A biologically inspired approach to tracking control of underactuated surface vessels subject to unknown dynamics
The tracking control problem of underactuated surface vessels is studied. A biologically inspired approach is proposed using backstepping, neurodynamics model and NN. The control algorithm is efficient as no time derivatives of virtual controls are needed. The NN learning algorithm derived from Lyapunov theory is computationally efficient. The control performance is shown to be faster and better than other approaches.
tracking control surface vessel robotics bio-inspired neural dynamics neural network