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✳️Inverted Pendulum Control: RL + MPC Implementation

1️⃣ Reinforcement Learning Features:
- Q-Learning for system identification
- Self-learning pendulum balancing
- No prior model needed

2️⃣ MPC Implementation:
- Real-time optimization
- Constraint handling
- Precise position/angle control

3️⃣ Hardware:
- DC motor (50:1 gearbox)
- Dual encoders
- STM32 controller
- Custom PWM driver

4️⃣ Performance:
- Upright stabilization
- Disturbance rejection
- Accurate tracking

By: Javad Safaei
Supervisor: Naser Pakar
🔹Telegram:
🆔 @MATLAB_House
@MATLABHOUSE

#InvertedPendulum #ReinforcementLearning #ModelPredictiveControl #Robotics #ControlSystems #Engineering

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904 viewsNaser Pakar, edited  Oct 25, 2024 at 17:09

✳️Inverted Pendulum Control: RL + MPC Implementation

1️⃣ Reinforcement Learning Features:
- Q-Learning for system identification
- Self-learning pendulum balancing
- No prior model needed

2️⃣ MPC Implementation:
- Real-time optimization
- Constraint handling
- Precise position/angle control

3️⃣ Hardware:
- DC motor (50:1 gearbox)
- Dual encoders
- STM32 controller
- Custom PWM driver

4️⃣ Performance:
- Upright stabilization
- Disturbance rejection
- Accurate tracking

By: Javad Safaei
Supervisor: Naser Pakar
🔹Telegram:
🆔 @MATLAB_House
@MATLABHOUSE

#InvertedPendulum #ReinforcementLearning #ModelPredictiveControl #Robotics #ControlSystems #Engineering

BY MATLAB House :: Channel