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1. Define the Environment

The environment is your system controlled by the PID controller. In an RL context, the environment must provide:

• State: A representation of the current status of the system. This could include the current error, previous error, and any other relevant information about the system's performance.
• Reward: Feedback on the system's performance. A common approach is to use a function of the error (e.g., the negative absolute error) so that a smaller error gives a higher reward.

2. Define the Agent and Action Space

The agent will adjust the PID parameters ((K_p), (K_i), (K_d)) to optimize performance. Actions in this context could be changes to these parameters. The action space must be carefully defined to allow the agent to explore different settings effectively. You might use a continuous action space where each action is a vector of changes to apply to (K_p), (K_i), and (K_d).

3. Choose an RL Algorithm

Given that the action space is continuous, algorithms designed for continuous action spaces are more suitable. Consider using:

• Deep Deterministic Policy Gradient (DDPG)
• Proximal Policy Optimization (PPO)
• Soft Actor-Critic (SAC)

4. Implement the RL Model

Using an existing framework like OpenAI Gym for the environment and Stable Baselines3 or TensorFlow Agents for the RL algorithm can simplify the implementation. You might need to create a custom Gym environment that simulates your PID-controlled system.

5. Train the Model

During training, the agent will interact with the environment, adjusting the PID parameters to maximize the cumulative reward (which correlates with minimizing the error). The training process involves balancing exploration (trying new PID settings) and exploitation (using settings known to perform well).

6. Evaluate and Fine-Tune

After training, evaluate the performance of the tuned PID controller to ensure it meets your system's requirements. It may be necessary to adjust the reward function, exploration strategy, or other parameters to achieve the desired outcomes.

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Slide 1: Introduction to PID Control with Reinforcement Learning

• Title: Implementing PID Control in Reinforcement Learning
• Content:
  • Using Python and RL libraries for PID control system modeling.
  • Gymnasium framework for simulation environment setup.

Slide 2: Custom Environment for DC Motor Control

• Title: Simulating a DC Motor with PID Control
• Content:
  • Custom Gym environment for DC motor simulation.
  • Angular velocity as the state, PID parameters (Kp, Ki, Kd) as actions.
  • Negative error as reward to minimize control errors.

Slide 3: Proximal Policy Optimization (PPO) Selection

• Title: Why PPO for PID Control?
• Content:
  • PPO chosen for balance between efficiency and implementation ease.
  • Ideal for continuous action spaces and ensures stable learning.

Slide 4: Action Space Definition

• Title: Defining the Action Space
• Content:
  • Actions represent PID controller parameters.
  • Wide bounds set for (Kp, Ki, Kd) to explore efficient control strategies.

Slide 5: Training Process and Model Evaluation

• Title: Training and Evaluating the Model
• Content:
  • PPO model training over 10,000 timesteps.
  • Evaluation focuses on minimizing the error between desired and actual motor speeds.

Slide 6: Addressing Training Challenges

• Title: Overcoming Common RL Challenges
• Content:
  • Strategies include ensuring numerical stability and appropriate parameter bounds.
  • Importance of tuning PID parameters within a feasible range for effective learning.

Slide 7: Conclusion and Looking Ahead

• Title: Achievements and Future Directions
• Content:
  • Successful PID control of a DC motor's angular velocity via RL.
  • Future work to explore complex control scenarios and optimization techniques.

how to setup the project

• Clone the repository
• Install poetry
• Run pip install poetry
• Run poetry install to install the dependencies
• Run poetry shell to activate the virtual environment
• Run python base.py to run the project