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This thesis introduces a novel online set-point modulation (SPM) technique that employs fuzzy logic to adjust the timing and magnitude of control system set-points. After first applying fuzzy logic-enabled SPM to underdamped second order systems for testing purposes, the new scheme is then applied to an electromagnetic transient simulation model of Manitoba Hydro’s existing Ponton SVC system, and the results are then compared to the original SVC system that uses traditional control approaches. Conclusions show that fuzzy logic-based SPM improves the controlled voltage’s transient overshoot response to changes in the voltage set-point.

The problem of the evaluation of fuzzy situations arises quite often in real life. Images, symbols, signals and so on usually are fuzzily described and can be considered as fuzzy situations. So the parameters, which characterize the fuzzy situation, are not only numerical ones, but verbal too, for example, an object can be described as "heavy, little and moves fast". It is difficult to decide something formally about the object with such characterization. Leasing companies meet this problem in contracts classification process too. Accordingly most leasing companies have own contracts classification (recognition) systems, which mostly are statistically based and the similarities and differences between good and bad contracts are not computed. Therefore the demand arises to develop the intellectual recognition system using fuzzy logic. This system should be simply integrating in any leasing company. It also must compute and measure the similarities and differences between good and bad contracts from their evaluation history. Such intellectual system could be as an adviser for business expert. The module of fuzzily characterized situation's evaluation is implemented using: 1) fuzzy sets, 2) fuzzy clustering, 3) fuzzy recognition. The general idea was experimentally investigated using artificially generated data as well as using data from real contracts. The software developed during this research is under preparation to be integrated in companies "Baltic... [to full text]

Wireless sensor nodes are resource constrained and have limited amount of energy. Therefore, designing protocols that conserve energy is an important area of research. Researchers have investigated architectures and topologies that allow energy efficient operation of WSNs. One of the popular techniques in this regard is clustering. A typical clustering protocol contains two main steps: cluster head election and cluster formation. This thesis is aimed at investigation of the cluster formation process. We propose a Fuzzy Logic based approach that uses three descriptors namely: energy level, distance between cluster-head and base station, and distance between the cluster-head and the sensor’s node. We compare our proposed model, FLCFP (Fuzzy Logic Clustering Formation Protocol), with the most popular model, LEACH, which was proposed previously to prolong network lifetime. FLCFP approach is shown to prolong network lifetime. In addition, it is shown that sensor node energy is consumed in a more uniform fashion.

In this thesis, an alternative control method based on Fuzzy Inference System (FIS) is proposed to keep the product composition of a distillation column constant. This study compares a proposed FIS to traditional PID (proportional, integral, derivative) control technique and analyzes the results. The FIS is applied to the control of the tray temperature of the distillation column by using indirect feed split control structure to modulate the steam flow with management of the tray temperature. In turn, this modulation maintains the composition of product at specified levels. Rockwell fuzzy designer is used to develop the fuzzy logic controller. Both a fuzzy logic controller and a PID controller are downloaded in the Rockwell ControlLogix L62 process controller. Chemstations Chemcad simulation software is used to run the distillation column simulations. Simulation results are programmed into L62 process controller to behave as a dynamic distillation column. Results show that the proposed fuzzy logic controller is more tolerant to disturbances in the feed flow and feed composition of the distillation column than the PID controller.

This thesis introduces a novel online set-point modulation (SPM) technique that employs fuzzy logic to adjust the timing and magnitude of control system set-points. After first applying fuzzy logic-enabled SPM to underdamped second order systems for testing purposes, the new scheme is then applied to an electromagnetic transient simulation model of Manitoba Hydro’s existing Ponton SVC system, and the results are then compared to the original SVC system that uses traditional control approaches. Conclusions show that fuzzy logic-based SPM improves the controlled voltage’s transient overshoot response to changes in the voltage set-point.

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While modern engineering systems have become increasingly integrated and complex over the years, interest in the application of control techniques which specifically attempt to formulate and solve the control problem in its inherently uncertain environment has been moderate, at best. More specifically, although many control schemes targeted at Heating, Ventilating and Air-Conditioning (HVAC) systems have been reported in the literature, most seem to rely on conventional techniques which assume that a detailed, precise model of the HVAC plant exists, and that the control objectives of the controller are clearly defined. Experience with HVAC systems shows that these assumptions are not always justifiable, and that, in practice, these systems are usually characterized by a lack of detailed design data and a lack of a robust understanding of the processes involved. Motivated by the need to more efficiently control complex, uncertain systems, this thesis focuses on the development and evaluation of a new neuro-fuzzy model-based predictive control scheme, where certain variables used in the optimization remain in the fuzzy domain. The method requires no training data from the actual plant under consideration, since detailed knowledge of the plant is unavailable. Results of the application of the control scheme to the control of thermal comfort in a simulated zone and to the control of the supply air temperature of an air-handling unit in the laboratory are presented. It is concluded that precious resources (as measured by actuator activity, for example) need not be wasted when controlling these systems. In addition, it is also shown that a very precise (and sometimes not necessarily accurate) control value computed at each sample is unnecessary. Rather, by defining the system and its environment in the fuzzy domain, the fuzzy decision algorithms developed here may be employed to get an "acceptable" control performance.

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