Fuzzy systems

This dissertation proposes a utility-centric peer-to-peer (P2P) energy trading framework as an alternative to traditional net metering, aiming to resolve conflicts between distributed energy resource owners and utilities. It advocates for practical software services and dynamic payment mechanisms tailored to prosumer needs, offering an alternative to reducing net metering incentives. Additionally, it explores game theory principles to ensure equitable compensation for prosumer cooperation, driving the adoption of P2P energy markets. It also builds on demand-side payment mechanisms like NRG-X-Change by adapting it to provide fair payment distribution to prosumer coalitions. The interoperable energy storage systems with P2P trading also presented battery chemistry detection using neural network models. A fuzzy inference system is also designed to facilitate prosumers' choice in participating in P2P markets, providing flexibility for energy trading preferences. The simulation results demonstrated the effectiveness of the proposed design schemes.

Microgrid is an essential part of the nation’s smart grid deployment plan, recognized especially for improving efficiency, reliability, flexibility, and resiliency of the electricity system. Since microgrid consists of different distributed generation units, microgrid scheduling and real-time dispatch play a crucial role in maintaining economic, reliable, and resilient operation. The control and optimization performances of the existing online approaches degrade significantly in microgrid applications with missing forecast information, large state space, and multiple probabilistic events. This dissertation focuses on these challenges and proposes efficient online learning and optimization-based approaches.
For addressing the missing forecast challenges on online microgrid operations, a new fitted rolling horizon control (fitted-RHC) approach is proposed in Chapter 2. The proposed fitted-RHC approach is designed with a regression algorithm that utilizes the empirical knowledge obtain from the day-ahead forecast to make microgrid real-time decisions whenever the intra-day forecast data is unavailable. Simulation results show that the proposed fitted-RHC approach can achieve the optimal policy for the deterministic case study and perform efficiently with the uncertain environment in the stochastic case study.

There have been much technological advances and research in Unmanned Surface
Vehicles (USV) as a support and delivery platform for Autonomous/Unmanned
Underwater Vehicles (AUV/UUV). Advantages include extending underwater search and
survey operations time and reach, improving underwater positioning and mission
awareness, in addition to minimizing the costs and risks associated with similar manned
vessel operations. The objective of this thesis is to present the design and development a
high-level fuzzy logic guidance controller for a WAM-V 14 USV in order to
autonomously launch and recover a REMUS 100 AUV. The approach to meeting this objective is to develop ability for the USV to intercept and rendezvous with an AUV that is in transit in order to maximize the probability of a final mobile docking maneuver. Specifically, a fuzzy logic Rendezvous Docking controller has been developed that generates Waypoint-Heading goals for the USV to minimize the cross-track errors between the USV and AUV. A subsequent fuzzy
logic Waypoint-Heading controller has been developed to provide the desired heading
and speed commands to the low-level controller given the Waypoint-Heading goals.
High-level mission control has been extensively simulated using Matlab and partially
characterized in real-time during testing. Detailed simulation, experimental results and
findings will be reported in this paper.

The focus of this research is concerned with performing forecasting in technoeconomic contexts using a set of certain novel artificial neural networks (ANNs). Relevant efforts in general, entail the task of quantitatively estimating the details about the likelihood of future events (or unknown outcomes/effects) based on past and current information on the observed events (or known causes). Commensurate with the scope and objectives of the research, the specific topics addressed are as follows:
A review on various methods adopted in technoeconomic forecasting and identified are econometric projections that can be used for forecasting via artificial neural network (ANN)-based simulations Developing and testing a compatible version of ANN designed to support a dynamic sigmoidal (squashing) function that morphs to the stochastical trends of the ANN input. As such, the network architecture gets pruned for reduced complexity across the span of iterative training schedule leading to the realization of a constructive artificial neural-network (CANN). Formulating a training schedule on an ANN with sparsely-sampled data via sparsity removal with cardinality enhancement procedure (through Nyquist sampling) and invoking statistical bootstrapping technique of resampling applied on the cardinality-improved subset so as to obtain an enhanced number of pseudoreplicates required as an adequate ensemble for robust training of the test ANN: The training and prediction exercises on the test ANN corresponds to optimally elucidating output predictions in the context of the technoeconomics framework of the power generation considered Prescribing a cone-of-error to alleviate over- or under-predictions toward prudently interpreting the results obtained; and, squeezing the cone-of-error to get a final cone-of-forecast rendering the forecast estimation/inference to be more precise Designing an ANN-based fuzzy inference engine (FIE) to ascertain the ex ante forecast details based on sparse sets of ex post data gathered in technoeconomic contexts - Involved thereof a novel method of .fusing fuzzy considerations and data sparsity.Lastly, summarizing the results with essential conclusions and identifying possible research items for future efforts identified as open-questions.

