[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Zhou et al., 2022 - Google Patents

Trajectory tracking control for autonomous underwater vehicles under quantized state feedback and ocean disturbances

Zhou et al., 2022

Document ID
8351897056232631318
Author
Zhou B
Su Y
Huang B
Wang W
Zhang E
Publication year
Publication venue
Ocean Engineering

External Links

Snippet

For the autonomous underwater vehicle (AUV) platform, state quantization is a widely existing phenomenon as the result of I/O signal conversion between hardware module and control module. This article considers such an adaptive trajectory tracking control problem …
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
    • G05B13/042—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 in which a parameter or coefficient is automatically adjusted to optimise the performance
    • 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
    • G05B11/00—Automatic controllers
    • G05B11/01—Automatic controllers electric
    • G05B11/32—Automatic controllers electric with inputs from more than one sensing element; with outputs to more than one correcting element
    • 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
    • G05D1/08—Control of attitude, i.e. control of roll, pitch, or yaw
    • G05D1/0808—Control of attitude, i.e. control of roll, pitch, or yaw specially adapted for aircraft
    • G05D1/0816—Control of attitude, i.e. control of roll, pitch, or yaw specially adapted for aircraft to ensure stability
    • 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
Deng et al. Model-based event-triggered tracking control of underactuated surface vessels with minimum learning parameters
Zhang et al. Fixed-time extended state observer-based trajectory tracking and point stabilization control for marine surface vessels with uncertainties and disturbances
Xiang et al. Robust fuzzy 3D path following for autonomous underwater vehicle subject to uncertainties
Huang et al. Robust practical fixed-time leader–follower formation control for underactuated autonomous surface vessels using event-triggered mechanism
Zhou et al. Trajectory tracking control for autonomous underwater vehicles under quantized state feedback and ocean disturbances
An et al. Robust fixed-time tracking control for underactuated AUVs based on fixed-time disturbance observer
Yan et al. Barrier function-based adaptive neural network sliding mode control of autonomous surface vehicles
Feng et al. Predictive compensator based event-triggered model predictive control with nonlinear disturbance observer for unmanned surface vehicle under cyber-attacks
Hu et al. Nussbaum-based fuzzy adaptive nonlinear fault-tolerant control for hypersonic vehicles with diverse actuator faults
Zhang et al. Neuro-adaptive trajectory tracking control of underactuated autonomous surface vehicles with high-gain observer
Esfahani et al. High performance super-twisting sliding mode control for a maritime autonomous surface ship (MASS) using ADP-based adaptive gains and time delay estimation
Wen et al. Robust formation tracking of multiple autonomous surface vessels with individual objectives: A noncooperative game-based approach
Yang et al. A recurrent neural network based fuzzy sliding mode control for 4-DOF ROV movements
Jiang et al. Neural network based adaptive sliding mode tracking control of autonomous surface vehicles with input quantization and saturation
Zhang et al. Disturbance observer-based composite neural learning path following control of underactuated ships subject to input saturation
Gong et al. Trajectory tracking control for autonomous underwater vehicles based on dual closed-loop of MPC with uncertain dynamics
Zhang et al. Disturbance observer-based prescribed performance super-twisting sliding mode control for autonomous surface vessels
Shi et al. Adaptive robust dynamic surface asymptotic tracking for uncertain strict-feedback nonlinear systems with unknown control direction
Wang et al. Adaptive fuzzy control of underwater vehicle manipulator system with dead-zone band input nonlinearities via fuzzy performance and disturbance observers
Du et al. A novel adaptive backstepping sliding mode control for a lightweight autonomous underwater vehicle with input saturation
Li et al. Saturated-command-deviation based finite-time adaptive control for dynamic positioning of USV with prescribed performance
Gao et al. Command filtered path tracking control of saturated ASVs based on time‐varying disturbance observer
Zhang et al. Robust adaptive fault-tolerant control for unmanned surface vehicle via the multiplied event-triggered mechanism
Shi et al. Composite finite-time adaptive anti-disturbance control for dynamic positioning of vessels with output constraints
Souissi et al. Time-varying nonsingular terminal sliding mode control of autonomous surface vehicle with predefined convergence time