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The collision-free straight-line following of an unmanned surface vehicle (USV) moving in a constrained water region subject to stationary and moving obstacles is addressed in this paper. USV systems are normally subjected to surge velocity constraints, yaw rate constraints, and unknown ocean currents. Herein, a safety-certificated line-of-sight (LOS) guidance method is proposed to achieve a constrained straight-line following task. First, an antidisturbance LOS guidance law is designed based on the LOS guidance scheme and an extended state observer. Furthermore, collision avoidance with waterway boundaries and stationary/moving obstacles is encoded in control barrier functions, utilizing which the safety constraints are transformed into input constraints. Finally, safety-certificated guidance signals are obtained by solving a quadratic programming problem subject to input constraints. Using the proposed safety-certified LOS guidance method, the USV can accomplish a straight-line following task with guaranteed input-to-state safety. Simulation results substantiate the efficacy of the proposed safety-certificated LOS guidance method for the straight-line following of USVs moving in a constrained water region subject to unknown ocean currents. [ABSTRACT FROM AUTHOR]

In this paper, an event-triggered neural network control method is proposed for autonomous surface vehicles subject to uncertainties and input constraints over wireless network. An event-triggered mechanism with three logic rules is employed to determine the wireless data transmission of states and control inputs. An event-driven neural network is applied to approximate the uncertainties using aperiodic sampled states. In addition, a predictor is employed to update the weights of neural network. An event-based bounded kinetic control law is applied to address the actuator constraints. The advantage of the proposed event-triggered neural network control approach is that the network traffic can be reduced while guaranteeing system stability and speed following performance. The closed-loop control system is proved to be input-to-state stable via cascade theory. The Zeno behavior can be avoided via the proposed event-triggered neural network control approach. A simulation example is provided to demonstrate the effectiveness of the proposed event-triggered neural network control approach for autonomous surface vehicles. [ABSTRACT FROM AUTHOR]

In this paper, a multi-objective fuzzy-decision-making-based (MO-FDM) finite-state model predictive control (FS-MPC) method is proposed for improving both steady-state and transient-state operations of the grid-connected converters (GCCs). The main objectives of the proposed method are to achieve the excellent steady-state tracking performance and high dynamics without any weighting factors in the designed controller. Firstly, a repetitive control technique is embedded in the predictive controller, and an integral error term is introduced into the standard cost function used in the GCCs. Secondly, a simple and systematic procedure, which relies on MO-FDM technique, is employed to avoid the selection of weighting factors. As such, this scheme has a good potential to avoid the complicated tuning work while the control performance is not affected. Furthermore, in order to enhance the dynamic response and improve the computational efficiency, a dynamic reference design with a simplified cascade-free FS-MPC strategy is embedded into the predictive controller. Finally, simulation results are presented for demonstrating the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

The application of autonomous underwater vehicles (AUV) is widespread with the development of the activities in deep ocean. For low energy consumption, low resistance and excellent maneuverability, fins are usually used to modify AUV hydrodynamic forces. The underactuated AUV can do gyratory motion by vertical fins and do diving and rising motion by horizontal fins. However, the modeling and performance prediction for the underactuated AUV with fins have not been solved well. We build the dynamics and kinematics model in 6-DOF for the underactuated AUV with fins and analyze the forces and hydrodynamic coefficients in detail, especially the fin effect. Various hull hydrodynamic coefficients are obtained by CFD numerical computation. A series of simulation experiments are conducted to predict AUV hydrodynamic performance, and the feasibility and accuracy are verified by comparing the results on WL-II underactuated AUV in ocean experiments. [ABSTRACT FROM AUTHOR]

This paper considers the cooperative dynamic positioning of multiple marine surface vessels in the presence of dynamical uncertainty and time-varying ocean disturbances. The objective is to enable a group of vessels to automatically position themselves in a desired time-varying formation and track a reference position. By employing a dynamic surface control technique, distributed adaptive controllers are derived on the basis of the information of neighboring vessels. Neural network with iterative updating laws is used to accurately identify the dynamical uncertainty and time-varying ocean disturbances. Two types of adaptive laws are proposed and validated: (1) direct iterative updating laws based on the velocity tracking errors; (2) composite iterative updating laws based on the tracking errors and the prediction errors. For both cases, Lyapunov-Krasovskii functionals are employed to analyze the stability of the closed-loop network, and uniform ultimate boundedness of error signals are established. The key features of the proposed controllers are as follows. First, the information exchanges are reduced by employing a distributed control strategy. Second, by using the iterative updating laws, the mixed uncertainty including the internal model uncertainty and external time-varying ocean disturbances can be compensated. Besides, the proposed controllers are easier to implement in digital processors with the derivative-free updating laws. Third, the prediction errors and tracking errors are combined to construct the composite iterative neural control laws, which are able to achieve faster adaptation and improved performance. Comparison studies are given to show the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

