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Machine learning based state observer for discrete time systems evolving on Lie groups
Conventional techniques involving machine learning based observers on systems evolving on Lie groups involve designing charts for the Lie group, training a machine learning based observer for each chart, and switching between the trained models based on the state of the system.
Transversally exponentially stable Euclidean space extension technique for discrete time systems
We propose a modification technique for discrete time systems for exponentially fast convergence to compact sets.
Angular velocity and linear acceleration measurement bias estimators for the rigid body system with global exponential convergence
Rigid body systems usually consider measurements of the pose of the body using onboard cameras/LiDAR systems, that of linear acceleration using an accelerometer and of angular velocity using an IMU.
Globally exponentially convergent observer for systems evolving on matrix Lie groups
We propose a globally exponentially convergent observer for the dynamical system evolving on matrix Lie groups with bounded velocity with unknown bound.
