Electrical engineering

Physics

Control engineering

Trajectory

Tracking error

Mathematics

Agronomy

Biology

Underactuation

Gene

Adaptive control

Operating system

Astronomy

Diagonal

Engineering

Artificial intelligence

Controller (irrigation)

Control (management)

Chemistry

Transformation (genetics)

Geometry

Observer (physics)

Computer science

Transient (computer programming)

Quantum mechanics

Control theory (sociology)

Artificial neural network

Biochemistry

Transient response

IEEE Transactions on Industrial Electronics

In this paper, an adaptive trajectory tracking control algorithm for underactuated unmanned surface vessels (USVs) with guaranteed transient performance is proposed. To meet the realistic dynamical model of USVs, we consider that the mass and damping matrices are not diagonal and the input saturation problem. Neural networks (NNs) are employed to approximate the unknown external disturbances and uncertain hydrodynamics of USVs. Moreover, both full-state feedback control and output feedback control are presented, and the unmeasurable velocities of the output feedback controller are estimated via high-gain observer. Unlike the conventional control methods, we employ the error transformation function to guarantee the transient tracking performance. Both simulation and experimental results are carried out to validate the superior performance via comparing with traditional potential integral control approaches.

Adaptive Neural Network Control of Underactuated Surface Vessels With Guaranteed Transient Performance: Theory and Experimental Results