AI in Electromechanical & Fluid-Powered Actuators
International Workshop on Intelligent Control and Optimization of Actuation Systems
About This Course
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.
Aim
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.
Workshop Objectives
-
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
Workshop Structure
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
Who Should Enrol?
-
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
Important Dates
Registration Ends
05/29/2025
IST 5 PM
Workshop Dates
05/29/2025 – 05/31/2025
IST 6 PM
Workshop Outcomes
-
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
Meet Your Mentor(s)
Fee Structure
Student Fee
₹1999 | $54
Ph.D. Scholar / Researcher Fee
₹2999 | $65
Academician / Faculty Fee
₹3999 | $75
Industry Professional Fee
₹5999 | $95
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
Join Our Hall of Fame!
Take your research to the next level with NanoSchool.
Publication Opportunity
Get published in a prestigious open-access journal.
Centre of Excellence
Become part of an elite research community.
Networking & Learning
Connect with global researchers and mentors.
Global Recognition
Worth ₹20,000 / $1,000 in academic value.
View All Feedbacks →