No description available.

No description available.

No description available.

No description available.

The speed control of a Brushless DC Motor Drive in the presence of load disturbance is investigated. Firstly some practical results are presented where a simple proportional-integral speed controller is used in the presence of a large step input speed demand as well as load disturbance. The wind-up problem caused by the saturation of the controller is discussed. In order to improve the performance of the proportional-integral speed controller in the presence of load variation, a load estimator is used with torque feedforward control. The results presented show the speed holding capability in the presence of load variation is significantly improved. A genetic algorithm is used on line to optimise the controller for different conditions such as large and small step input speed demand and load disturbance. The results presented show that a genetic algorithm is capable of finding the tuning of the controller for optimal performance. Single-input single-output and two-input two-output fuzzy speed controllers are also used and the results compared to a proportional-integral controller. Results are presented showing that a single-input single-output fuzzy controller works as a proportional controller with variable gain whereas the two-input two-output fuzzy controller is capable of driving the motor at variable speed and load torque with excellent performance. The robustness of the fuzzy controllers is compared to the proportional-integral controller and the results presented show that the fuzzy one is more robust then the proportional-integral. A genetic algorithm is also used on line for the optimisation of the two-input twooutput fuzzy speed controller and the results show that despite the large number of parameters to be optimised, the tuning for optimal performance is also possible.

No description available.

No description available.

Autorotation maneuvers are required to perform a safe landing of a helicopter in cases of engine loss in a single engine vehicle and transmission or tail rotor malfunction. The rise of autonomous helicopter technology, and the pilot skill required to manually perform an autorotation, motivate the need for new autonomous autorotation control laws. Previous approaches to automatic control for this maneuver have relied on control law optimization based on a high-fidelity model of the helicopter, or have attempted to match recorded trajectories flown by an expert human pilot. In this paper, a new expert control system is proposed. The term “expert control system” is used because the system is intended to mimic the actions that a human pilot might take, does not require any iterative learning, model prediction, or optimization at runtime, and is based on an inference system that involves fuzzy logic, PID, and other conventional control techniques. The multi-stage control law drives the helicopter to a near-optimal steady-state descent and uses an estimate of the time to impact to safely flare and land the helicopter in the vast majority of flight conditions. The control law is validated using a full 6-degree-of-freedom simulation of both a full-size attack helicopter and a small hobby-class helicopter. The pro- posed control design is highly flexible and may be used to perform fully autonomous autorotation or to provide guidance to pilots during manual autorotation maneuvers.

With the rapid growth of the digital imaging, image processing techniques are widely involved in many industrial and medical applications. Image thresholding plays an essential role in image processing and computer vision applications. It has a vast domain of usage. Areas such document image analysis, scene or map processing, satellite imaging and material inspection in quality control tasks are examples of applications that employ image thresholding or segmentation to extract useful information from images. Medical image processing is another area that has extensively used image thresholding to help the experts to better interpret digital images for a more accurate diagnosis or to plan treatment procedures. Opposition-based computing, on the other hand, is a recently introduced model that can be employed to improve the performance of existing techniques. In this thesis, the idea of oppositional thresholding is explored to introduce new and better thresholding techniques. A recent method, called Opposite Fuzzy Thresholding (OFT), has involved fuzzy sets with opposition idea, and based on some preliminary experiments seems to be reasonably successful in thresholding some medical images. In this thesis, a Weighted Opposite Fuzzy Thresholding method (WOFT) will be presented that produces more accurate and reliable results compared to the parent algorithm. This claim has been verified with some experimental trials using both synthetic and real world images. Experimental evaluations were conducted on two sets of synthetic and medical images to validate the robustness of the proposed method in improving the accuracy of the thresholding process when fuzzy and oppositional ideas are combined.

Fuzzy Cognitive Maps (FCMs) are a widely used, neuro-fuzzy based qualitative approach for the modeling of dynamic systems, which allow for both static and dynamic analyses. They are capable of modeling complex systems with nonlinearities and unknown physical behaviour. FCMs describe a given system by means of concepts connected by quantified cause-effect relationships. This dissertation contributes to the subject of computer-driven generation of FCMs that can be used to perform an accurate dynamic analysis of the modeled system. The dynamic analysis provides insights into the degree of presence, and dependencies between the concepts in successive iterations of the simulation of a given FCM model. Such simulation studies could be used to analyze what-if scenarios in the context of decision support and to perform time series predictions. Two research directions within the framework of FCM development, which concern the learning of FCMs from historical data and an aggregation of FCMs that were proposed by multiple experts, are investigated. Several new automated computational methods for data-driven learning and aggregation of FCMs are introduced and empirically evaluated. These methods utilize real-coded genetic algorithms (RCGA)-based optimization. This choice of the optimization vehicle was motivated by their well-documented efficiency in searching large and continuous search spaces, which are inherent to our problem. Experimental evaluation demonstrates that the proposed RCGA-based learning method outperforms modern existing approaches when the dynamic analysis is considered. A novel divide and conquer-based learning strategy to improve scalability of the RCGA approach, is also proposed. This strategy is shown to be competitive or even better than solutions based on the parallelization of the underlying genetic algorithm. The RCGA-based learning method is further extended to provide improved FCMs when the number of connections of the map is known a priori. Experimental evaluation shows that the density-based learning method outperforms the generic RCGA-based approach when using a relatively accurate density estimate, and that both methods are equivalent when the estimate is inaccurate. In addition, a novel method for the aggregation of multiple input FCMs, is proposed. When compared to existing aggregation approaches, this method provides solutions that are more accurate when dynamic analysis is the objective. / Software Engineering and Intelligent Systems