This project investigates adaptive and robust control strategies for single robotic systems, with a particular focus on six degrees of freedom (6-DOF) unmanned aerial vehicles (UAVs). The research addresses the challenge of maintaining stable and precise trajectory tracking in the presence of unknown and time-varying system parameters, including inertia, actuator characteristics, and variable payloads. By combining virtual proportional-derivative (PD) controllers with adaptive schemes, the project ensures that translational and rotational dynamics are accurately controlled, even under significant parametric uncertainty and external disturbances.

A central component of the work is the development and comparison of multiple adaptive control schemes, including classical adaptive controllers based on the certainty equivalence principle and enhanced adaptive methods incorporating continuous robust terms. These schemes are designed to estimate unknown parameters in real-time and adjust control inputs dynamically, ensuring that the UAV maintains stability and accurate tracking performance. The robustness of the controllers is further enhanced to compensate for perturbations caused by unmodeled dynamics, time-varying parameters, and environmental disturbances, providing resilience in realistic operational conditions.

The project combines rigorous theoretical analysis with simulation-based validation, demonstrating that the proposed controllers outperform existing adaptive and sliding mode approaches in terms of tracking accuracy, stability, and control efficiency. These results provide a general framework for adaptive and robust control of under-actuated robotic systems, with potential applications beyond UAVs to other autonomous aerial, ground, or marine robots that operate under uncertain and dynamic conditions.

RELATED PUBLICATIONS

This project investigates robust formation control strategies for cooperative unmanned aerial vehicles (UAVs), enabling multiple UAVs to operate in dynamic and uncertain environments with minimal human intervention. As UAV autonomy increases, traditional control systems designed for static or predictable conditions are insufficient to handle environmental uncertainties, disturbances, and interactions with other intelligent agents. The project addresses these challenges by developing control architectures that maintain stable and coordinated flight, even when UAVs have heterogeneous dynamics, limited communication, or time-varying disturbances.

A key focus of the research is leader-follower and consensus-based formation control, where UAVs synchronize their motion to a virtual leader or to each other using distributed and centralized control protocols. Advanced techniques, including discrete-time sliding mode control, robust distributed schemes, and nonlinear consensus controllers, are employed to ensure asymptotic consensus and stability across the formation. Communication topologies are rigorously defined using graph theory, allowing UAVs to maintain coordination even when only some agents are connected to the leader, or when communication is limited or directional.

The project combines theoretical analysis, rigorous mathematical proofs, and numerical simulation to validate the performance of the proposed control strategies. Simulations in MATLAB and Simulink demonstrate the effectiveness of the controllers in maintaining formation, handling disturbances, and ensuring robustness against parametric uncertainty and environmental variability. The outcomes provide a foundation for enabling multi-UAV cooperation in complex real-world tasks, such as surveillance, search and rescue, environmental monitoring, and coordinated inspection missions, where precise formation and reliable operation are critical.

RELATED PUBLICATIONS

RELATED PUBLICATIONS