Intelligent adaptive control for MIMO uncertain nonlinear systems
Expert Systems with Applications: An International Journal
Ontology-based fuzzy support agent for ship steering control
Expert Systems with Applications: An International Journal
Path-following algorithms and experiments for an unmanned surface vehicle
Journal of Field Robotics
Expert Systems with Applications: An International Journal
Optimisation of autonomous ship manoeuvres applying Ant Colony Optimisation metaheuristic
Expert Systems with Applications: An International Journal
Brief Global tracking control of underactuated ships by Lyapunov's direct method
Automatica (Journal of IFAC)
Global κ-exponential way-point maneuvering of ships: Theory and experiments
Automatica (Journal of IFAC)
Hi-index | 12.05 |
This paper proposes an efficient neural network (NN) approach to tracking control of an autonomous surface vehicle (ASV) with completely unknown vehicle dynamics and subject to significant uncertainties. The proposed NN has a single-layer structure by utilising the vehicle regressor dynamics that expresses the highly nonlinear dynamics in terms of the known and unknown dynamic parameters. The learning algorithm of the NN is simple yet computationally efficient. It is derived from Lyapunov stability analysis, which guarantees that all the error signals in the control system are uniformly ultimately bounded (UUB). The proposed NN approach can force the ASV to track the desired trajectory with good control performance through the on-line learning of the NN without any off-line learning procedures. In addition, the proposed controller is capable of compensating bounded unknown disturbances. The effectiveness and efficiency are demonstrated by simulation and comparison studies.