چكيده به لاتين
Power electronic switching converters are widely used in the electrical and electronics industries due to their high efficiency, small size, reasonable price and high reliability. Boost converter is a widely used DC-DC converter in electrical machines, renewable sources, uninterruptible power supplies, power factor correction equipment, etc. In general, power electronic converters are nonlinear systems that change with time, and due to the uncertainty of the converter parameters, it is difficult to accurately model them dynamically in all converter operating conditions, so controlling boost converters is a challenging nonlinear control problem. therefore, the linear controller not relatively suitable because the converter model depends on the condition of the switching elements. In this research, a nonlinear control strategy based on reinforcement learning for boost converter control is proposed. In this research, the problem of boost converter control is formulated as an optimal multi-stage decision problem with the aim of achieving a constant output voltage. Optimal multi-stage decision-making problems can be solved using the framework of Markov decision-making processes and reinforcement learning. The simulation results show that the reinforcement learning strategy to control the output voltage with appropriate speed and accuracy has converged to 80 volts where the required voltage is the problem.
