Gabriel Da Silva Lima

The choice of activation function in RBF networks can dramatically alter the adaptation dynamics and tracking performance of robotic controllers, even when stability is preserved.

Real-time adaptation of a neural network controller leads to over 49% improvement in yaw orientation tracking for quadrotors in unpredictable environments.

Adaptive neural network control plus multi-sensor fusion yields significantly improved trajectory tracking for differential drive robots in the face of unmodeled dynamics and sensor noise.