Output-Feedback Path-Following Control of Autonomous Underwater Vehicles Based on an Extended State Observer and Projection Neural Networks

• Published in: IEEE transactions on systems, man, and cybernetics. Systems Vol. 48; no. 4; pp. 535 - 544
• Main Authors: Peng, Zhouhua, Wang, Jun
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Autonomous underwater vehicles; Computer simulation; Control systems; Control theory; Error signals; extended state observer (ESO); Feedback control; Governors; Guidance (motion); Kinetic theory; Marine vehicles; Neural networks; Observers; Optimization; Output feedback; path-following; projection neural networks; Quadratic programming; Recurrent neural networks; Reference signals; State observers; Surges; Uncertainty; Vehicle dynamics
• ISSN: 2168-2216; 2168-2232
• DOI: 10.1109/TSMC.2017.2697447

This paper presents a design method for output-feedback path-following control of under-actuated autonomous underwater vehicles moving in a vertical plane without using surge, heave, and pitch velocities. Specifically, an extended state observer (ESO) is developed to recover the unmeasured velocities as well as to estimate total uncertainty induced by internal model uncertainty and external disturbance. At the kinematic level, a commanded guidance law is developed based on a vertical line-of-sight guidance scheme and the observed velocities. To optimize guidance signals, optimization-based reference governors are formulated as bound-constrained quadratic programming problems for computing optimal reference signals. Two globally convergent recurrent neural networks called projection neural networks are used to solve the optimization problems in real-time. Based on the optimal reference signals and ESO, a kinetic control law with disturbance rejection capability is constructed at the kinetic level. It is proved that all error signals in the closed-loop system are uniformly and ultimately bounded. Simulation results substantiate the efficacy of the proposed method for output-feedback path-following of under-actuated autonomous underwater vehicles.