Description of the positionMulti-agent robotic systems often operate in complex and uncertain environments and under various constraints, while accurate acquisition of local information (such as distances or relative positions) in spatially distributed agents is essential to guarantee successful implementation of cooperative global missions.
The project aims to develop a resilient and robust networked coordination framework for multi-robotic systems that are able to adress local system conflict and uncertain sensor information, and to improve decision-making capability with guaranteed stability, optimality and intelligence. The objective of this PhD research is to exploit advanced control techniques (distributed adaptive control and filtering, robust control, distributed learning-based control) that improve resilience and security in networked multi-robot systems.
The PhD position is associated with support from the EAISI (Eindhoven Artificial Intelligence Systems Institute) research initiative. The project will contribute to one of EAISI's key missions: 'to develop AI-technology for real-time autonomous decision-making in engineering systems', and address one key challenge: 'decision-making under uncertainty in complex engineering systems'.
The Control System (CS) group features Autonomous Motion Control (AMC) Lab which consists of multiple UAVs, ground vehicles, and VICON facilities. The AMC Lab provides an ideal testbed for testing and implementation of novel resilient and secure coordination in multi-vehicle systems from this project.
Main research directions
- Identify and model key sources (measurement data, communication venues, etc.) that directly affect the performance degradation of multi-agent coordination.
- Design secure distributed control laws (e.g., networked adaptive control, disturbance rejection control) that are robust to system imperfections and uncertain environment.
- Develop resilient coordination control law with learning-based approach (e.g., scalable Gaussian-Process based learning) that improves distributed decision-making capability with uncertain information and stochastic perturbations.
- Validation in the AMC Lab with the multi-robot system testbed.
Control Systems groupThe CS group research activities span all facets of systems and control theory, such as linear, nonlinear and hybrid systems theory, model predictive control, distributed control, networked system, machine learning for control, modeling and identification, formal methods in control. The CS group has a strong interconnection with industry via national and European funded projects in a variety of application areas like high-precision mechatronics, power electronics, and sustainable energy (mobility, transport, smart grids). CS owns an Autonomous Motion Control (AMC) laboratory and hosts several high-tech setups. The PhD student will join the group and interact with the other members of the CS group (around 40 researchers), where he/she will participate in a mix of academic and industrial research activities. Research within the CS Group is characterized by personal supervision. The PhD student will have access to the advanced courses offered by the Dutch Institute for Systems and Control, and will attend the yearly Benelux Meeting on Systems and Control.