Techn. Hochsch., Diss., 2002--Aachen.

Zugl.: Hagen, Fernuniv., Diss., 2007

Universiẗat, Diss., 2001--Bonn.

This thesis concentrates on learning and identification of fuzzy systems, and this thesis is composed about learning fuzzy systems from data for regression and function approximation by constructing complete, compact, and consistent fuzzy systems. Fuzzy systems are prevalent to solve pattern recognition problems and function approximation problems as a result of the good knowledge representation. With the development of fuzzy systems, a lot of sophisticated methods based on them try to completely solve pattern recognition problems and function approximation problems by constructing a great diversity of mathematical models. However, there exists a conflict between the degree of the interpretability and the accuracy of the approximation in general fuzzy systems. Thus, how to properly make the best compromise between the accuracy of the approximation and the degree of the interpretability in the entire system is a significant study of the subject.The first work of this research is concerned with the clustering technique on constructing fuzzy models in fuzzy system identification, and this method is a part of clustering based learning of fuzzy systems. As the determination of the proper number of clusters and the appropriate location of clusters is one of primary considerations on constructing an effectively fuzzy model, the task of the clustering technique aims at recognizing the proper number of clusters and the appropriate location as far as possible, which gives a good preparation for the construction of fuzzy models. In order to acquire the mutually exclusive performance by constructing effectively fuzzy models, a modular method to fuzzy system identification based on a hybrid clustering-based technique has been considered. Due to the above reasons, a hybrid clustering algorithm concerning input, output, generalization and specialization has hence been introduced in this work. Thus, the primary advantage of this work is the proposed clustering technique integrates a variety of clustering properties to positively identify the proper number of clusters and the appropriate location of clusters by carrying out a good performance of recognizing the precise position of each dataset, and this advantage brings fuzzy systems more complete.The second work of this research is an extended work of the first work, and two ways to improve the original work have been considered in the extended work, including the pruning strategy for simplifying the structure of fuzzy systems and the optimization scheme for parameters optimization. So far as the pruning strategy is concerned, the purpose of which aims at refining rule base by the similarity analysis of fuzzy sets, fuzzy numbers, fuzzy membership functions or fuzzy rules. By other means, through the similarity analysis of which, the complete rules can be kept and the redundant rules can be reduced probably in the rule base of fuzzy systems. Also, the optimization scheme can be regarded as a two-layer parameters optimization in the extended work, because the parameters of the initial fuzzy model have been fine tuning by two phases gradation on layer. Hence, the extended work primarily puts focus on enhancing the performance of the initial fuzzy models toward the positive reliability of the final fuzzy models. Thus, the primary advantage of this work consists of the simplification of fuzzy rule base by the similarity-based pruning strategy, as well as more accuracy of the optimization by the two-layer optimization scheme, and these advantages bring fuzzy systems more compact and precise.So far as a perfect modular method for fuzzy system identification is concerned, in addition to positively solve pattern recognition problems and function approximation problems, it should primarily comprise the following features, including the well-understanding interpretability, low-degree dimensionality, highly reliability, stable robustness, highly accuracy of the approximation, less computational cost, and maximum performance. However, it is extremely difficult to meet all of these conditions above. Inasmuch as attaining the highly achievement from the features above as far as possible, the research works of this thesis try to present a modular method concerning a variety of requirements to fuzzy systems identification.

In this dissertation three robust control strategies are proposed for a class of multi-input multi-output dynamic systems with matched or mismatched perturbations. Firstly, an adaptive variable structure observer and controller are introduced for solving the regulation problems, where some state variables are not measurable. By utilizing adaptive mechanisms in the design of sliding mode controller, one can enable the controlled systems not only to generate a reaching mode in finite time, but also to suppress the mismatched perturbations during the sliding mode. Secondly, the design of adaptive sliding mode controllers with application to robot manipulators is presented to solve the tracking problems. The dynamic equations of the controlled systems contain a perturbed leading coefficient matrix and can be either positive definite or negative definite. The asymptotical stability of the controlled systems will be attained if the proposed control scheme is employed. Thirdly, a design methodology of adaptive sliding mode controller based on T-S fuzzy model is proposed to solve tracking problems. It is shown that the trajectories of the controlled systems can be driven into a designated sliding surface in finite time, and the property of asymptotical stability is also guaranteed. All these three control schemes are designed by means of Lyapunov stability theorem. Each control scheme contains three parts. The first part is designed for eliminating measurable feedback signals. The second part is used for adjusting the convergent rate of state variables (or tracking errors) of the controlled system. The third part is the adaptive control mechanism, which is used to adapt some unknown constants of the least upper bounds of perturbations, so that the knowledge of the least upper bounds of matched or mismatched perturbations are not required. Several numerical examples and an application of controlling robot manipulator are demonstrated for showing the feasibility of the proposed control methodologies.

Stuttgart, Univ., Studienarb., 2004.

No description available.

