Сравнение на методи

	Моделно-предиктивно управление	Линеаризация чрез обратна връзка
Област	Теория на управлението	Теория на управлението
Семейство	Machine learning	Machine learning
Година на възникване	1978	1983
Създател	Jacques Richalet	Alberto Isidori
Тип	algorithm	algorithm
Основополагащ източник	Richalet, J., Rault, A., Testud, J., & Papon, J. (1978). Model predictive heuristic control. Automatica, 14(5), 413-428.	Isidori, A. (1995). Nonlinear Control Systems (3rd ed.). Springer-Verlag.
Други названия	MPC, Receding Horizon Control	Exact Linearization, Nonlinear Feedback Control, Input-Output Linearization
Свързани	5	4
Резюме	Model Predictive Control (MPC) is an advanced control strategy that uses an explicit process model to predict future system behavior over a finite horizon and solves an optimization problem at each control step. First formalized by Richalet et al. in 1978, MPC has become the dominant approach in process control industries, from chemical plants to autonomous vehicles, because it naturally handles constraints and can optimize multiple objectives simultaneously.	Feedback Linearization is a nonlinear control technique that uses a nonlinear state-feedback transformation to convert a nonlinear system into a linear one, enabling the use of standard linear control methods. Developed by Isidori, Sontag, and others in the 1980s, feedback linearization is conceptually elegant and powerful: if the system satisfies certain structural conditions (relative degree, decoupling matrix rank), the nonlinearities can be exactly cancelled through feedback, reducing the problem to linear design.