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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
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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