This paper focuses on the adaptive trajectory tracking control for a remotely operated vehicle (ROV) with an unknown dynamic model and the unmeasured states. Unlike most previous trajectory tracking control approaches, in this paper, the velocity states and the angular velocity states in the body-fixed frame are unmeasured, and the thrust model is inaccurate. Obviously, it is more in line with the actual ROV systems. Since the dynamic model is unknown, a new local recurrent neural network (local RNN) structure with fast learning speed is proposed for online identification. To estimate the unmeasured states, an adaptive terminal sliding-mode state observer based on the local RNN is proposed, so that the finite-time convergence of the trajectory tracking error can be guaranteed. Considering the problem of inaccurate thrust model, an adaptive scale factor is introduced into thrust model, and the thruster control signal is considered as the input of the trajectory tracking system directly. Based on the local RNN output, the adaptive scale factor, and the state estimation values, an adaptive trajectory tracking control law is constructed. The stability of the trajectory tracking control system is analyzed by the Lyapunov theorem. The effectiveness of the proposed control scheme is illustrated by simulations.

An integrated biologically inspired self-organizing map (SOM) algorithm is proposed for task assignment and path planning of an autonomous underwater vehicle (AUV) system in 3-D underwater environments with obstacle avoidance. The algorithm embeds the biologically inspired neural network (BINN) into the SOM neural networks. The task assignment and path planning aim to arrange a team of AUVs to visit all appointed target locations, while assuring obstacle avoidance without speed jump. The SOM neuron network is developed to assign a team of AUVs to achieve multiple target locations in underwater environments. Then, in order to avoid obstacles and speed jump for each AUV that visits the corresponding target location, the BINN is utilized to update weights of the winner of SOM, and achieve AUVs path planning and effective navigation. The effectiveness of the proposed hybrid model is validated by simulation studies.

In this paper, the formation obstacle avoidance problem of autonomous underwater vehicles (AUVs) under the disturbances of ocean currents is studied. A variable formation reconfiguration and obstacle avoidance control scheme based on affine transform and the improved artificial potential field (AT-IAPF) is designed, which enable AUVs to avoid both static and dynamic obstacles under external interference, and maintain the desired time-varying formation. Because of the robustness and strong effectiveness of the time-varying control of AT and the obstacle avoidance control law of IAPF. The AT-IAPF algorithm improves the multi-AUV systems’ environmental adaptability and obstacle avoidance performance. Using the Lyapunov function’s stability constraint guarantees stability of a multi-AUV system. A series of simulation results based on MATLAB verify that AUVs can effectively avoid obstacles with different formation shapes. Obstacle avoidance experiments on bionic robotic fish demonstrate the proposed method’s feasibility. Note to Practitioners —This paper was motivated by the problem of formation reconfiguration and obstacle avoidance for AUVs. Still, it also applies to unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs). The existing formation control methods usually solve the problems of formation acquisition and time-invariant maneuvering, and rarely consider the problem of formation obstacle avoidance. This paper presents a new formation obstacle avoidance method using affine transformation (AT) and improved artificial potential field (IAPF) techniques. We use the IAPF method to plan a possible path for the formation in the obstacle environment. At the same time, the appropriate formation shape is selected according to the obstacle information to better adapt to the environment. The preliminary experiments of two bionic robot fish in near-surface positions show that this method is feasible. During the experiment, UWB is used for positioning, and a Zigbee module is used to communicate and transmit data. But it still needs to solve the problem of underwater communication, and it has yet to be tested on multiple bionic robot fish. In future studies, we will conduct multiple actual AUV formation obstacle avoidance experiments or do 3D formation control experiments underwater.