Peng Xingguang

Progress of theoretical modelling and empirical studies on collective motion

The study on the collective motion of the biological systems, such as flocks of birds and schools of fish, has attracted the attention of researchers from diverse disciplines whilst the relevant studies are still emerging. Here, we review the state-of-the-art of collective motion on two prospects, the theoretical modelling and the empirical analysis. In particular, the latest developments of the individual-based modelling approach for the collective motion are summarized. Meanwhile, the new phenomena observed from the empirical analysis of the biological swarm system is sorted and analyzed. Finally, a brief discussion is provided to outline the open problems and the possible research directions in collective motion.

Global approximation based adaptive RBF neural network control for supercavitating vehicles

A global approximation based adaptive radial basis function (RBF) neural network control strategy is proposed for the trajectory tracking control of supercavitating vehicles (SV). A nominal model is built firstly with the unknown disturbance. Next, the control scheme is established consisting of a computed torque controller (CTC) for the practical vehicle and an RBF neural network controller to estimate model error between the practical vehicle and the nominal model. The network weights are adapted by employing a Lyapunov-based design. Then it is shown by the Lyapunov theory that the trajectory tracking errors asymptotically converge to a small neighborhood of zero. The control performance of the proposed controller is illustrated by simulation.