Model Predict Control

Model Predictive Control (MPC) is a control strategy that uses mathematical models to predict the future behavior of a system and optimize control actions to achieve desired performance. It is a closed-loop control approach that continuously measures the system's state, estimates the future state based on the model, and calculates the optimal control actions to achieve a desired objective.

MPC is widely used in various industries such as chemical, process, and automotive industries, where complex systems need to be controlled in real-time. It has several advantages over traditional control methods, including the ability to handle constraints on input and output variables, the ability to handle nonlinear systems, and the ability to handle time-varying dynamics.

MPC involves several steps, including model formulation, state estimation, optimization, and control action calculation. The model is typically formulated as a set of differential equations that describe the system's behavior. The state estimation step involves estimating the current state of the system using available sensor measurements. The optimization step involves formulating an optimization problem that minimizes a cost function while satisfying the system constraints. The control action calculation step involves solving the optimization problem to calculate the optimal control actions to achieve the desired objective.

Overall, MPC is a powerful control strategy that enables the efficient and effective control of complex systems. Its ability to handle constraints and nonlinear dynamics makes it particularly useful in real-world applications.