AI in Electromechanical & Fluid-Powered Actuators

Focus: Predictive control algorithms for electromechanical systems

• AI for DC/AC Motor Modeling and Predictive Control
  • Overview of motor dynamics
  • Dataset generation for speed/torque profiling
  • Introduction to predictive models (Regression, LSTM)

• Build a Predictive Model for DC Motor Speed Control
  • Simulate motor control using Python or MATLAB
  • Train model with historical speed and torque data
  • Predict future states for PID enhancement

Focus: Pressure optimization and adaptive control in fluid systems

• Reinforcement Learning for Fluid Pressure Control
  • Fundamentals of Q-learning for continuous systems
  • Application to fluid dynamics and system response
• Intelligent Fault Detection in Hydraulic & Pneumatic Loops
  • Common failure patterns (leaks, pressure drops)
  • AI classifiers for anomaly detection

• Optimize Pneumatic Stroke via Q-Learning
  • Define state-action space for piston control
  • Implement Q-learning agent for energy-efficient control
  • Visualize pressure vs stroke response

Focus: Real-time inference and simulation in smart systems

• Sensor Fusion and AI Signal Processing
  • Combining pressure, position, and temperature sensors
  • Kalman Filters, AI-based filtering
  • Noise reduction and signal calibration

• Real-Time Simulation of a Hydraulic Press using ML
  • Integrate multiple sensor inputs
  • Build ML model to simulate and predict press behavior
  • Real-time decision-making for system protection