Gas turbines are complex processes characterized by the instability and uncertainty of various sources. The range of useful operating in an axial compressor which is part of a turbine gas is limited by aerodynamic instabilities that are surge and rotating stall. This paper presents two intelligent fractional order sliding mode controllers. At first, a robust sliding fractional surface form is proposed to deal with hazardous phenomena which limit compression systems performance, and speed transitions, which can lead to temporary stall development, pressure drop at the output, degrade the effective operation of compressors and consequently gas turbines. Second, to reduce the chattering/fluctuation in control, a fuzzy logic and finite time criterion are used as switching control at the reaching phase in the sliding mode control. Additionally, the controller gains are obtained by offline multi-objective Particle swarm optimization (MOPSO) search. Finally, the surge and rotating stall of a Variable Speed Axial Compressor (VSAC) in a gas turbine are investigated under the system nonlinearities and also in presence of an external disturbance and perturbations. The simulation results signify the performance of the two MOPSO-based fractional sliding mode controllers.
In gas turbine process, the axial compressor is subjected to aerodynamic instabilities because of rotating stall and surge associated with bifurcation nonlinear behaviour. This paper presents a Genetic Algorithm and Particle Swarm Optimization (GA/PSO) of robust sliding mode controller in order to deal with this transaction between compressor characteristics, uncertainties and bifurcation behaviour. Firstly, robust theory based equivalent sliding mode control is developed via linear matrix inequality approach to achieve a robust sliding surface, then the GA/PSO optimization is introduced to find the optimal switching controller parameters with the aim of driving the variable speed axial compressor (VSAC) to the optimal operating point with minimum control effort. Since the impossibility of finding the model uncertainties and system characteristics, the adaptive design widely considered to be the most used strategy to deal with these problems. Simulation tests were conducted to confirm the effectiveness of the proposed controllers.
In order to actively control combustion reaction, this study proposes an adaptive neuro-fuzzy (ANFIS) control scheme of interaction between premixed combustion reaction and acoustic flame perturbation where the flame pressure movement will be considered as model perturbation. Using the Cantera database, it is possible to investigate the mechanisms by which the combustion process interacts with acoustic, vorticity, and entropy waves. A well-stirred reactor (WSR) has been extensively used to model combustion processes in three different reaction zone regimes. We designed the control architecture to achieve an intelligent representation of the system for various operating scenarios, which was motivated by the complexity of the mathematical model that was being used. This goal is accomplished by an artificial bee colony (ABC), which uses simulated data from a mathematical model to optimize a neuro-fuzzy with less computational expense. The optimized neuro-fuzzy identifier is converted to an adaptive neural-based (ANFIS) controller optimized to control the outputs of the system. In keeping with the combustion temperature set point, the results demonstrate a remarkable attenuation of flame perturbation and acceptable combustion reaction quality (NOx emission).
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