Event-Triggered Adaptive Control (ETAC) for Robotic Navigation

This project implements a robust Event-Triggered Adaptive Control system in MATLAB. It is designed to simulate a robotic system navigating a trajectory while minimizing communication bandwidth between sensors and the controller without sacrificing tracking performance.

Overview

Traditional control systems are "Time-Triggered," meaning they send data at fixed intervals (e.g., every 0.01s). In contrast, this Event-Triggered approach only transmits data when a specific error threshold is exceeded. This is particularly useful for remote robotics or IoT devices where saving battery and network bandwidth is critical.

Key Features

• Adaptive Law: Estimates unknown system parameters (Mass, Friction, and External Disturbances) in real-time.
• Event-Triggering Condition: Uses a relative threshold ($\sigma |x|^2 + \delta$) to decide when to broadcast state updates.
• Hybrid Simulation: Solves the common ode45 integration tolerance errors by using a segmented hybrid loop.
• Stability Enhancements: Implements Parameter Projection (clamping) and a Dead-Zone to prevent numerical singularities and "Zeno" behavior.

Project Structure

• MRS_Final_Stable.m: The main entry point. Handles the hybrid simulation loop and manages the event-triggering logic.
• dynamics_final.m: Contains the physical system dynamics and the adaptive control laws.
• analyze.m: Independent script to calculate and display Communication Savings and performance metrics.

Performance & Analysis

The system generates a Communication Report comparing the ETAC behavior against a hypothetical 100Hz fixed-rate sensor.

• Tracking: Maintains high precision on a linear trajectory.
• Efficiency: Typically achieves 80-90% communication savings depending on the sensitivity parameters ($\sigma, \delta$).

How to Run

1. Ensure both MRS_Final_Stable.m and dynamics_final.m are in the same MATLAB directory.
2. Run MRS_Final_Stable.m.
3. View the Command Window for the Communication Savings report and the generated plots for trajectory tracking and trigger instances.

Requirements

• MATLAB (R2021a or later recommended)
• No additional toolboxes required.