제어 알고리즘 설계
토크 제어 서브시스템과 속도 제어 서브시스템을 만들고, 타깃 실행 시간을 확인하고, 제어 이득을 조정합니다.
카테고리
- 벡터 제어
FOC(자속 기준 제어) 및 DTC(직접 토크 제어)와 같은 벡터 제어 기법을 활용하여 모터 제어 알고리즘을 설계한다
- 6단계 정류
6단계 정류 기법을 이용한 모터 제어 알고리즘 설계
- 개루프 제어
개루프 제어를 사용한 모터 제어 알고리즘의 설계
- 이득 계산 및 조정
벡터 제어를 적용하기 위한 PI 제어기 이득 계산
- 비선형 특성 분석
실제 환경을 기반으로 한 비선형 특성을 활용하는 모터 제어 알고리즘의 설계
추천 예제
Generate Motor Control Models for Selected Algorithm and Hardware
Use Motor Control Blockset™ to generate a Simulink® model that is configured for a specific hardware and motor control technique.
Algorithm-Export Workflows for Custom Hardware
Enables you to use any custom motor-control hardware (hardware not used in the Motor Control Blockset™ examples) to run a three-phase permanent magnet synchronous motor (PMSM) using field-oriented control (FOC). Using the algorithm export workflows, which involve generating code for the control algorithm by using Simulink® and Embedded Coder® and then integrating it with either manually written or externally generated hardware driver code. This example explains the algorithm export workflows along with the intermediate steps.
Swap Motors with Single Deployment of Sensorless FOC Algorithm
Run a permanent magnet synchronous motor (PMSM) in an industrial drive application setup using a sensorless field-oriented control (FOC) algorithm. The example uses a sensorless Flux Observer to estimate the motor position. Industrial drives enable you to replace a motor with a new one without repeated deployment of code. An industrial drive setup needs only nameplate parameters to adapt the software to the new motor.
AUTOSAR-Based FOC of PMSM
Implement an AUTOSAR-based field-oriented control (FOC) algorithm to run a permanent magnet synchronous motor (PMSM).
Field-Oriented Control of PMSM Using Position Estimated by Neural Network
Implement field-oriented control (FOC) of a permanent magnet synchronous motor (PMSM) using a rotor position estimated by an autoregressive neural network (ARNN) trained with Deep Learning Toolbox™.
Field-Oriented Control of PMSM Using Reinforcement Learning
Use the control design method of reinforcement learning to implement field-oriented control (FOC) of a permanent magnet synchronous motor (PMSM). The example uses FOC principles. However, it uses the reinforcement learning (RL) agent instead of the PI controllers. For more details about FOC, see 자속 기준 제어(FOC).
Motor Control Architectures Based on Different Current Sampling and PWM Frequencies
Enables you to implement different motor control architectures that use non-identical sampling rates for ADC conversion, PWM, and current controller algorithm to run a permanent magnet synchronous motor (PMSM) using field-oriented control (FOC).
Run Field Oriented Control of PMSM Using Model Predictive Control
Uses Model Predictive Control (MPC) to control the speed of a three-phase permanent magnet synchronous motor (PMSM).
Field-Oriented Control of PMSM Using Reinforcement Learning
Use the control design method of reinforcement learning to implement field-oriented control (FOC) of a permanent magnet synchronous motor (PMSM). The example uses FOC principles. However, it uses the reinforcement learning (RL) agent instead of the PI controllers. For more details about FOC, see 자속 기준 제어(FOC).
Determine Power Losses and THD for PWM Methods
Calculates the inverter power loss and total harmonic distortion (THD) in motor current for different pulse-width modulation (PWM) methods. The example uses field-oriented control (FOC) algorithm that runs a permanent-magnet synchronous motor (PMSM) in speed control mode as a reference. The example only supports simulation.
Analyze and Verify Motor Control Algorithms Using Polyspace
Uses the Polyspace® static code analysis tools to analyze and verify Simulink® models containing motor control algorithms. Static code analysis is a software verification technique that analyzes source code for quality, reliability, and security without executing the code. This approach uses robust error detection routines (that include checks for critical run-time errors) to identify bugs and defects and in addition ensures compliance with common coding standards. It provides a cost-effective alternative to measure and track the software quality metrics and eliminates the need to instrument the code or to write elaborate unit test cases.
