Fundamentals of Model Predictive Control

This chapter discusses how the process of transforming or mapping continuous functions, models, and equations into discrete counterparts is related to discrete mathematics and is often referred to as discretization. The finite control‐set model predictive control (FCS‐MPC) strategy is an intuitive and powerful tool for controlling power converters compared with the classical linear control with proportional‐integral (PI) regulator and space vector modulation (SVM). The closed‐loop stability is a classical subject matter in control engineering. In the closed control of power converters, state variables such as voltages, currents, and power have a tendency to diverge from the set‐point value. The definition of the cost function is an important design stage in FCS‐MPC for fulfilling the operational requirements in various power conversion applications. In general, the cost function design is simple and flexible and can handle multivariable systems with different physical natures, magnitudes, frequencies, and phase angles for control variable. Numerous power conversion applications employ current control in the inner control loop, irrespective of the power converter topology.