AI in Electromechanical & Fluid-Powered Actuators

To equip participants with interdisciplinary knowledge and hands-on skills to apply Artificial Intelligence (AI) in electromechanical and fluid-powered actuation systems, improving control, responsiveness, energy efficiency, and fault prediction in dynamic environments.

• Bridge AI techniques with real-world actuator systems

• Introduce participants to data-driven control theory and implementation

• Promote reliability, energy efficiency, and adaptive behavior in systems

• Explore case studies that validate AI integration in motion systems

• Enable participants to build prototypes and testbeds using AI frameworks

Day 1: AI for Motor Control & Predictive Modeling

🧩 Core Module:

• 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)

🛠️ Hands-On Lab:

• 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

Day 2: AI-Enhanced Fluid Control & Reinforcement Learning

🧩 Core Modules:

• 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

🛠️ Hands-On Lab:

• 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

Day 3: Sensor Fusion, Signal Processing & AI Simulation

🧩 Core Modules:

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

🛠️ Hands-On Lab:

• 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

• Aerospace, automotive, and defense engineering companies

• Industrial automation and process control firms

• Robotics R&D labs and innovation startups

• Smart agriculture and medical robotics sectors

• OEMs and system integrators for hydraulic/pneumatic systems

• Mechatronics and mechanical engineers

• Electrical/control systems engineers

• Robotics developers and automation specialists

• Researchers in AI, fluid mechanics, or mechatronic systems

• Advanced students (UG/PG/PhD) in related fields