In this paper, we study the quantitative design paradigm (QDP) of tracing control for a class of second-order system and further extend this to solve the trajectory tracking problem of autonomous underwater vehicles. The key merit of QDP is the capability of assigning some quantitative indices (e.g., overshoot and convergence time). To pursue performance enhancement with regard to tracking performance and bandwidth saving, a sectionalized event-triggered mechanism (SETM) incorporated with prespecified convergence time is proposed. To recognize the peculiarities of the lumped disturbances, an estimation-triggered neural network (ETNN) is designed via a property indicator, such that the disturbances that deteriorate the performance of the closed-loop system will be compensated and beneficial disturbances will be reserved otherwise, enabling less energy consumption without sacrificing the tracking performance. Theoretical analysis and simulation results are provided and analyzed, validating the performance and efficiency of the proposed solution.

Controlling biomimetic underwater robots in unknown flow fields remains a challenge due to the strong nonlinearity of the fluid. This article investigates the attitude holding task of a robotic fish swimming in reality. Such a typical sensing-based control task requires the fish to keep a desired angle of attack in an unknown and even varied incoming flow. To this end, we propose a learning-based approach by using a deep neural network directly maps the raw data of sensors equipped on the robot to the continuous control signals in an end-to-end manner. First, based on experimental data of the physical robot, we construct a data-driven simulation environment including three modules of dynamic, sensor, and control. The dynamic and sensor modules are established to model the dynamics of the fish and to generate its sensors’ data, based on which a deep reinforcement learning (DRL) algorithm in the control module is trained to get a control policy. Then, we directly deploy the trained policy to a physical robotic fish for attitude holding task. Experimental results demonstrate the robustness and effectiveness of the DRL policy and, thus, verify the success of our approach to achieving sim-to-real transfer.

A lateral line system is a flow-responsive organ system, with which fish can effectively sense the surrounding flow field, thus serving functions in flow-aided fish behaviors. Inspired by such a biological characteristic, artificial lateral line systems (ALLSs) have been developed for promoting technological innovations of underwater robots. In this article, we focus on investigating state estimation of a freely swimming robotic fish in multiple motions, including rectilinear motion, turning motion, gliding motion, and spiral motion. The state refers to motion parameters, including linear velocity, angular velocity, motion radius, etc., and trajectory of the robotic fish. Specifically, for each motion, a pressure variation (PV) model that links motion parameters to PVs surrounding the robotic fish is first built; then, a linear regression analysis method is used for determining the model parameters. Based on the acquired PV model, motion parameters can be estimated by solving the PV model inversely using the PVs measured by the ALLS. Finally, a trajectory estimation method is proposed for estimating trajectory of the robotic fish based on the ALLS-estimated motion parameters. The experimental results show that the robotic fish is able to estimate its trajectory in the aforementioned multiple motions with the aid of ALLS, with small estimation errors.

In this paper, we present the dynamic modeling and model predictive tracking control for a fin-actuated robot with barycentre regulating mechanism in multiple motions. Specifically, a dynamic model for the robot is established firstly. Based on the dynamic model, a model predictive tracking control algorithm is proposed. And simulations of of tracking rectangle trajectory, sine-like trajectory, ascending trajectory, and spiral trajectory are conducted to validate the algorithm. The simulation results demonstrate that the proposed algorithm is able to implement trajectory tracking of the robot with small position error and orientation error. This paper contributes to trajectory tracking for an underwater robot with controllable barycentre in multiple motions, which has been rarely explored.

Lateral line system (LLS) is a sensory organ system which serves functions in varieties of flow-relative fish behaviors. Inspired by excellent performances of LLS in fish behaviors, multiple artificial lateral line systems (ALLSs) have been designed and applied to promote underwater robot technology. In this article, we focus on using ALLS for longitudinal separation sensing between two adjacent swimming robotic fish whose tails oscillate, and meanwhile the two fish are towed with a precisely controlled speed which equals to the rectilinear speed of freely-swimming robotic fish with the same oscillating parameters. Flow visualizations based on dye injection technique, hydrogen bubble technique, and computational fluid dynamics simulation are conducted to study the distribution characteristics of the vortices caused by the robotic fish. In addition, towing tank experiments are conducted for investigating the qualitative relationships between the longitudinal separations and the ALLS-measured HPVs of the two robotic fish. This work provide a guidance for the application of ALLS in relative states sensing of underwater robot group composed of two or more individuals, which has been rarely explored.

In this article, a theoretical model describing the superficial hydrodynamic pressure variations (HPVs) of two adjacent robotic fish with leader-follower formation is established based on Bernoulli equation. Model parameters are identified by measured HPVs data and yawing rate data in experiments, based on multiple linear regression method. The HPVs data are measured by pressure sensor arrays based artificial lateral line system, and the yawing rate data are measured by attitude and heading reference system of the robotic fish. Errors between the measured superficial HPVs and the HPVs estimated by the established model are analyzed in detail. And it demonstrates that the established model has perfect performance in describing the superficial HPVs, with small errors. This article contributes to modeling of flow variations caused by free motions of underwater vehicle, which is always a challenge. The established model has the potentials of providing guidance for flow-aided control of underwater vehicle.