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MPC, model predictive control 본문
-Model predictive control
https://en.wikipedia.org/wiki/Model_predictive_control
Model predictive control (MPC) is an advanced method of process control that is used to control a process while satisfying a set of constraints.
While many real processes are not linear, they can often be considered to be approximately linear over a small operating range. Linear MPC approaches are used in the majority of applications with the feedback mechanism of the MPC compensating for prediction errors due to structural mismatch between the model and the process. In model predictive controllers that consist only of linear models, the superposition principle of linear algebra enables the effect of changes in multiple independent variables to be added together to predict the response of the dependent variables. This simplifies the control problem to a series of direct matrix algebra calculations that are fast and robust.
-Dynamical system
https://en.wikipedia.org/wiki/Dynamical_system
In mathematics, a dynamical system is a system in which a function describes the time dependence of a point in a geometrical space.
-대수학 (algebra)
https://ko.wikipedia.org/wiki/%EB%8C%80%EC%88%98%ED%95%99
대수학은 일련의 공리들을 만족하는 수학적 구조들의 일반적인 성질을 연구하는 한 분야.
대수학은 방정식을 푸는 방법을 연구하는 학문에서 시작되었다.
대수학 = 방정식 (쉽게 생각해서)
-Euler–Lagrange equation
https://en.wikipedia.org/wiki/Euler%E2%80%93Lagrange_equation
Euler–Lagrange equation is useful for solving optimization problems in which, given some functional, one seeks the function minimizing or maximizing it.
- MPC의 optimization 을 Euler-Lagrange equations 를 통해 함.
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