We focus on the complete cycle of an automation project, from initial concept and mathematical modeling to physical deployment and testing.
- Modeling and Simulation: Creating mathematical models of dynamic systems (e.g., in MATLAB/Simulink or Python) to predict behavior.
- Controller Design: Designing and tuning control algorithms, such as PID (Proportional-Integral-Derivative), State-Space, and advanced techniques like Model Predictive Control (MPC).
[Image of a block diagram for a closed-loop control system]
- Hardware Implementation: Programming microcontrollers (Arduino, ESP32, Raspberry Pi) and PLCs for real-time control applications.
- Robotics: Working on mobile robotics, robotic manipulators, and developing kinematic/dynamic control systems.
- Industrial Automation: Exploring SCADA systems, HMI interfaces, sensors, actuators, and industrial communication protocols (e.g., Modbus, CAN).
- Data Acquisition & Analysis: Implementing systems to collect, process, and analyze system performance data for optimization.
| Category | Tools and Platforms | Description |
|---|---|---|
| Programming | Python, C/C++, Ladder Logic | Low-level hardware control and high-level data processing. |
| Software | MATLAB/Simulink, LabVIEW, ROS, Gazebo | System modeling, simulation, and complex robotics frameworks. |
| Hardware | Arduino, Raspberry Pi, ESP32, PLCs | Microcontrollers, Single-Board Computers, and Industrial Controllers. |
| Control Theory | PID, State-Space, Optimal Control | Core principles for stabilization and performance tuning. |
If you have a passion for making things move, optimizing processes, or just curious about how systems self-regulate, you're in the right place! Every member, regardless of their current skill level, has a valuable contribution to make. Join our community to learn, collaborate, and build the future of automated systems.