The observer-based controller design for a T-S fuzzy system subject to external disturbance is investigated in this paper. First, an H$\infin$ observer is designed to estimate the unknown system states, by a transformation process for system states and the disturbance term, a state feedback H$\infin$ controller is given. Then, the separation principle for a T-S fuzzy system subject to external disturbance is proposed. With the help of the improved separation principle, the observer and controller can be combined together to make the fuzzy system input state stable, while the H$\infin$ index can also be guaranteed. The proposed design approach is applicable for both known and unknown premise variables. In stability analysis, the membership functions derivatives dependent method is proposed to reduce the conservatism. In the simulation part, stability analysis of a three-rule system is carried out to illustrate the conservatism reduction effects in Example 1, then a vessel dynamic positioning control process subject to ocean disturbance is adopted to illustrate the effectiveness of the proposed methods for both known/unknown premise variable cases.

Target state estimation is a key technology for unmanned underwater vehicles to achieve target tracking, collision avoiding, formation control, and other tasks. Compared with other measurement methods, underwater measurement has lower reliability due to the uncertainty of sonar detection. In this case, the performance of target state estimation depends heavily on the target motion model. However, the dynamics of unmanned underwater vehicles are very complex and nonlinear. Although many state estimation methods for nonlinear systems have been proposed, the complex dynamics of unmanned underwater vehicle and uncertainty in sonar detection remain challenges for underwater target state estimation problems. This paper proposes a gated recurrent units-based particle filter to improve the performance of the target state estimator for unmanned underwater vehicles. A deep neural network framework based on gated recurrent units is used to establish the mapping between the previous measurements and the current target states. This neural network learns the dynamics model of unmanned underwater vehicles and recognizes the measurement noise. The proposed filter samples from previous measurements of the target unmanned underwater vehicle, and the fully trained deep neural network predicts the current states of the sampled particles. The proposed method solves the low accuracy and instability caused by motion modeling errors and system nonlinearity. Simulation results show that the proposed gated recurrent units-based particle filter for unmanned underwater vehicle state estimation has better accuracy and stability than the traditional state estimation methods. All simulations are done with the noise which is Gaussian noise pass through a nonlinear transformation.

A safety guaranteed formation control problem for waterjet unmanned surface vehicles (USV) under the consideration of unmodeled dynamics, environmental disturbances, input saturation, and output constraints is addressed in this paper. The leader-follower method and line-of-sight (LOS) strategy are employed to achieve the desired formation structure. Firstly, two tan-type barrier Lyapunov functions (BLF) are designed to limit the formation errors always within a predefined range to avoid collisions and keep communications, it can also relax the assumption of each USV's initial position to random. Besides, a bioinspired neurodynamics method is involved to avoid the appearance of the virtual velocity differential terms in the control law, which is different from the traditional dynamic surface method and makes it easier to implement in practice. Further, adaptive neural networks (NN) are used to approximate unmodeled dynamics and an auxiliary dynamics system is introduced to handle the input saturation. It is proved that the uniform ultimate boundedness of all signals in the control system can be achieved. Finally, simulation studies show the effectiveness of the proposed control law.

On account of the external disturbances and difficult maneuverability of a hovercraft, this paper devises a safety-guaranteed, robust, nonlinear, path-following control strategy of a hovercraft targeted for unknown dynamics, unavailable velocity, and unknown external ocean disturbances. Firstly, for the sake of accurately observing unavailable lumped disturbances and unavailable velocity measurements, a finite-time extended state observer (FTESO) is proposed. Secondly, a line-of-sight (LOS) guidance law constructed with a bounded-gain-forgetting (BGF) adaptive estimator is devised to follow the desired path while considering external environmental disturbances accurately, in which the tracking errors and the parameter estimation are both proven to be bounded. In addition, for the sake of safety, a safety-guaranteed auxiliary system that can constrain the drift angle during the hovercraft’s navigation is proposed. Thirdly, the robust, nonlinear, path-following controllers achieved high tracking performance with the constructed safety-guaranteed compensation backstepping method. Finally, according to the Lyapunov and homogeneous theories, the observation error can be guaranteed to zero and the tracking error can converge to an arbitrarily small region near zero in finite time. Numerical simulations illustrate the effectiveness for the proposed robust, nonlinear, path-following scheme.