Haptic applications often employ devices with many degrees of freedom in order to allow the user to have natural movement during human-machine interaction. From the development point of view, the complexity in mechanical dynamics imposes a lot of challenges in modelling the behaviour of the device. Traditional system identification methods for nonlinear systems are often computationally expensive. Moreover, current research on using neural network approaches disconnect the physical device dynamics with the identification process. This thesis proposes a different approach to system identification of complex haptic devices when analytical models are formulated. It organizes the unknowns to be identified based on the governing dynamic equations of the device and reduces the cost of computation. All the experimental work is done with the Freedom 6S, a haptic device with input and feedback in positions and velocities for all 6 degrees of freedom . <br /><br /> Once a symbolic model is developed, a subset of the overall dynamic equations describing selected joint(s) of the haptic robot can be obtained. The advantage of being able to describe the selected joint(s) is that when other non-selected joints are physically fixed or locked up, it mathematically simplifies the subset dynamic equation. Hence, a reduced set of unknowns (e. g. mass, centroid location, inertia, friction, etc) resulting from the simplified subset equation describes the dynamic of the selected joint(s) at a given mechanical orientation of the robot. By studying the subset equations describing the joints, a locking sequence of joints can be determined to minimize the number of unknowns to be determined at a time. All the unknowns of the system can be systematically determined by locking selected joint(s) of the device following this locking sequence. Two system identification methods are proposed: Method of Isolated Joint and Method of Coupling Joints. Simulation results confirm that the latter approach is able to successfully identify the system unknowns of Freedom 6S. Both open-loop experimental tests and close-loop verification comparison between the measured and simulated results are presented. <br /><br /> Once the haptic device is modelled, fuzzy logic is used to address chattering phenomenon common to strong virtual effects. In this work, a virtual wall is used to demonstrate this approach. The fuzzy controller design is discussed and experimental comparison between the performance of using a proportional-derivative gain controller and the designed fuzzy controller is presented. The fuzzy controller is able to outperform the traditional controller, eliminating the need for hardware upgrades for improved haptic performance. Summary of results and conclusions are included along with suggested future work to be done.

Autonomous mobile sensor networks consist of a number of autonomous mobile robots equipped with various sensors and tasked with a common mission. This thesis considers the topology control of such an ad hoc mobile sensor network. In particular, I studied the problem of controlling the size, with respect to a distance metric, of the network for general interactive forcing among agents. Developed is a stability result, allowing one to design force laws to control the spread of the network. Many of the current results assume a known and/or fixed topology of the graph representing the communication between the nodes, i.e. the graph laplacian is assumed constant. They also assume fixed and known force-laws. Hence, the results are limited to time-invariant dynamics. The research considers stability analysis of sensor networks, unconstrained by specific forcing functions or algorithms, and communication topologies. Since the graph topologies are allowed to change as the agents move about, the system dynamics become discontinuous in nature. Filippov’s calculus of differential equations with discontinuous right hand sides is used to formally characterize the multi-agent system with the above attributes. Lyapunov’s Stability Theory, applied to discontinuous systems, is then used to derive bounds on the norm of the system states given bounds on its initial states and input. The above derived stability results lend themselves to the derivation of methods for the design of algorithms or force-laws for mobile sensor networks. The efficacy of the derived results is illustrated through several examples where it is shown how they may be used for synthesizing a topology managing strategy. Examples are given of designing force-laws that limit the network in a desired area.

In most range-based localization methods, inferring distance from radio signal strength using mathematical modeling becomes increasingly unreliable and complicated in indoor and extreme environments, due to effects such as multipath propagation and signal interference. We propose FuzLoc, a range-based, anchor-based, fuzzy logic enabled system system for localization. Quantities like RSS and distance are transformed into linguistic variables such as Low, Medium, High etc. by binning. The location of the node is then solved for using a nonlinear system in the fuzzy domain itself, which outputs the location of the node as a pair of fuzzy numbers. An included destination prediction system activates when only one anchor is heard; it localizes the node to an area. It accomplishes this using the theoretical construct of virtual anchors, which are calculated when a single anchor is in the node’s vicinity. The fuzzy logic system is trained during deployment itself so that it learns to associate an RSS with a distance, and a set of distances to a probability vector. We implement the method in a simulator and compare it against other methods like MCL, Centroid and Amorphous. Extensive evaluation is done based on a variety of metrics like anchor density, node density etc.