Metody plánování pohybu jsou významnou součástí robotiky, resp. mobilních robotických platforem. Technicky je realizace plánování pohybu z globální úrovně převedena do posloupnosti akcí na úrovni specifické robotické platformy a definovaného prostředí, včetně omezení. V rámci této práce byla provedena recenze mnoha metod určených pro plánování cest, přičemž hlavním těžištěm byly metody založené na tzv. rychle rostoucích stromech (RRT), prostorovém rozkladu (CD) a využití fuzzy expertních systémů (FES). Dosažené výsledky, resp. prezentované algoritmy, využívají dostupné informace z pracovního prostoru mobilního robotu a jsou aplikovatelné na řešení globální pohybové trajektorie mobilních robotů, resp. k řešení specifických problémů plánování cest s omezením typu úzké koridory či překážky s proměnnou polohou v čase. V práci jsou představeny nové plánovací postupy využívající výhod algoritmů RRT a CD. Navržené metody jsou navíc efektivně rozšířeny s využitím fuzzy expertního systému, který zlepšuje jejich chování. Práce rovněž prezentuje řešení pro plánovací problémy typu identifikace úzkých koridorů, či významných oblastí prostoru řešení s využitím přístupů na bázi dekompozice prostoru. V řešeních jsou částečně zahrnuty sub-optimalizace nalezených cest založené na zkracování nalezené cesty a vyhlazování cesty, resp. nahrazení trajektorie hladkou křivkou, respektující lépe předpokládanou dynamiku mobilního zařízení. Všechny prezentované metody byly implementovány v prostředí Matlab, které sloužilo k simulačnímu ověření efektivnosti vlastních i převzatých metod a k návrhu prostoru řešení včetně omezení (překážky). Získané výsledky byly vyhodnoceny s využitím statistických přístupů v prostředí Minitab a Matlab.

This research is focused on the integration problem of process planning and scheduling in steel heat treatment operations environment using artificial intelligent techniques that are capable of dealing with such problems. This work addresses the issues involved in developing a suitable methodology for scheduling heat treatment operations of steel. Several intelligent algorithms have been developed for these propose namely, Genetic Algorithm (GA), Sexual Genetic Algorithm (SGA), Genetic Algorithm with Chromosome differentiation (GACD), Age Genetic Algorithm (AGA), and Mimetic Genetic Algorithm (MGA). These algorithms have been employed to develop an efficient intelligent algorithm using Algorithm Portfolio methodology. After that all the algorithms have been tested on two types of scheduling benchmarks. To apply these algorithms on heat treatment scheduling, a furnace model is developed for optimisation proposes. Furthermore, a system that is capable of selecting the optimal heat treatment regime is developed so the required metal properties can be achieved with the least energy consumption and the shortest time using Neuro-Fuzzy (NF) and Particle Swarm Optimisation (PSO) methodologies. Based on this system, PSO is used to optimise the heat treatment process by selecting different heat treatment conditions. The selected conditions are evaluated so the best selection can be identified. This work addresses the issues involved in developing a suitable methodology for developing an NF system and PSO for mechanical properties of the steel. Using the optimisers, furnace model and heat treatment system model, the intelligent system model is developed and implemented successfully. The results of this system were exciting and the optimisers were working correctly.

Para acompanhar o atual crescimento de demanda energética mundial, novas topologias de geradores estão sendo pesquisadas, estando nesse nicho o Gerador a Relutância Chaveado. Para seu correto funcionamento é necessário que técnicas de controle sejam empregadas para garantir níveis estáveis de tensão gerada mediante variações de velocidade e/ou carga. Portanto, o objetivo deste trabalho é apresentar uma metodologia de um controlador fuzzy da tensão gerada para a máquina em questão. Uma simulação em Matlab Simulink é apresentada para um sistema de geração de energia utilizando um gerador a relutância chaveado integrada com a malha de controle fuzzy. Resultados da dinâmica do funcionamento do controlador fuzzy são apresentados. O Controlador fuzzy proposto apresentou bom desempenho ao manter a tensão gerada em níveis desejáveis frente a distúrbios de carga e de variação de velocidade no eixo do gerador. Trata-se de um controlador robusto e versátil que garante estabilidade de tensão gerada mesmo com a operação do sistema com velocidade variável e/ou variação de carga. / Due to the growing demand of electric power energy, the engineering has to evolve by producing new efficient techniques and low cost equipment. Therefore, new electric power generator topologies have been studied, mainly switched reluctance generators due to their simple structure, reliability and low cost of fabrication. In order for a good operation of a switched reluctance generator, control techniques have to be applied to guarantee stable voltage levels under variable speed and load conditions. Hence, the objective of this work is to present a methodology based on fuzzy voltage controller for switched reluctance machine. Simulations are achieved in Matlab/Simulink for a power energy generation system using a switched reluctance generator with a fuzzy control loop. Results of the dynamic response of such controller are presented. The fuzzy controller could obtain good performance maintaining voltage levels in desired range. Therefore, the proposed controller showed to be robust, versatile and guarantee the voltage stability under speed and load variations.