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기본 모델

선형 시스템의 일반적인 모델(예: 전달 함수 및 상태공간 모델).

수치적 LTI(선형 시불변) 모델은 선형 시스템을 나타내는 데 사용하는 기본 구성요소입니다. 수치적 LTI 모델 객체를 사용하면 동적 시스템을 널리 사용되는 표현으로 저장할 수 있습니다. 예를 들어, tf 모델은 전달 함수를 분자 및 분모 다항식의 계수로 표현하고 ss 모델은 LTI 시스템을 상태공간 행렬로 표현합니다. 비례 계수, 적분 계수, 미분 계수로 PID 제어기를 나타내는 데 특화된 LTI 모델 유형도 있습니다.

개별 구성요소를 LTI 모델로 표현하고 여러 구성요소를 연결하여 제어 아키텍처를 모델링하면 더 복잡한 제어 시스템 모델을 작성할 수 있습니다. 예제는 Control System Modeling with Model Objects 항목을 참조하십시오.


모두 확장

tf전달 함수 모델
zpk영점-극점-이득 모델
ss상태공간 모델
frdCreate frequency-response data model, convert to frequency-response data model
filtSpecify discrete transfer functions in DSP format
dssCreate descriptor state-space models
pid병렬 형식 PID 제어기 만들기, 병렬 형식 PID 제어기로 변환
pidstd Create a PID controller in standard form, convert to standard-form PID controller
pid2Create 2-DOF PID controller in parallel form, convert to parallel-form 2-DOF PID controller
pidstd2 Create 2-DOF PID controller in standard form, convert to standard-form 2-DOF PID controller
rssGenerate random continuous test model
drssGenerate random discrete test model


LTI SystemSimulink에서 선형 시불변 시스템 모델 객체 사용
LPV SystemSimulate Linear Parameter-Varying (LPV) systems

도움말 항목


Control System Modeling with Model Objects

Model objects can represent components such as the plant, actuators, sensors, or controllers. You connect model objects to build aggregate models that represent the combined response of multiple elements.

What Are Model Objects?

Model objects represent linear systems as specialized data containers that encapsulate model data and attributes in a structured way.

Using Model Objects

Ways to use model objects include linear analysis, compensator design, and control system tuning.

연속시간 모델

Creating Continuous-Time Models

This example shows how to create continuous-time linear models using the tf, zpk, ss, and frd commands.

Transfer Functions

Represent transfer functions in terms of numerator and denominator coefficients or zeros, poles, and gain.

State-Space Models

Represent state-space models in terms of the state-space matrices.

Frequency Response Data (FRD) Models

Represent dynamic systems in terms of the magnitude and phase of their responses at various frequencies.

Proportional-Integral-Derivative (PID) Controllers

Represent PID controllers in terms of controller gains or time constants.

Two-Degree-of-Freedom PID Controllers

2-DOF PID controllers can achieve faster disturbance rejection without significant increase of overshoot in setpoint tracking.

Using the Right Model Representation

This example shows some best practices for working with LTI models.

이산시간 모델

Creating Discrete-Time Models

This example shows how to create discrete-time linear models using the tf, zpk, ss, and frd commands.

Discrete-Time Numeric Models

Represent discrete-time numeric models by specifying a sample time when you create the model object.

Discrete-Time Proportional-Integral-Derivative (PID) Controllers

The integrator and filter terms in discrete-time PID controllers can be represented by several different formulas.


MIMO Transfer Functions

Create MIMO transfer functions by concatenating SISO transfer functions or by specifying coefficient sets for each I/O channel.

MIMO State-Space Models

These examples show how to represent MIMO systems as state-space models.

MIMO Frequency Response Data Models

Use frequency-response data from multiple I/O pairs in a system to create a MIMO frequency response model.

Select Input/Output Pairs in MIMO Models

Extract particular I/O channels from a MIMO dynamic system model.

Simulink의 LTI 모델

Import LTI Model Objects into Simulink

Use the LTI System block to import linear system model objects into Simulink®.

모델 객체에 대한 세부 정보

Types of Model Objects

Model object types include numeric models, for representing systems with fixed coefficients, and generalized models for systems with tunable or uncertain coefficients.

Dynamic System Models

Represent systems that have internal dynamics or memory of past states, such as integrators, delays, transfer functions, and state-space models.

Numeric Models

Numeric LTI Models represent dynamic elements, such as transfer functions or state-space models, with fixed coefficients.

Static Models

Represent static input/output relationships, including tunable or uncertain parameters and arrays.