The tube hydroforming (THF) technology has been widely used in manufacturing the lightweight and high strength components. The success of THF is largely dependent on the selection of the loading paths: internal pressure vs. time and axial feeding vs. time. The Finite element method is used to simulate the forming result of different loading paths and reduce the cost of die-testing. T-shape tube hydroforming is investigated adaptive simulation by combining FEM code LS-DYNA with fuzzy logic controller subroutine is proposed. During the simulation process, subroutines can adjust the loading paths according to the values of the minimum tube thickness and its variance. Then, the purpose of better thickness distribution of the formed tube at the side branch is achieved. Comparing with other linear loading paths, this adaptive control method got better results. In experiments, the validity of LS-DYNA applied in THF process is verified and the experimental results by adaptive simulation are better than those by the linear loading paths.

This thesis is based on the DSP to develop a facial expression recognition system. Most facial expression recognition systems suppose that human faces have been found, or the background colors are simple, or the facial feature points are extracted manually. Only few recognition systems are automatic and complete. This thesis is a complete facial expression system. Images are captured by CCD camera. DSP locates the human face, extracts the facial feature points and recognizes the facial expression automatically. The recognition system is divided into four sub-system: Image capture system, Genetic Algorithm human face location system, Facial feature points extraction system, Fuzzy logic facial expression recognition system. Image capture system is using CCD camera to capture the facial expression image which will be recognized in any background, and transmitting the image data to SRAM on DSP through the PPI interface on DSP. Human face location system is using genetic algorithm to find the human face¡¦s position in image by facial skin color and ellipse information, no matter what the size of the human face or the background is simple. Feature points extraction system is finding 16 facial feature points in located human face by many image process skills. Facial expression recognition system is analyzing facial action units by 16 feature points and making them fuzzily. Judging the four facial expression: happiness, anger, surprise and neutral, by fuzzy rule bases.. According to the results of the experiment. The facial expression system has nice performance on recognition rate and recognition speed.

With scarce the energy source and the worsened environment pollution, how to create and use a clean and never exhausted energy is becoming very important day by day. This thesis we proposed the research and development of a solar energy storage system with fuzzy logic control. This solar energy storage system is composed of the solar cell, charger, batteries, buck converter and digital a signal processor. The solar energy storage charging system charger is based on buck circuit control with battery cycle pulse charging. with the fuzzy control theory combined in the tactics of charging , it¡¦s can improve the efficiency of charging, suppress the abnormally battery temperature rise, lengthen the battery¡¦s life, and reduce the waste used. In the experiment, four different charging methods, with the same starting voltage, are compared in terms of temperature control. Among the four methods, the fuzzy logic control proposed in this thesis is able to control the battery temperature at a good 30 Celsius Degree. Experimental and simulation results demonstrate the effectiveness and validity of the system.

The digital image processing has been developed for a long time. The image detection and tracking are involved to a variety of digital techniques. In this research we introduce the digital image processing techniques, base on a boosted cascade of simple features to develop a face detection and tracking system. Due to a large amount of computation in face detection under the complex environment will affect the detection rate and velocity efficiency. Therefore, we use the extended feature and set of 45ﾟ rotated feature with fast feature computation which called the integral image, combine with the deformable templates. We can compute a part of the image block to reduce the computation and improve the system. In the PAN-TILT unit, we use fuzzy logic. The results of experiment show that system is robust and fast.

