A Review of Current Research and Advances in Unmanned Surface Vehicles

A predictive type-3 fuzzy control for underactuated surface vehicles

Predictive compensator based event-triggered model predictive control with nonlinear disturbance observer for unmanned surface vehicle under cyber-attacks

Distributed formation control of underactuated ships with connectivity preservation and collision avoidance

Fixed-time composite robust H∞ tracking control of marine surface vessels based on the barrier Lyapunov function and an event-triggered strategy

Extended-state-observer-based distributed model predictive formation control of under-actuated unmanned surface vehicles with collision avoidance

This article addresses the distributed formation control of multiple under-actuated autonomous surface vehicles (ASVs) in a receding-horizon setting. The ASVs are subject to physical constraints, in addition to stationary and moving obstacles. A barrier-certified distributed model predictive control method is proposed with the capability of avoiding collision with stationary and moving obstacles and neighboring ASVs. Specifically, a data-driven neural predictor is used to learn unknown functions in ASV kinetics. A nominal distributed receding-horizon position control law is developed based on the learned unknown function to achieve the desired formation within physical constraints. To ensure the safety requirement, a barrier-certified control law is designed based on control barrier functions to generate the signals of optimal surge force and heading angle within the safety constraints. A receding-horizon heading control law is designed based on the data-driven neural predictor to track the desired heading signals. Constrained quadratic programming problems are formulated based on barrier functions for barrier-certified distributed formation control and solved via neurodynamic optimization using one-layer recurrent neural networks. Thus, the proposed control method is able to ensure obstacle avoidance in the formation control of multiple ASVs in the presence of stationary and moving obstacles. Simulation results are elaborated to validate the efficacy of the proposed barrier-certified distributed model predictive control method for ASV formation.

Barrier-Certified Distributed Model Predictive Control of Under-Actuated Autonomous Surface Vehicles via Neurodynamic Optimization

This paper investigates the safe cooperative path following of connected autonomous surface vehicles subject to static and dynamic obstacles, as well as physical constraints. A barrier-certified model predictive cooperative path following control method is proposed with the capability of collision avoidance and constraint satisfaction. Specifically, a robust-exact-differentiators-based extended state observer is employed to estimate the unknown kinetics including model uncertainties and external disturbances. Based on a path variable containment approach, a nominal receding-horizon control law is designed to achieve cooperative path following task within the physical constraints. A safe optimal control law is designed based on control barrier functions to generate optimal surge force and heading angle within the safety constraints. A receding-horizon heading control law is designed to track the desired heading signals. Constrained quadratic programming problems are formulated and solved via neurodynamic optimization. Simulation results are elaborated to validate the efficacy of the proposed barrier-certified model predictive control method for cooperative path following subject to static and dynamic obstacles. 

barrier-Certified Model Predictive Cooperative Path Following Control of Connected Autonomous Surface Vehicles

This paper is concerned with maximum power tracking of variable-speed wind energy conversion systems under low wind speed. First, by introducing a prescribed performance function and an error transformation technique, a maximum power tracking method with prescribed performance is proposed, such that the steady state and transient performance of the system can be analyzed quantitatively. Meanwhile, an extended state observer is utilized for real-time estimation of system uncertainties including aerodynamic torque and disturbances. Then, the cascade system theory and input-to-state stability are used to analyze the stability of the closed-loop system. Finally, simulation results demonstrate the feasibility of the proposed maximum power tracking controller based on prescribed performance function and extended state observer.

Guanghao Lv

Papers

Extended-state-observer-based distributed model predictive formation control of under-actuated unmanned surface vehicles with collision avoidance

Barrier-Certified Distributed Model Predictive Control of Under-Actuated Autonomous Surface Vehicles via Neurodynamic Optimization

barrier-Certified Model Predictive Cooperative Path Following Control of Connected Autonomous Surface Vehicles

Maximum Power Tracking Control of Wind Energy Conversion Systems Based on Prescribed Performance Function and Extended State Observer