This project aims to design an electric drive system for a hoisting mechanism, focusing on minimizing the time for lifting and lowering loads. The design process includes motor parameter selection, control scheme design, and simulation validation.
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Analyze Technical Parameters and Control Requirements
- Analyze load characteristics and control requirements.
- Provide a block diagram of the drive system composition.
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Select Motor
- Choose appropriate DC or AC motor rated parameters based on technical parameters and control requirements (calculation required before selection).
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Design Control Scheme
- Select suitable motor start, speed regulation, and braking schemes according to control requirements.
- Analyze the four-quadrant operating points based on motor operation principles and mechanical characteristic curves (lifting the weight from the ground to a high place and then smoothly placing it back on the ground).
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Build MATLAB Simulation Platform
- Set motor parameters.
- Simulate the chosen motor start, braking, and speed control methods using MATLAB.
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Simulate the Drive System
- Build a simulation framework for the drive system on the MATLAB simulation platform.
- Simulate the entire process of lifting and lowering the weight.
- Debug the system operation, observe changes in key variables (speed, current, voltage, torque), and adjust the control scheme according to load control requirements.
- Control Objective: Design the electric drive system of the hoisting mechanism with the shortest possible lifting/lowering time as the control objective.
- Empty hook weight: 10 kg
- Maximum load: 1000 kg
- Transmission ratio: 100:1
- Transmission efficiency: 0.9
- Maximum lifting speed: 1500 r/min
- Minimum lowering speed: 300 r/min
- Lifting/lowering height: 25 m
- Drum diameter: 0.4 m
- Open platform: MATLAB 2023 Simulink
- Determine motor selection and control scheme by analyzing technical parameters.
- Use MATLAB Simulink to build the simulation platform, input motor parameters and control schemes.
- Run the simulation, observe key variable changes, and adjust the control scheme to achieve optimal performance.
