AI in Electromechanical & Fluid-Powered Actuators

AI in Electromechanical & Fluid-Powered Actuators is an international workshop tailored for professionals, researchers, and engineers exploring how machine learning and intelligent control systems are revolutionizing the behavior and performance of actuators in robotics, aerospace, automotive, and industrial automation.

• 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

Focus: Predictive control algorithms for electromechanical systems

🧩 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

Focus: Pressure optimization and adaptive control in fluid systems

🧩 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

Focus: Real-time inference and simulation in smart systems

🧩 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

• 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