Unconstrained Model Predictive Control

Unconstrained model predictive control is a widely adopted method in the field of automation control. This approach enables prediction and control of complex systems through learning mechanisms without requiring explicit system models. The implementation typically involves solving a quadratic optimization problem at each control interval, where the cost function minimizes the difference between predicted outputs and reference trajectories. Key algorithmic components include state-space modeling, prediction horizon configuration, and recursive least-squares estimation for system identification. Unconstrained MPC demonstrates superior robustness and stability compared to many alternative methods, while inherently adapting to handle noise and disturbances through its receding horizon implementation. This adaptive capability allows for enhanced control performance across diverse operational environments, with common implementations featuring real-time optimization solvers and disturbance rejection mechanisms.