My main research interests and contributions lie within the areas of control systems, artificial intelligence, mechatronics, robotics, guidance, control and automation of unmanned ground and aerial vehicles. The main motivation of my research has always been to inject “learning” phenomena into various types of control algorithms as well as their real time implementations.
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Without loss of generality, there exist two basic methods for the realization of UAV automatic guidance: local positioning systems (vision or laser based sensors) and global positioning systems (GPSs). Today’s fast moving technology allows us the application of real-time kinematic (RTK)-global positioning systems (GPSs) which can provide an accurate positioning accuracy of a few centimetres. However, they may not be very accurate for a precise landing. As an alternative, vision-based systems are cheap to implement. However, they have some disadvantages in outdoor environments, e.g. to be very sensitive to light conditions. In this project, the advantages of local and global positioning systems will be combined to realize one specific goal: precise landing. The UAV will use the GPS until a certain border which will be an artificial sphere around the target point. After the artificial sphere, the navigation method will be stitched from GPS to a low cost camera.
The performance of a model-based controller is quite satisfactory in case of having a precise mathematical model of the system as well as when uncertainties and parameter changes are negligible. However, for the case of small size UAVs, it is a difficult, time consuming and expensive task to obtain the precise model of the system. Moreover, such small UAVs are more vulnerable to transient environmental conditions which will destabilize them.
In such cases, a model-based controller with a learning capability or a model-free controller is preferable; both methods will be implemented throughout this project. For the online learning purpose in the former method, the combination of artificial neural networks and fuzzy logic controllers will be implemented for the control of small size UAVs. In the latter method, artificial neural networks or fuzzy neural networks will be preferred. The most significant advantage of learning capability is that it is able to expand the operating envelope that cannot be modelled precisely in real-life. Thus, the system dynamics can be learned on-line.
New buildings in Singapore may soon have a high-tech building inspector rolling up to their door steps armed with laser scanners and high-tech cameras that can spot the tiniest cracks and defects.
This new building inspector is a robot invented by scientists from Nanyang Technological University, Singapore (NTU Singapore), co-developed with Singapore’s national industrial developer JTC and local start-up CtrlWorks.
Named QuicaBot – short for Quality Inspection and Assessment Robot – it can move autonomously to scan a room in minutes, using high-tech cameras and laser scanners to pick up building defects like cracks and uneven surfaces.
A few of these robots working together will make inspecting a building a breeze. The robots can upload 3D data of the scans to the cloud and inform the human operator, who can then inspect critical and complex defects.
“Visual inspection of a new building is an intensive effort that takes two inspectors, so we have designed a robot to assist a human inspector to do his job in about half the time, saving precious time and manpower, and with great accuracy and consistency,” explained Prof Kayacan.
The main goal of this project is to design model predictive control (MPC)-moving horizon estimation (MHE) framework for highly nonlinear unmanned aerial vehicles (UAVs). In order to reach the main goal, the following sub tasks will be realized in real time:
The hypotheses to be tested would include whether or not it is achievable:
The research questions to be addressed would include:
The performance of a model-based controller (e.g. a model predictive controller (MPC)) is quite satisfactory in case of having a precise mathematical model of the system as well as uncertainties and parameter changes are negligible. However, in practical applications, it is a challenging task to determine an accurate model of a mechatronic system. Moreover, the system to be controlled is always subjected to both internal and external noise, and it has to operate under uncertain working environments. In such cases, a model-based controller with a learning capability or a model-free controller is preferable.
The research interest in the control and navigation of unmanned aerial vehicles (UAVs) has significantly increased in recent years. This trend seems to continue growing in the near future since the application areas for UAV are wide spread from remote delivery of urgent materials to reconnaissance purposes including all military and civilian applications. Among all the required endeavors in software and hardware of a UAV, the most prominent goal is the completion of a mission completely autonomously. In order to be able to take off, do a specific pre-defined mission and land on successfully, the UAV must have sophisticated estimation and control algorithms. Since it is difficult to obtain precise models for UAVs and they have to operate under uncertain working conditions, the main goal of this project is to design several learning model-based and model-free control techniques for the control of UAVs. As a model-based approach, linear and nonlinear model predictive controllers will be elaborated with some estimation/learning techniques, such as extended Kalman filters (EKFs) and moving horizon estimators (MHEs). As a model-free method, the combination of neural networks and fuzzy logic controllers will be studied.
Carbon composite structures are the most preferred materials that are used to manufacture UAV structures. Their high strength to weight ratio is an advantage but at the same time, they are costly. Besides, the cost of manufacturing complex geometries would require moulds which would again add into the cost and time for the fabrication of these parts. Critical design details such as an aerodynamic twist might not be accurately realized in this method of fabrication. In addition, to produce robust carbon fibre parts, the mechanics is complex and failure can occur frequently. Furthermore, the repair or replacement of broken carbon fibre part is again another costly affair. In contrast, in order to manufacture complex UAV structures, 3D printing method would be an advantage. 3D printing does not require any moulds or fixture which would drastically reduce the cost and time. Adding details to the complex structures during the manufacturing process would be time consuming and labour demanding. However, modification of the 3D printed part would be easier since adjustments would be done directly on the CAD software.
Making use of the advantage of 3D printing, the design and manufacturing of the VTOL UAV is proposed in this project. Later on, the following phases of this project proposal explore the novelty of the VTOL transition flight, mission flight profile (hovering, transition, cruise and landing) modelling and flight controller design. Finally, flight trials of the 3D printed VTOL UAV will be conducted on the final phase of the project.