With the strikingly fast development of industrial applications and research projects, control systems have become more and more complex than ever. Intelligent control techniques, featuring their being more robust and their availability when system mathematical models are unknown, have proven to be one of the most attractive and highlighted areas in the automatic control arena. This thesis concentrates first on the design of a laser tracking system. A standard design procedure of Fuzzy Logic Controllers (FLCs) is followed, which is then realized in a PC-based environment in the design. An essential issue in this thesis study is the auto tuning of the Fuzzy Logic Controller. An efficient tuning method, mu-law functions, which can adjust both the shape and scaling gain of fuzzy controller's decision table is adopted. Also a search process called Downhill Simplex Search is chosen. Combining these two methods, a Simplex-mu-law auto-tuning algorithm that fits our application is applied to tune the FLC for the laser tracking system. Another issue covered in this research is to modify the Fuzzy Logic Controller structure by changing the distribution of the membership functions. Based on the analysis of the real time error histogram of the system, a novel method is proposed in the thesis for the modification of the membership functions To assess the effectiveness of the methods proposed in this thesis, a prototype laser tracking system is constructed at the FAU Robotics Center. The control strategy proposed in this thesis is tested extensively by simulations and experimentations on the prototype system.

We discuss a set of indirect combining techniques for addressing multi-category classification problems that have been used in many domains, but not for intrusion detection systems. In contrast to the indirect combining techniques, direct techniques generally extend associated binary classifiers to handle multi-category classification problems. An indirect combining technique decomposes the original multi-category problem into, based on some criteria, multiple binary-category problems. We investigated two different approaches for building the binary classifiers. The results of the binary classifiers are then merged using a combining technique---three different combining techniques were studied. We implement some of the indirect combining techniques proposed in recent literature, and apply them to a case study of the DARPA KDD-1999 network intrusion detection project. The results demonstrate the usefulness of using indirect combining techniques for the multi-category classification problem of intrusion detection systems.

An automatic mine detection method has been designed for the purpose of locating mine-like objects on the seabed in real time using a high frequency, high resolution side scan sonar. The processing flow includes a calculation of the average scattering function of the local environment, shadow detection, and a fuzzy logic clustering/fuzzy logic detection procedure for identifying mine-like shadows. An Autonomous Underwater Vehicle (AUV) equipped with a fuzzy detection system gives the Navy the capability of rapidly locating bottom mines in littoral underwater environments during over-the-horizon operations.

Recent advances in computer engineering make the computational approaches to controller design for high order systems practical. In this dissertation, a series of computational methods based on cell state space for the design and optimization of Takagi-Sugeno (TS) type Fuzzy Logic Controllers (FLCs) are presented. The approaches proposed in this research can be classified into two categories: feed forward design and feedback design. An Optimal Control Table (OCT) based on cell state space is used in all the feed forward design approaches. An FLC can be trained by Least Mean Square (LMS) algorithm with an OCT serving as the training set. For high order systems, due to physical memory limit, the cell resolution is generally low. A specially modified k-d tree representation of cell space is proposed to save the memory while keeping the cell resolution as high as possible. The control command for a point that is not a cell center is approximated by interpolating an OCT. All these commands can be used as training data to train an FLC. An iterative feedback design approach named Incremental Best Estimate Directed Search (IBEDS) is proposed to further optimize a training set. It is a kind of globally directed random search method. The general philosophy is that since the best possible performance of an FLC largely depends on the quality of the training set, if the training set is optimized, an FLC trained by the set would also be optimized. Based on IBEDS, two other feedback FLC design algorithms are also proposed. In one algorithm, subtractive clustering method is used to extract the structure of an FLC from an OCT. The coefficients of the FLC obtained are then optimized with IBEDS. The other algorithm applies IBEDS to three system models and finds the training set that has the worst performance for all the models. This training set is further optimized to improve robustness of a trained FLC.

