Finite-time predictor line-of-sight–based adaptive neural network path following for unmanned surface vessels with unknown dynamics and input saturation

Abstract

In the presence of unknown dynamics and input saturation, a finite-time predictor line-of-sight-based adaptive neural network scheme is presented for the path following of unmanned surface vessels. The proposed scheme merges with the guidance and the control subsystem of unmanned surface vessels together. A finite-time predictor-based line-of-sight guidance law is developed to ensure unmanned surface vessels effectively converging to and following the referenced path. Then, the pathfollowing control laws are designed by combining neural network-based minimal learning parameter technique with backstepping method, where minimal learning parameter is applied to account for system nonparametric uncertainties. The key features of this scheme, first, the finite-time predictor errors are guaranteed; second, designed controllers are independent of the system model; and third, only required two parameters update online for each control law. The rigorous theory analysis verifies that all signals in the path-following guidance-control system are semi-globally uniformly ultimately bounded via Lyapunov stability theory. Simulation results illustrate the effectiveness and performance of the presented scheme.

Filtered Extended State Observer Based Line-of-Sight Guidance for Path Following of Unmanned Surface Vehicles With Unknown Dynamics and Disturbances

“…where ε is positive, p > 1, q > 1, the condition (p − 1)(q − 1) = 1 must be satisfied. Lemma 2 [39]: The control input τ i (i = u, r) of USV are limited by physical constraint and it satisfied −τ i max ≤ τ i ≤ τ i max , so τ i is defined by the following function of the command τ i0 ,…”

Extended state observer-based integral line-of-sight guidance law for path following of underactuated unmanned surface vehicles with uncertainties and ocean currents

“…The control of the underactuated system is always realized using motion coupling or dynamic coupling. [23][24][25][26] In a controllable system, an effective control technique is backstepping control, which has been widely adopted given its systematic calculated amount. 17,26,27 The backstepping technique can eliminate the constraint that the relative degree must be 1 in classical passive systems.…”

Path following of underactuated surface ships based on model predictive control with neural network

“…In the study of Shen and Jing, 22 both neuron adaptive and iterative sliding mode strategy were developed for the path tracking of underactuated ships. In the study of Yu et al, 23 the input saturation was considered via an auxiliary system and the MLP was provided to approximate the unknown dynamic without external disturbances.…”

“…The MMG model is used to improve the prediction accuracy as the internal model. Moreover, with reference to the literature, 19,23,25,28 a RBF NN is used to compensate the uncertainties for the improvement of the internal model accuracy and the controller robustness.…”