Huang et al., 2021 - Google Patents
Robust practical fixed-time leader–follower formation control for underactuated autonomous surface vessels using event-triggered mechanismHuang et al., 2021
- Document ID
- 6734425054894919633
- Author
- Huang C
- Zhang X
- Zhang G
- Deng Y
- Publication year
- Publication venue
- Ocean Engineering
External Links
Snippet
This paper investigates a practical fixed-time formation control strategy for underactuated autonomous surface vessels with unknown dynamics based on event-triggered mechanism. To deal with the leader–follower configuration without the information of leader velocity, the …
- 230000015572 biosynthetic process 0 title abstract description 48
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
- 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
- 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
- G05D1/0883—Control of attitude, i.e. control of roll, pitch, or yaw specially adapted for space vehicles
-
- 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
- G05D1/02—Control of position or course in two dimensions
- G05D1/0206—Control of position or course in two dimensions specially adapted to water vehicles
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Huang et al. | Robust practical fixed-time leader–follower formation control for underactuated autonomous surface vessels using event-triggered mechanism | |
Zhang et al. | Composite neural learning fault-tolerant control for underactuated vehicles with event-triggered input | |
Song et al. | 1 bit encoding–decoding-based event-triggered fixed-time adaptive control for unmanned surface vehicle with guaranteed tracking performance | |
Liu et al. | Incremental predictive control-based output consensus of networked unmanned surface vehicle formation systems | |
Shou et al. | Virtual guidance-based coordinated tracking control of multi-autonomous underwater vehicles using composite neural learning | |
Zhou et al. | Interleaved periodic event-triggered communications-based distributed formation control for cooperative unmanned surface vessels | |
Zhang et al. | Adaptive neural fault-tolerant control for USV with the output-based triggering approach | |
Zhang et al. | Disturbance observer-based composite neural learning path following control of underactuated ships subject to input saturation | |
Sun et al. | Adaptive trajectory tracking control of vector propulsion unmanned surface vehicle with disturbances and input saturation | |
Wu et al. | Output-feedback finite-time safety-critical coordinated control of path-guided marine surface vehicles based on neurodynamic optimization | |
CN114967714B (en) | Autonomous underwater robot anti-interference motion control method and system | |
Jiang et al. | Neural network based adaptive sliding mode tracking control of autonomous surface vehicles with input quantization and saturation | |
Lv et al. | Event-triggered neural network control of autonomous surface vehicles over wireless network | |
Zhao et al. | Rotation matrix-based finite-time attitude synchronization control for flexible spacecrafts with unknown inertial parameters and actuator faults | |
Duan et al. | Distributed Robust Learning Control for Multiple Unmanned Surface Vessels With Fixed-Time Prescribed Performance | |
Zhang et al. | Robust adaptive fault-tolerant control for unmanned surface vehicle via the multiplied event-triggered mechanism | |
Zhou et al. | Trajectory tracking control for autonomous underwater vehicles under quantized state feedback and ocean disturbances | |
Xu et al. | Anti-disturbance fault-tolerant formation containment control for multiple autonomous underwater vehicles with actuator faults | |
Gao et al. | Command filtered path tracking control of saturated ASVs based on time‐varying disturbance observer | |
Wang et al. | Distributed finite-time velocity-free robust formation control of multiple underactuated AUVs under switching directed topologies | |
Zhang et al. | DVSL guidance-based composite neural path following control for underactuated cable-laying vessels using event-triggered inputs | |
Zhang et al. | Improved event-triggered robust adaptive control for marine vehicle with the fault compensating mechanism | |
Gong et al. | Three-dimensional optimal trajectory tracking control of underactuated AUVs with uncertain dynamics and input saturation | |
Wu et al. | Transient-reinforced tunnel coordinated control of underactuated marine surface vehicles with actuator faults | |
Wang et al. | Command filter-based adaptive practical prescribed-time asymptotic tracking control of autonomous underwater vehicles with limited communication angles |