Motion Control of Autonomous Robotic Vehicles

Synopsis

This course addresses the modeling, analysis, and control of autonomous robotic vehicles using modern nonlinear control theory. It covers both fundamental and advanced topics, with emphasis on Lyapunov‑based analysis, robust and safety‑critical control design, cooperative motion control, and practical implementation aspects. Theoretical concepts are systematically illustrated through examples drawn from ground robots, marine vehicles, and unmanned aerial vehicles (UAVs).

Teaching is organized around a balanced combination of theoretical exposition and practical classes. Alongside lectures, students will take part in hands‑on sessions using Python and Google Colab, where control algorithms are explored through interactive simulations. These practical activities are designed to bridge theory and practice, develop intuition, validate theoretical results, and provide experience with tools commonly used in research and advanced engineering applications.

Program

1. Models of Autonomous Vehicles

Overview of representative dynamical and kinematic models used across robotic platforms:

• Ground robots (unicycle‑type kinematics)
• Autonomous Surface Craft (ASC) and marine vessels
• Autonomous Underwater Vehicles (AUVs)
• Fixed‑wing and rotary‑wing Unmanned Aerial Vehicles (UAVs)

2. Nonlinear Control Concepts

Key tools for analyzing and designing stabilizing and safety‑critical control laws:

• Lyapunov stability and Input‑to‑State Stability (ISS)
• Control Lyapunov Functions (CLFs)
• Control Barrier Functions (CBFs) for safety and constraint enforcement
• Cascaded and interconnected nonlinear system analysis
• Nonlinear control design methods, including sliding‑mode and Lyapunov‑based approaches

3. Motion Control for Single Vehicles

Core techniques for stabilizing and guiding individual autonomous vehicles:

• Point‑stabilization and way‑point tracking
• Trajectory‑tracking
• Path‑following
• Moving Path‑Following (MPF) for paths attached to moving reference frames

4. Cooperative Control of Multiple Vehicles

Foundations of coordinated motion among vehicle teams:

• Cooperative path‑following and formation maintenance
• Time-critical cooperative path following
• Synchronization of path‑parameter evolution via consensus‑style mechanisms
• Dealing with communication delays, losses, and intermittent connectivity

5. From Theory to Practice

Practical considerations for implementing motion control algorithms on real robotic systems:

• Inner‑loop and outer‑loop control architectures
• Navigation systems and sensor fusion, and state estimation
• Communication constraints and logic‑based update strategies
• Demonstrations through simulation studies and field experiments involving AUV formations and UAV target‑tracking missions