acceptable fatigue life. For the first time, using a powerful commercial explicit finite element analysis tool, a detailed dynamic analysis has been carried of a deep groove ball bearing with a rotating inner race. The analysis has led to a consistent representation of complex motions consisting of rotations and revolutions of rolling elements, and generated insights into the stresses developed in the various components such as balls and races. In conclusion, a simple yet effective fuzzy logic-based yaw control algorithm has been presented in the current research. According to this algorithm, two inputs i.e. a yaw rate error and a driver steering angle are used for generating an output in the form of an additive steering angle which potentially can aid a driver in avoiding straying from an intended path.