Fuzzy modeling and PDC control of a power generation system based on an internal combustion engine with an electric generator

In this work, a Takagi-Sugeno (T-S) type fuzzy strategy is used, with optimized model and control parameters, applied to a power generating system composed of an internal combustion engine (ICE) coupled to an electric generator. This strategy avoids deriving a mathematical model by means of energy balance with thermodynamics and the electric generator in its nonlinear representation. First, a nonlinear mathematical model for the electrical power system is identified using the fuzzy c-means algorithm in the premises and the consequent in state space, with experimental input-output data. Then, using the particle swarm algorithm (PSO), the optimal fuzzy parameter is identified and to find the optimal poles of the system for the controller gains. Then, using the parallel distributed compensation (PDC) strategy, for each consequent of the T-S fuzzy model, a state feedback controller is tuned. Also, linear matrix inequalities (LMI) are used to demonstrate the stability of the system. Finally, in order to validate the proposed strategies, experimental physical tests are performed with a brushless motor as load, varying the power consumption.