Model Predictive Control (MPC) is an advanced control strategy that uses an optimization algorithm to determine the optimal control inputs for a system over a finite period of time, based on a mathematical model, predictions of future disturbances, and current system states. Compared to simpler control methods like PID, MPC can provide better control performance by anticipating future disturbances and taking account of system constraints. MPC requires a mathematical model of the system, a prediction horizon, a control horizon, a cost function, and constraints on the system states and control inputs. This makes the design and tuning of an MPC system more complex than the simpler methods, and MPC is also computationally expensive. However, the benefits of improved performance and safe operation with complex systems make this method a powerful choice for many control applications.
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