Nothing Special   »   [go: up one dir, main page]

Liang et al., 2021 - Google Patents

Swarm velocity guidance based distributed finite-time coordinated path-following for uncertain under-actuated autonomous surface vehicles

Liang et al., 2021

Document ID
3050906508162330927
Author
Liang X
Qu X
Wang N
Li Y
Publication year
Publication venue
ISA transactions

External Links

Snippet

This article mainly researches the problem of distributed finite-time coordinated path- following for under-actuated autonomous surface vehicles (ASVs) within a network swarm. Each vehicle in swarm system suffers from velocity restrictions and multiple uncertainties …
Continue reading at www.sciencedirect.com (other versions)

Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/50—Machine tool, machine tool null till machine tool work handling
    • G05B2219/50109—Soft approach, engage, retract, escape, withdraw path for tool to workpiece
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/0011—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot associated with a remote control arrangement
    • G05D1/0044—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot associated with a remote control arrangement by providing the operator with a computer generated representation of the environment of the vehicle, e.g. virtual reality, maps
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0287—Control of position or course in two dimensions specially adapted to land vehicles involving a plurality of land vehicles, e.g. fleet or convoy travelling
    • G05D1/0291—Fleet control
    • G05D1/0295—Fleet control by at least one leading vehicle of the fleet
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models

Similar Documents

Publication Publication Date Title
Liang et al. Distributed coordinated tracking control of multiple unmanned surface vehicles under complex marine environments
Wang et al. Fuzzy unknown observer-based robust adaptive path following control of underactuated surface vehicles subject to multiple unknowns
Zhang et al. Robust neural path-following control for underactuated ships with the DVS obstacles avoidance guidance
Shojaei et al. Line-of-sight target tracking control of underactuated autonomous underwater vehicles
Xiang et al. Survey on fuzzy-logic-based guidance and control of marine surface vehicles and underwater vehicles
Liang et al. Swarm velocity guidance based distributed finite-time coordinated path-following for uncertain under-actuated autonomous surface vehicles
Liang et al. A novel distributed and self-organized swarm control framework for underactuated unmanned marine vehicles
Liu et al. Coordinated path following of multiple underacutated marine surface vehicles along one curve
Qu et al. Finite-time sideslip observer-based synchronized path-following control of multiple unmanned underwater vehicles
Liu et al. Adaptive barrier Lyapunov function-based obstacle avoidance control for an autonomous underwater vehicle with multiple static and moving obstacles
Zhang et al. Improved composite learning path-following control for the underactuated cable-laying ship via the double layers logical guidance
Zhang et al. Three-dimensional formation–containment control of underactuated AUVs with heterogeneous uncertain dynamics and system constraints
Zhang et al. Disturbance observer-based composite neural learning path following control of underactuated ships subject to input saturation
Ghommam et al. Prescribed performances based fuzzy-adaptive output feedback containment control for multiple underactuated surface vessels
Wen et al. Characteristic model-based path following controller design for the unmanned surface vessel
Qu et al. Fuzzy state observer-based cooperative path-following control of autonomous underwater vehicles with unknown dynamics and ocean disturbances
He et al. Robust orientation-sensitive trajectory tracking of underactuated autonomous underwater vehicles
Zhang et al. Event-triggered robust neural control for unmanned sail-assisted vehicles subject to actuator failures
Gao et al. Command filtered path tracking control of saturated ASVs based on time‐varying disturbance observer
Rani et al. A neural network based efficient leader–follower formation control approach for multiple autonomous underwater vehicles
Lv et al. GVF-based guidance and super-twisting control of autonomous surface vehicle for target tracking in obstacle environments with experiments
Sun et al. An innovative distributed self-organizing control of unmanned surface vehicle swarm with collision avoidance
Xie et al. Three-dimensional mobile docking control method of an underactuated autonomous underwater vehicle
Jia et al. Distributed observer-based finite-time control of moving target tracking for UAV formation
Li et al. Formation control of a group of AUVs using adaptive high order sliding mode controller