Design and Tuning a fuzzy logic controller (FLCs) are usually done in two stages. In the first stage, the structure of a FLC is determined based on physical characteristics of the system. In the second stage, the parameters of the FLC are selected to optimize the performance of the system. The task of tuning FLCs can be performed by a number of methods such as adjusting control gains, changing membership functions, modifying control rules and varying control surfaces. A method for the design and tuning of FLCs through modifying their control surfaces is presented in this dissertation. The method can be summarized as follows. First, fuzzy control surfaces are modeled with Bezier functions. Shapes of the control surface are then adjusted through varying Bezier parameters. A Genetic Algorithm (GA) is used to search for the optimal set of parameters based on the control performance criteria. Then, tuned control surfaces are sampled to create rule-based FLCs. To further improve the system performance, continuity constraints of the curves are imposed. Under the continuity constraints with the same number of tunable parameters, one can obtain more flexible curves that have the potential to improve the overall system performance. An important issue is to develop a new method to self-tune a fuzzy PD controller. The method is based on two building blocks: (I) Bezier functions used to model the control surfaces of the fuzzy PD controller; and, shapes of control surfaces are then adjusted by varying Bezier parameters. (II) The next step involves using a gradient-based optimization algorithm with which the input scaling factors and Bezier parameters are on-line tuned until the controller drives the output of the process as close as possible to the reference position. To protect vendors and consumers from being victimized, various trust models have been used in e-commerce practices. However, a strict verification and authentication process may pose unnecessary heavy cost to the vendor. As an application of the control strategy proposed, this dissertation presents a solution to the reduction of costs of a vendor. With two fuzzy variables (price, credit-history), a trust-surface can be tuned to achieve an optimal solution in terms of profit margin of the vendor. With this new approach, more realistic trust decisions can be reached.

Modern people are becoming more and more dependent on computers in their daily lives. Most industries, from automobile, avionics, oil, and telecommunications to banking, stocks, and pharmaceuticals, require computers to function. As the tasks required become more complex, the complexity of computer software and hardware has increased dramatically. As a consequence, the possibility of failure increases. As the requirements for and dependence on computers increases, the possibility of crises caused by computer failures also increases. High reliability is an important attribute for almost any software system. Consequently, software developers are seeking ways to forecast and improve quality before release. Since many quality factors cannot be measured until after the software becomes operational, software quality models are developed to predict quality factors based on measurements collected earlier in the life cycle. Due to incomplete information in the early life cycle of software development, software quality models with fuzzy characteristics usually perform better because fuzzy concepts deal with phenomenon that is vague in nature. This study focuses on the usage of fuzzy logic in software reliability engineering. Discussing will include the fuzzy expert systems and the application of fuzzy expert systems in early risk assessment; introducing the interval prediction using fuzzy regression modeling; demonstrating fuzzy rule extraction for fuzzy classification and its usage in software quality models; demonstrating the fuzzy identification, including extraction of both rules and membership functions from fuzzy data and applying the technique to software project cost estimations. The following methodologies were considered: nonparametric discriminant analysis, Z-test and paired t-test, neural networks, fuzzy linear regression, fuzzy nonlinear regression, fuzzy classification with maximum matched method, fuzzy identification with fuzzy clustering, and fuzzy projection. Commercial software systems and the COCOMO database are used throughout this dissertation to demonstrate the usefulness of concepts and to validate new ideas.