Neural Network Identification-Based Model Predictive Heading Control for Wave Gliders

Neural Network Identification-Based Model Predictive Heading Control for Wave Gliders

Blog Article

This paper deals with the neural network identification-based model predictive heading control problem in a wave glider.First, based on a kinematic model of the wave glider subjected to external disturbance and system uncertainty, a state space model of the wave glider is established.Then, a neural network identification-based model predictive heading controller (NNI-MPHC) probios intelliflora for dogs is designed for the wave glider.

The heading controller mainly includes three components: a model predictive controller, a neural network-based model identifier, and a linear reduced-order extended state observer.Third, a design algorithm hlpro4sw of the NNI-MPHC is presented.The algorithm is demonstrated through simulation, where the results show the following: (i) The designed NNI-MPHC is remarkably capable of guaranteeing the tracing effects of the wave glider.

(ii) Comparing the NNI-MPHC and existing heading controllers, the former is better than the latter in terms of tracking accuracy and rapidity and robustness to model uncertainty and/or external disturbances.