This paper considers the coordinated formation pattern control of multiple marine surface vehicles in the presence of model uncertainty and time-varying ocean disturbances induced wind, waves and ocean currents. Leaderless and leader-follower formation controllers depending on the information of neighboring vehicles are devised based on a backstepping technique. Neural networks together with adaptive filtering methods are employed to extract the low frequency content of the model uncertainty and ocean disturbances. The results are further extended to the formation pattern control with unmatched time-varying ocean currents. An observer is developed to precisely identify the time-varying ocean currents. Then, observer-based leaderless and leader-follower formation controllers are proposed. For both cases, the stability properties of the multi-vehicle systems are established via Lyapunov analysis, and the formation tracking errors converge to an adjustable neighborhood of origin. An advantage of this design is that it results in adaptive formation controllers with guaranteed low frequency control signals, which facilitates practical implementations. An example is given to show the performance of the proposed methods. [ABSTRACT FROM AUTHOR]

This paper considers the distributed coordinated tracking problem of multiple autonomous underwater vehicles with a time-varying reference trajectory. Each vehicle is subject to model uncertainty and time-varying ocean disturbances. A novel predictor-based neural dynamic surface control design approach is proposed to develop the node controllers, under which synchronization between vehicles can be reached on condition that the augmented graph induced by the vehicles and the reference trajectory contains a spanning tree. The prediction errors are used to update the neural adaptive laws, which enable fast identifying the vehicle dynamics without excessive knowledge of their dynamical models. Further, this result is extended to the output-feedback case where only position-yaw information can be measured. A local predictor, based on its own position-yaw information, is constructed, not only to recover the unmeasured velocity information, but also to identify the unknown dynamics for each vehicle. A linear matrix inequality-based analysis is performed for the stability of the predictor. Then, distributed output-feedback tracking controllers are developed to achieve synchronization between vehicles in the presence of unknown dynamics and unmeasured velocities. For both cases, the stability properties of the closed-loop network are established via Lyapunov analysis. Simulation results demonstrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

This paper considers the cooperative path following problem of multiple marine surface vehicles subject to input saturation, unknown dynamical uncertainty and unstructured ocean disturbances, and partial knowledge of the reference velocity. The control design is categorized into two envelopes. Path following for each vehicle amounts to reducing an appropriately defined geometric error. Vehicles coordination is achieved by exchanging the path variables, as determined by the communications topology adopted. The control design is developed with the aid of the neural network-based dynamic surface control (DSC) technique, an auxiliary design, and a distributed estimator. The key features of the developed controllers are as follows. First, the neural network-based adaptive DSC technique allows for handling the unknown dynamical uncertainty and ocean disturbances without the need for explicit knowledge of the model, and at the same time simplify the cooperative path following controllers by introducing the first-order filters. Second, input saturations are incorporated into the cooperative path following design, and the stability of the modified control solution is verified. Third, the amount of communications is reduced effectively due to the distributed speed estimator, which means the global knowledge of the reference speed is relaxed. Under the proposed controllers, all signals in the closed-loop system are guaranteed to be uniformly ultimately bounded. Simulation results validate the performance and robustness improvement of the proposed strategy. [ABSTRACT FROM AUTHOR]

Unlike the tracking control of a single marine vehicle, this paper considers the leaderless and leader-follower cooperative control of multiple marine surface vehicles subject to unknown nonlinear dynamics and ocean disturbances, all seeking to maintain a relative formation. For both cases, a cooperative control design approach is proposed by integrating neural networks, a backstepping technique, and graph theory. It is shown that with the developed cooperative controllers, formation behavior among vehicles can be achieved for any undirected connected communication graphs without requiring the accurate model of each vehicle. Based on Lyapunov stability analysis, all signals in the closed-loop system are guaranteed to be uniformly ultimately bounded, and cooperative tracking errors converge to a small neighborhood of the origin. Simulation results are given to show the efficacy of the proposed methods. [ABSTRACT FROM AUTHOR]

A new adaptive control design approach is presented for a class of uncertain strict-feedback nonlinear systems. In the controller design process, all unknown functions at intermediate steps are passed down, and only one neural network is used to approximate the lumped unknown function of the system at the last step. By this approach, the designed controller contains only one actual control law and one adaptive law, and can be given directly. Compared with existing methods, the structure of the designed controller is simpler and the computational burden is lighter. Stability analysis shows that all the closed-loop system signals are uniformly ultimately bounded, and the steady state tracking error can be made arbitrarily small by appropriately choosing control parameters. Simulation studies demonstrate the effectiveness and merits of the proposed approach. [ABSTRACT FROM AUTHOR]