Gas turbines are widely used as power sources in many industrial and transportation applications. This kind of engine is the most preferred prime movers in aircrafts, power plants and some marine vehicles. They have different configurations according to their mechanical constructions such as turbo-prop, turbo-shaft, turbojet, etc. These engines have different efficiencies and specifications and some advantages and disadvantages compared to Otto-Cycle engines. In this thesis, a small turbojet engine is investigated in order to find different control algorithms. AMT Olympus HP small turbojet engine has been used to determine the mathematical model of a gas turbine engine. Some important experimental data were taken from AMT Olympus engine by making many experiments. All components of the engine have been modeled by using laws of thermodynamics and some arithmetic calculations such as numerical solution of nonlinear differential equations, digitizing compressor and turbine map etc. This mathematical model is employed to create control algorithm of the engine. At first, standard control strategies had been considered such as P (proportional), PI (proportional integral), and PID (proportional-integral-differential) controllers. Because of the nonlinearities in gas turbines, standard control algorithms are not commonly used in literature. At the second stage fuzzy logic controllers have been designed to control the engine efficiently. This control algorithm was combined with mathematical of the engine in MATLAB environment and input-output relations were investigated. Finally, fuzzy logic control algorithm was embedded into an electronic controller.

In the polymer extrusion process the product quality like mechanical, optical, electrical properties and homogeneity etc. can be achieved by controlling the melt temperature, melt pressure or viscosity within a narrow fluctuation range. In the earlier studies there are many literatures in connection with the extrusion quality and related quality controls; i.e. temperature control, pressure control and viscosity control. In each of the control strategies, it is believed that the most effective to maintain product quality utilising viscosity control, because a polymer viscosity closely correlates with its composition and molecular distribution, and hence the characteristic of the material. In the viscosity control strategy, viscosity is an induced variable calculated from either the (1) flow rate and pressure drop with in-line rheometer or (2) melt temperature, screw speed (or pressure), geometrical dimensions of extruder, and extrusion material constants without in-line rheometer; the former method may interfere the output rate while the latter one does not. On the demand of using viscosity-measuring instruments as sensors to control the quality of the products, we developed an empirical off-line viscosity model, which is used to derive the extrusion viscosity models in the control process without in-line rheometer. The off-line viscosity model is proved more accuracy than other previous suggested models, such as WLF and Andrade¡¦s equations, to fit the experimental data. Polypropylene (PP) was used in this study to test the effectiveness of the extrusion viscosity models. Comparing the calculated results, it was found that the viscosity characteristics obtained by the extrusion viscosity models are in agreement with those obtained by using an in-line rheometer. Both methods can be used to obtain the viscosity in the polymer extrusion process. The objective of this study is to develop extrusion viscosity models together with collected data from several experimental tests and template rule-base to build a Multi-Input Multi-Output (MIMO) fuzzy logic closed-loop controller for the plastics extrusion control. The objective of this controller is to eliminate process variations and to produce the polymer of consistent quality. The fuzzy logic is provided for designing the MIMO closed-loop controller because it is suitable for applying to the polymer extrusion process control with such advantages as handling complex problems like non-linear, time varying behaviour and poor quality measurements happened in the extrusion process, etc. The experimental pre-tests include (1) investigation of the relationship between melt temperature and barrel setting temperatures (2) investigation of the relationship between melt pressure and screw speed and (3) building the relation equation between measured viscosity, melt temperature and speed for the in-line rheometer, etc. In order to test the effectiveness of the MIMO FLC, an off-line simulation program is developed, and the closed-loop tests are performed on the extruder. The test results prove that the designed MIMO FLC can effectively control the quality of products.

In this thesis, we develop a method using fuzzy logic to do cooperative localization. In a group of robots, at a given instant, each robot gives crisp pose estimates for all the other robots. These crisp pose values are converted to fuzzy membership functions based on various physical factors like acceleration of the robot and distance of separation of the two robots. For a given robot, all these fuzzy estimates are taken and fused together using fuzzy fusion techniques to calculate a possibility distribution function of the pose values. Finally, these possibility distributions are defuzzified using fuzzy techniques to find a crisp pose value for each robot. A MATLAB code is written to simulate this fuzzy logic algorithm. A Kalman filter approach is also implemented and then the results are compared qualitatively and quantitatively.