This paper investigates the finite-time-prescribed performance-based (FTPP) platoon formation control problem of unmanned surface vehicles (USVs) with connectivity preservation andcollisionavoidance.Eachvehicleissubjecttounmeasuredvelocities, model uncertainties, external disturbances and actuator input delay. By introducing a finite-time performance function (FTPF), the desired transient and steady-state performances can be guaranteed in finite time, which makes the line-of-sight (LOS) error and angle error be limited within the desired constraints to achieve collision avoidance and connectivity maintenance. Furthermore, a relevant variable containing constrained upper and lower bounds is designed to further realize the FTPP with asymmetric properties. In order to recover unmeasured velocity information, estimate uncertain dynamics of the vehicle and compensate for external disturbances, an extended state observer (ESO) based on position-heading information is proposed. Thereinto, the event-triggered mechanism is introduced to reduce the communication burden from the sensor to the observer channel. The effects of actuator input delay are eliminated by using the Pade approximation transform. A second-order linear tracking differentiator (TD) is introduced into the kinematic design to avoid the measurement of acceleration in the dynamics control design. The stability analysis shows that the proposed control scheme ensures that all signals of the USVs are bounded, and the Zeno behavior will not occur. Moreover, the distance between the two neighboring USVs converges to the prescribed range, meanwhile, the tracking angle error limits to a desired arbitrarily small region within finite time. Finally, the simulation results further confirm the effectiveness of the proposed control scheme

This paper focuses on a distributed cooperative time-varying formation maneuvering issue of under-actuated unmanned surface vehicles (USVs). A fleet of USVs is guided by a parameterized path with a time-varying formation while avoiding collisions and preserving the connectivity in the environment with multiple obstacles. In some surface missions, due to the obstacles in the external environment, the bandwidth limitations of the communication channel, and the hardware components/performance constraints of the USVs themselves, each vehicle is considered to be subject to model uncertainty, actuator quantization, sensor dead zone, and velocity constraints. During the control design process, the radial basis function (RBF) neural networks (NNs) are utilized to deal with nonlinear terms. Based on a nonlinear decomposition method, the relationship between the control signal and the quantization one is established, which overcomes the difficulty arising from actuator quantization. A Nussbaum function is introduced to handle the unknown output dead zone problem caused by reduced sensor sensitivity. Moreover, a universal-constrained function is employed to satisfy both the constrained and unconstrained requirements during formation keeping and obstacle avoidance. The Lyapunov stability theory confirmed that the error signals are uniformly ultimately bounded (UUB). The simulation results demonstrate the effectiveness of the proposed distributed formation control of multiple USVs.

This paper investigates a fault-tolerant control problem for the dynamic positioning of unmanned marine vehicles based on a Takagi–Sugeno (T-S) fuzzy model using an integral sliding mode scheme. First, the T-S fuzzy model of an unmanned marine vehicle is established by taking the yaw angle variable range into account. An integral sliding mode control scheme combined with the ${\mathcal{H}}_{\infty }$ performance index is then developed to attenuate the initial influence of thruster faults and ocean disturbances. The unknown nonlinear function is approximated using a fuzzy logic system based on a representation of marine data, which provides a good tradeoff between resolution of the unknown nonlinear term approximation and computational complexity for marine engineering by adjusting the number of fuzzy logic system rules. In addition, the fault estimation information is utilized to design the sliding mode surface on the basis of an adaptive mechanism and a matrix full rank decomposition technique, which reduces conservatism. The validity of the proposed approach is finally demonstrated by an analysis of simulation results using a typical floating production vessel model.

This paper addresses integral sliding mode output feedback fault-tolerant control (FTC) of unmanned marine vessels (UMVs) with unknown premise variables and actuator faults. Due to the complexity of the marine environment, the presence of uncertainties in the yaw angle renders the premise variables in the Takagi–Sugeno (T–S) fuzzy model of UMVs unknown. Consequently, traditional integral sliding mode techniques become infeasible. To address this issue, a control strategy combining integral sliding mode based on output feedback with a compensator utilizing switching mechanisms is proposed. First, a radial basis function neural network is used to approximate the nonlinear terms in the UMV T–S fuzzy model. In addition, an integral sliding mode surface is constructed based on fault estimation information and membership function estimation. On this basis, an FTC scheme based on integral sliding mode output feedback is developed to ensure that the UMV system is asymptotically stable and satisfies the prescribed ${\mathcal{H}}_{\infty }$ performance index. Finally, simulation results are provided to demonstrate the effectiveness of the presented control strategy.

This paper studies the design problem of fault detection (FD) observer for unmanned marine vehicles (UMVs) based on the T-S fuzzy model. Firstly, T-S fuzzy systems are used to approximate the nonlinear dynamics in UMVs. Secondly, to improve the FD performance of UMVs, a new $H_{\infty }/H\_$ performance index, which depends on the membership functions, is defined. Then, based on the membership-function-dependent $H_{\infty }/H\_$ performance index, a new fuzzy FD observer strategy, where the fuzzy submodels are not all required to be with the same $H\_$ performance index, is developed to detect the sensor fault in UMVs; the corresponding synthesis conditions of the FD observer are derived based on the Lyapunov theory. Different from the conventional FD strategies, in the proposed membership-function-dependent FD method, the fuzzy submodels—which the system always works on—can have a larger $H\_$ performance index, such that the performance of the FD can be improved. In the end, an example is given to show the effectiveness of the presented method.