ABSTRACT In this dissertation, several adaptive control design schemes for a class of nonlinear systems are proposed. The first topic of the research is concerned with self-tuning PID controller design. The main problem of designing PID controller is how to determine the values of three control gains, i.e., proportional gain , integral gain , and derivative gain . We attempt to use the technique of adaptive control based on the Lyapunov approach to design the PID controller for some class of partially known nonlinear systems. Three PID control gains are adjusted on-line such that better output performance can be achieved. The stability of the closed-loop PID control systems is analyzed and guaranteed by introducing a supervisory control and a modified adaptation law with projection. Second, two kinds of adaptive neural control systems including the direct and indirect neural controls are considered by using simple single auto-tuning neuron. We will first propose a novel neuron called auto-tuning neuron and use it to take place of the roles of the traditional neural networks used in the direct and indirect adaptive neural control systems. This can greatly reduce the computational time and network complexities due to the simple configuration of the auto-tuning neuron. It is also easy for hardware implementation. Third, based on the idea borrowed from natural evolution, genetic algorithm can search for optimal or near-optimal solutions for an optimization problem over the search domain. An optimization technique of real-coded genetic algorithm is used to design the PID controller by minimizing the performance index of integrated absolute error. The improvements of our results over that using other methods are also illustrated. In the last part of each section, some computer simulation results will also be provided to illustrate our proposed methods.

A huge number of genomic information, including protein and DNA sequences, is generated by the human genome project. Deciphering these sequences and detecting local residue patterns of multiple sequences are very difficult. One of the ways to decipher these biological sequences is to detect local residue patterns from them. However, detecting unknown patterns from multiple sequences is still very difficult. In this thesis, we propose an algorithm, based on the Gibbs sampler method, for identifying local consensus patterns (motifs) in monomolecular sequences. We first designed an ACO (ant colony optimization) algorithm to find a good initial solution and a set of better candidate positions for revising the motif. Then the Gibbs sampler method is applied with these better candidate positions as the input. The required time for finding motifs using our algorithm is reduced drastically. It takes only 20 % of time of the Gibbs sampler method and it maintains the comparable quality.

This dissertation presents a methodology for the near optimal design of fixture layouts in multi-station assembly processes. An optimal fixture layout improves the robustness of a fixture system, reduces product variability and leads to manufacturing cost reduction. Three key aspects of the multi-station fixture layout design are addressed: a multi-station variation propagation model, a quantitative measure of fixture design, and an effective and efficient optimization algorithm. Multi-station design may have high dimensions of design space, which can contain a lot of local optima. In this dissertation, I investigated two algorithms for optimal fixture layout designs. The first algorithm is an exchange algorithm, which was originally developed in the research of optimal experimental designs. I revised the exchange routine so that it can remarkably reduce the computing time without sacrificing the optimal values. The second algorithm uses data-mining methods such as clustering and classification. It appears that the data-mining method can find valuable design selection rules that can in turn help to locate the optimal design efficiently. Compared with other non-linear optimization algorithms such as the simplex search method, simulated annealing, genetic algorithm, the data-mining method performs the best and the revised exchange algorithm performs comparably to simulated annealing, but better than the others. A four-station assembly process for a sport utility vehicle (SUV) side frame is used throughout the dissertation to illustrate the relevant concepts and the resulting methodology.

Photoacoustic computed tomography (PAT) has great potential for application in the biomedical field. It best combines the high contrast of electromagnetic absorption and the high resolution of ultrasonic waves in biological tissues. In Chapter II, we present time-domain reconstruction algorithms for PAT. First, a formal reconstruction formula for arbitrary measurement geometry is presented. Then, we derive a universal and exact back-projection formula for three commonly used measurement geometries, including spherical, planar and cylindrical surfaces. We also find this back-projection formula can be extended to arbitrary measurement surfaces under certain conditions. A method to implement the back-projection algorithm is also given. Finally, numerical simulations are performed to demonstrate the performance of the back-projection formula. In Chapter III, we present a theoretical analysis of the spatial resolution of PAT for the first time. The three common geometries as well as other general cases are investigated. The point-spread functions (PSF's) related to the bandwidth and the sensing aperture of the detector are derived. Both the full-width-at-half-maximum of the PSF and the Rayleigh criterion are used to define the spatial resolution. In Chapter IV, we first present a theoretical analysis of spatial sampling in the PA measurement for three common geometries. Then, based on the sampling theorem, we propose an optimal sampling strategy for the PA measurement. Optimal spatial sampling periods for different geometries are derived. The aliasing effects on the PAT images are also discussed. Finally, we conduct numerical simulations to test the proposed optimal sampling strategy and also to demonstrate how the aliasing related to spatially discrete sampling affects the PAT image. In Chapter V, we first describe a prototype of the RF-induced PAT imaging system that we have built. Then, we present experiments of phantom samples as well as a preliminary study of breast imaging for cancer detection.

