Performance Analysis of Boost Converter Using Model Predictive Controller

Model Predictive Control (MPC) has emerged as a powerful control strategy for power electronics applications, particularly in DC-DC converters, due to its ability to handle system constraints and optimize performance in real time. The boost converter is a crucial component in many power electronic systems, widely used in renewable energy systems, electric vehicles, and industrial applications. Achieving high efficiency and dynamic performance in such converters is a significant challenge due to the non-linear and time-varying nature of the system. This study describes how to effectively regulate a DC-DC boost converter voltage using a Model Predictive Controller (MPC). The boost converter, widely used in power electronics for stepping up voltage levels, requires precise control to ensure optimal performance, especially under varying load and input conditions. Traditional control methods often struggle with nonlinearity and rapid dynamic changes, leading to performance degradation. MPC, with the flexibility to tackle multi-variable control issues and forecast future actions using a system model, offers a robust solution. By continuously solving an optimization problem at each sampling interval, MPC adjusts the control input to maintain the desired output voltage while respecting system constraints. Simulation results demonstrate that MPC outperforms conventional control methods by improving transient response, reducing steady-state error, and enhancing the overall efficiency of the boost converter. The proposed approach also showcases the controller's adaptability to real-time disturbances, making it a promising technique for advanced power electronic applications.