Real-time motion control system with PID control, sensor feedback, forward kinematics, and performance metrics.
RoboControl is a simulation of a real-time robot motion control system. It models how control signals are generated and applied to drive a robot toward a target position using a PID controller. The system incorporates sensor feedback with noise, simulates motion dynamics, and computes forward kinematics for a multi-joint robotic arm.
This project demonstrates core concepts used in robotics, control systems, and real-time software.
- Real-time control loop running at fixed frequency (50 Hz)
- PID-based motion control (proportional, integral, derivative terms)
- Simulated sensor feedback with noise
- Forward kinematics for a 3-link robotic arm
- Structured logging for system state tracking
- Metrics for evaluating control performance
Target Position → PID Controller → Control Signal → Motion Simulation
↓
Sensor Feedback
↓
Error Computation
↓
Metrics & Logging
The PID controller computes a control signal based on:
- Current error (target − sensed position)
- Accumulated past error
- Rate of change of error
This allows the system to minimize error while maintaining stability.
The system updates position and velocity using a simplified physics model:
- velocity is adjusted using the control signal
- position is updated based on velocity
- random noise is added to simulate real-world uncertainty
A noisy sensor reading is generated from the true position to mimic real hardware sensors. The controller uses this noisy signal instead of the actual state.
The system computes the position of a 3-link robotic arm using joint angles and basic trigonometry, producing the end-effector position.
The system tracks:
- average control error
- average control signal
Logs are printed with timestamps for debugging and analysis.
python3 main.py
[12:45:10] Starting RoboControl simulation...
[12:45:10] Step 0: target=10.00, sensed=0.02, pos=0.01, vel=0.20, end_effector=(2.83, 1.34)
=== Metrics Summary ===
Average absolute error: 4.2315
Average control signal: 3.8821
[12:45:14] Simulation complete.
- Python
- Control Systems (PID)
- Linear Algebra and Kinematics
- Real-time Simulation
- Designing stable control loops using PID
- Handling noisy sensor feedback
- Simulating motion dynamics
- Computing forward kinematics for robotic systems
- Measuring system performance using metrics
- Visualization of robot motion using matplotlib
- Extension to multi-axis (2D/3D) systems
- Trajectory planning instead of static targets
- Dynamic PID tuning
- Hardware integration with real sensors
Ancy Patel