In-process Optical Coordinate Measuring Machine (OCMM) offers the potential of diagnosing in a timely manner variation sources that are responsible for product quality defects. Such a sensor system can help manufacturers improve product quality and reduce process downtime. Effective use of sensory data in diagnosing variation sources depends on the optimal design of a sensor system, which is often known as the problem of sensor placements. This thesis addresses coordinate sensor placement in diagnosing dimensional variation sources in assembly processes. Sensitivity indices of detecting process mean and variance components are defined as the design criteria and are derived in terms of process layout and sensor deployment information. Exchange algorithms, originally developed in the research of optimal experiment deign, are employed and revised to maximize the detection sensitivity. A sort-and-cut procedure is used, which remarkably improve the algorithm efficiency of the current exchange routine. The resulting optimal sensor layouts and its implications are illustrated in the specific context of a panel assembly process.

The rapid emergence of distributed fuel cell powered generations (DFPGs) operating in parallel with utility has brought a number of technical concerns as more DFPGs are connected to utility grid. One of the most challenging problems is known as islanding phenomenon. This situation occurs when a network is disconnected from utility grid and is energized by local DFPGs. It can possibly result in injury to utility personnel arriving to service isolated feeders, equipment damage, and system malfunction. In response to the concern, this dissertation aims to develop a robust anti-islanding algorithm for utility interconnection of DFPGs. In the first part, digital signal processor (DSP) controlled power electronic converters for utility interconnection of DFPGs are developed. Current control in a direct-quadrature (dq) synchronous frame is proposed. The real and reactive power is controlled by regulating inverter currents. The proposed digital current control in a synchronous frame significantly enhances the performance of DFPGs. In the second part, the robust anti-islanding algorithm for utility interconnection of a DFPG is developed. The power control algorithm is proposed based on analysis of a real and reactive power mismatch. It continuously perturbs (±5%) the reactive power supplied by the DFPG while monitoring the voltage and frequency. If islanding were to occur, a measurable frequency deviation would take place, upon which the real power of the DFPG is further reduced to 80%; a drop in voltage positively confirms islanding. This method is shown to be robust and reliable. In the third part, an improved anti-islanding algorithm for utility interconnection of multiple DFPGs is presented. The cross correlation method is proposed and implemented in conjunction with the power control algorithm. It calculates the cross correlation index of a rate of change of the frequency deviation and (±5%) the reactive power. If this index increases above 50%, the chance of islanding is high. The algorithm initiates (±10%) the reactive power and continues to calculate the correlation index. If the index exceeds 80%, islanding is now confirmed. The proposed method is robust and capable of detecting islanding in the presence of several DFPGs independently operating. Analysis, simulation and experimental results are presented and discussed.

Synthetically generated terrain models are useful across a broad range of applications, including computer generated art & animation, virtual reality and gaming, and architecture. Existing algorithms for terrain generation suffer from a number of problems, especially that of being limited in the types of terrain that they can produce and of being difficult for the user to control. Typical applications of synthetic terrain have several factors in common: first, they require the generation of large regions of believable (though not necessarily physically correct) terrain features; and second, while real-time performance is often needed when visualizing the terrain, this is generally not the case when generating the terrain. In this thesis, I present a new, design-by-example method for synthesizing terrain height fields. In this approach, the user designs the layout of the terrain by sketching out simple regions using a CAD-style interface, and specifies the desired terrain characteristics of each region by providing example height fields displaying these characteristics (these height fields will typically come from real-world GIS data sources). A height field matching the user's design is generated at several levels of detail, using a genetic algorithm to blend together chunks of elevation data from the example height fields in a visually plausible manner. This method has the advantage of producing an unlimited diversity of reasonably realistic results, while requiring relatively little user effort and expertise. The guided randomization inherent in the genetic algorithm allows the algorithm to come up with novel arrangements of features, while still approximating user-specified constraints.

