الفهرس 
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Abstract
This work is motivated by presenting an intelligent supervisory controller based on the BIBO stability conditions for improving the response of the industrial processes. This work is focused on the flow process as the control of flow is commonly a conclusive matter in many processes as it is considered an effective factor of product quality and safety of critical processes such as nuclear reactors, distillation columns, agriculture activities (irrigation, sprayers) and gas lines. Tracking the accurate models is required for simulating the process behavior, also for controller design relying on these models. The major objectives of the research undertaken in this thesis was to propose a novel schemes for modeling the nonlinear flow process and introduce an intelligent supervisory controller to improve the output performance. Thesis objectives was achieved by proposing the FSMM and FTPM schemes for the identification process and introducing the Fuzzy-PI controller. The FSMM technique was proposed by making subspace identification at discerning operating conditions. A first order plus time delay structure was chosen to be the basis for the local models. Then the local models were synthesized by the fuzzy model synthesizer. For the proposed FTPM, the first order plus time delay structure was chosen to be the basis for the model (lower-level). Then the fuzzy parameter synthesizer (upper-level) was constructed to generate the input-dependent model parameters. The proposed approaches were compared with NARX and HW models. Experimental study on the PCS training set was created to further validate the effectiveness of the proposed models. The steady state I/O map was utilized to compare the output of the models with that of physical process. The application of I/O map determined the level of congruence between the process and the proposed models. The experimental results proved the high closeness between the proposed models and the physical process. The results indicated also the capability of the proposed techniques to precisely capture the real process dynamics, reduce the value of the NRMSE, reduce the complexity of the single global modeling and increase the operating range with better model accuracy. The fuzzy-PI controller was constructed to control the best model (FTPM). The Fuzzy-PI control scheme was formed by lower-level classical PI controller and upper-level intelligent controller. The lower-level part was the main controller which should deliver the control signal to the process and the upper-level part provided a mechanism to adapt the classical PI controller parameters. The BIBO stability of the Fuzzy-PI control system was performed using the SGT. The overall Fuzzy-PI controller parameters were chosen to ensure the BIBO stability conditions. Based on the results of simulation and practical cases conducted in this context, it was demonstrated the robustness and the good performance of the Fuzzy-PI controller to satisfy stable tracking of the flow process to the desired outputs under set-point tracking and many disturbance changes. The organization of the thesis is as follows: This context involves five chapters organized as follows: Chapter 1 represents an introduction to modeling and control of flow process, including a literature review, motivation and thesis outline and organization. Chapter 2: This chapter presents the structure of the PCS training set, the component list and the specifications for each component. Also shows the configuration diagram of the PCS training set. Chapter 3 introduces the modeling of the nonlinear flow process as a first object of this research. First, this chapter gives a brief notes about the types of process modeling and the system identification techniques. Second, this chapter proposes the FSMM and the FTPM for the identification process. The designing stages of each scheme are described. In this chapter, graphical and numerical methods are used for the validation process of the proposed models compared with NARX and HW models. Moreover, the models are validated in the self-validation phase also two cross-validations are applied to powerfully demonstrate the effectiveness of the proposed schemes. Chapter 4: The second object of this research is performed in this chapter. The control of the flow process is provided based on the intelligent supervisory control specially the Fuzzy-PI controller. The designing stages of the fuzzy-PI controller are described. In this chapter, the BIBO stability analysis of the overall control system is performed using the SGT. A comparison between the conventional and the fuzzy-PI controllers is also performed in this chapter via the simulation and practical results. Chapter 5: conclusions and future directions for further investigations are conducted.