Intelligent Cybernetics Group

ICG's Recent Projects:

Develop a GAT-based formation controller that can handle communication loss
Controller outperforms traditional formation controllers under both no-attack and DoS attack scenarios .
real-world flight tests with zero-shot Sim2Real

Address the robust formation control of MASs with nonlinear dynamics, external disturbances, and FDI attacks
Develope a GAN-based controller to manage total uncertainties and facilitate linear dynamics.
Demonstrate effectiveness against mode collapse and instability issues typically problematic in GANs training.

Modular Design – Transforms the parcel into the drone airframe using plug-and-fly propeller units with integrated motors, batteries, and controllers for rapid, scalable assembly.
Energy-Efficient Descent – Recovers energy during free-fall through a custom wind-harvesting circuit.
Decentralized Intelligence – Employs a learning-based control system for robust, autonomous coordination across varying payload shapes, sizes, and centers of gravity.

eVTOL Flight Control Development

Adaptive Flight Control – Custom algorithms ensure seamless transitions between vertical hover and forward cruise, delivering smooth, stable flight in Singapore’s urban airspace.
Fault-Tolerant Architecture – Redundant sensing and real-time health monitoring keep the aircraft controllable under component failures, meeting stringent safety standards for passenger operations.
High-Fidelity Digital Twin – Hardware-in-the-loop and full 6-DoF simulation accelerate controller tuning, safety validation, and certification readiness for Singapore’s eVTOL routes.

Reimagining space situational awareness with onboard AI

Satellite-Integrated Vision – Multi-view cameras and AI detection on smart microsatellites track even tiny debris over a broad area, removing reliance on ground telescopes and radars.
Edge-AI Orbit Analytics – Lightweight neural models running on minimal onboard power deliver real-time orbit determination and propagation with high precision, sharply reducing bandwidth use and ground-link delays.
Scalable, Site-Free Solution – Readily deployable on single satellites or constellations, the system offers a patent-ready, cost-effective SSA capability ideal for land-scarce nations.

AI-Powered Morphing Hummingbird Drone

Bio-Inspired Morphing Airframe – Flexible wings and coordinated wing-tail morphing replicate hummingbird mechanics, enabling silent hovering and rapid manoeuvres in cluttered environments.
Physics-Informed AI Modelling – A learning based framework captures unsteady aerodynamics and wing-tail interactions, delivering high-fidelity dynamics without large labelled datasets.
Agile, Wind-Resilient Control – Gain-scheduled controllers derived from the learned model provide stable hover and swift attitude changes, advancing drone capabilities for sensitive biodiversity missions.

Drone Swarm Navigation in GNSS-Denied Environments

Localization&Perception – Each drone fuses LiDAR, vision, IMU and UWB data to self-localise and map in real time even when GPS is unavailable.
Safe-Agility Planner – A hierarchical planner couples deep-RL–driven agility limits with provably safe convex optimisation, producing smooth, collision-free paths that adapt speed and aggressiveness to local obstacle density on the fly.
Rapid Morphing Swarm Formation – An event-triggered formation system treats the swarm as one deformable body, recalculating its geometry in milliseconds so the group can compress, expand or rotate to slip through tight gaps while maintaining cohesion.

Prof. Saeid Nahavandi (FIEEE) : Vice Chancellor, Swinburne University of Technology, Australia.
Prof. Peng Shi (FIEEE) : Professor, The University of Adelaide, Australia.
Prof. Sreenatha G Anavatti (SMIEEE) : Senior Lecturer, University of New South Whales, Australia.
Prof. Xie Lihua (FIEEE) : Professor, Nanyang Technological University, Singapore.

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