This thesis describes the use of an ultrasonic goal-seeking algorithm while using ionic polymer metal composite (IPMC), an electroactive polymer, as the actuator to drive a vessel towards a goal. The signal transmitting and receiving circuits as well as the goal seeking algorithm are described in detail. Two test vessels were created; one was a larger vessel that contained all necessary components for autonomy. The second was a smaller vessel that contained only the sensors and IPMC strips, and all power and signals were transmitted via an umbilical cord. To increase the propulsive efforts of the second, smaller vessel, fins were added to the IPMC strips, increasing the surface area over 700%, determined to yield a 22-fold force increase. After extensive testing, it was found that the three IPMC strips, used as oscillating fins, could not generate enough propulsion to move either vessel, with or without fins. With the addition of fins, the oscillating frequency was reduced from 0.86-Hz to 0.25-Hz. However, the goal-seeking algorithm was successful in guiding the vessel towards the target, an ultrasonic transmitter. When moved manually according to the instructions given by the algorithm, the vessel successfully reached the goal. Using assumptions based on prior experiments regarding the speed of an IPMC propelled vessel, the trial in which the goal was to the left of the axis required 18.2% more time to arrive at the goal than the trial in which the goal was to the right. This significant difference is due to the goal-seeking algorithm’s means to acquire the strongest signal. After the research had concluded and the propulsors failed to yield desired results, many factors were considered to rationalize the observations. The operating frequency was reduced, and it was found that, by the impulse-momentum theorem, that the propulsive force was reduced proportionally. The literature surveyed addressed undulatory motion, which produces constant propulsive force, not oscillatory, which yields intermittent propulsive force. These reasons among others were produced to rationalize the results and prove the cause of negative results was inherent to the actuators themselves. All rational options have been considered to yield positive results.

Much research has been done on wireless sensor networks. However, most protocols and algorithms for such networks are based on the ideal model Unit Disk Graph (UDG) model or do not assume any model. Furthermore, many results assume the knowledge of location information of the network. In practice, sensor networks often deviate from the UDG model significantly. It is not uncommon to observe stable long links that are more than five times longer than unstable short links in real wireless networks. A more general network model, the quasi unit-disk graph (quasi-UDG) model, captures much better the characteristics of wireless networks. However, the understanding of the properties of general quasi-UDGs has been very limited, which is impeding the design of key network protocols and algorithms. In this dissertation we study the properties for general wireless sensor networks and develop new topological/geometrical techniques for wireless sensor networking. We assume neither the ideal UDG model nor the location information of the nodes. Instead we work on the more general quasi-UDG model and focus on figuring out the relationship between the geometrical properties and the topological properties of wireless sensor networks. Based on such relationships we develop algorithms that can compute useful substructures (planar subnetworks, boundaries, etc.). We also present direct applications of the properties and substructures we constructed including routing, data storage, topology discovery, etc. We prove that wireless networks based on quasi-UDG model exhibit nice properties like separabilities, existences of constant stretch backbones, etc. We develop efficient algorithms that can obtain relatively dense planar subnetworks for wireless sensor networks. We also present efficient routing protocols and balanced data storage scheme that supports ranged queries. We present algorithmic results that can also be applied to other fields (e.g., information management). Based on divide and conquer and improved color coding technique, we develop algorithms for path, matching and packing problem that significantly improve previous best algorithms. We prove that it is unlikely for certain problems in operation science and information management to have any relatively effective algorithm or approximation algorithm for them.

Noise shaping is one of the distinguishing characteristics of the sigma-delta modulation technique that separates it from other types of pulse-width modulation schemes. In sigma-delta modulation, unwanted noise and harmonics caused by quantization are intentionally pushed toward the high-frequency band in order to achieve high resolution data conversion within the signal bandwidth. Due to its robust, high in-band linearity, the sigma-delta modulation technique finds numerous applications in industry. Generally speaking, as the order of a sigma-delta modulator increases, the modulator performance becomes better, which unfortunately comes along with a decrease in the modulator¡¦s stable input range. This thesis presents a novel adaptive law which is capable of on-line tuning the loop filter of a sigma-delta modulator and reducing the out of band gain of the resulting noise transfer function when the modulator input is large, thereby widening the stable input range of the modulator without significant sacrifice of the performance. The simulation and experimental results prove the effectiveness of the proposed adaptive sigma-delta modulator.