Lei Wan

Path Following of an Underactuated AUV Based on Fuzzy Backstepping Sliding Mode Control

This paper addresses the path following problem of an underactuated autonomous underwater vehicle (AUV) with the aim of dealing with parameter uncertainties and current disturbances. An adaptive robust control system was proposed by employing fuzzy logic, backstepping and sliding mode control theory. Fuzzy logic theory is adopted to approximate unknown system function, and the controller was designed by combining sliding mode control with backstepping thought. Firstly, the longitudinal speed was controlled, then the yaw angle was made as input of path following error to design the calm function and the change rate of path parameters. The controller stability was proved by Lyapunov stable theory. Simulation and outfield tests were conducted and the results showed that the controller is of excellent adaptability and robustness in the presence of parameter uncertainties and external disturbances. It is also shown to be able to avoid the chattering of AUV actuators.

Horizontal-plane Trajectory-tracking Control of an Underactuated Unmanned Marine Vehicle in the Presence of Ocean Currents

Based on an integral backstepping approach, a trajectory-tracking control algorithm is proposed for an underactuated unmanned marine vehicle (UMV) sailing in the presence of ocean-current disturbance. Taking into consideration the UMV models fore/aft asymmetry, a nonlinear three-degree-of-freedom (3DOF) underactuated dynamic model is established for the horizontal plane. First, trajectory-tracking differences between controllers designed based on symmetric and asymmetric models of the UMV are discussed. In order to explicitly study the effect of ocean-current interference on the trajectory-tracking controller, the ocean current is integrated into the kinematic and dynamic models of the UMV. Detailed descriptions of distinct trajectory-tracking control performances in the presence of different ocean-current velocities and direction angles are presented. The well-known persistent exciting (PE) condition is completely released in the designed trajectory-tracking controller. A mild integral item of trajectory tracking error is merged into the control law, and global stability analysis of the UMV system is carried out using Lyapunov theory and Barbalats Lemma. Simulation experiments in the semi-physical simulation platform are implemented to confirm the effectiveness and superiority of the excogitated control algorithm.

Point Stabilization for an Underactuated AUV in the Presence of Ocean Currents

This paper presents a state-feedback-based backstepping control algorithm to address the point stabilization (or set-point regulation) control problem for an underactuated autonomous underwater vehicle (AUV) in the presence of constant and irrotational ocean current disturbance. A nonlinear three degree of freedom dynamic model in the horizontal plane for an AUV without symmetry fore/aft is considered. The expression of the relationship between the desired heading angle of the AUV and direction angle of the ocean current, which is a necessary condition for precise point stabilization control of an underactuated AUV in the presence of ocean current disturbance is firstly discussed in this paper. The proposed backstepping control law for point stabilization has further been enriched by incorporating an additional integral action for enhancing the steady state performance of the AUV control system, while practical asymptotic stability analysis of the system is carried out using Lyapunov theory and Barbalats Lemma. Simulation experiments of an underactuated AUV verify the theorem proposed and demonstrate the effectiveness of the controller.

Adaptive line-of-sight path following control for underactuated autonomous underwater vehicles in the presence of ocean currents

This article presents a nonlinear adaptive line-of-sight path following controller for underactuated autonomous underwater vehicles in the presence of ocean currents. Firstly, a new nonsingular path following error kinematic model in the Serret–Frenet frame is developed, where a nominal course angle error is introduced to significantly simplify the guidance law design. Secondly, an adaptive line-of-sight guidance law with the introduction of the current observer is proposed to make the vehicle produce a variable sideslip angle to compensate for the drift force for any parametric curved-path path following. Benefit from the global κ-exponential convergence property of the designed current observer, the actual course angle error can be eliminated indirectly. Then, dynamic controller built on Lyapunov theory and backstepping technique guarantee the uniform global exponential stability of the yaw and relative surge velocity. In the end, stability analysis shows that the global κ-exponential stability is achieved for the closed-loop system. Simulation results demonstrate the effectiveness of the proposed control scheme.

Heading Control System Design for a Micro-USV Based on an Adaptive Expert S-PID Algorithm

Abstract The process of heading control system design for a kind of micro-unmanned surface vessel (micro-USV) is addressed in this paper and a novel adaptive expert S-PID algorithm is proposed. First, a motion control system for the micro-USV is designed based on STM32-ARM and the PC monitoring system is developed based on Labwindows/CVI. Second, by combining the expert control technology, S plane and PID control algorithms, an adaptive expert S-PID control algorithm is proposed for heading control of the micro-USV. Third, based on SL micro-USV developed in this paper, a large number of pool experiments and lake experiments are carried out, to verify the effectiveness and reliability of the motion control system designed and the heading control algorithm proposed. A great amount of comparative experiment results shows the superiority of the proposed adaptive expert S-PID algorithm in terms of heading control of the SL micro-USV.

Robust composite neural dynamic surface control for the path following of unmanned marine surface vessels with unknown disturbances

This article presents a robust composite neural-based dynamic surface control design for the path following of unmanned marine surface vessels in the presence of nonlinearly parameterized uncertainties and unknown time-varying disturbances. Compared with the existing neural network-based dynamic surface control methods where only the tracking errors are commonly used for the neural network weight updating, the proposed scheme employs both the tracking errors and the prediction errors to construct the adaption law. Therefore, faster identification of the system dynamics and improved tracking accuracy are achieved. In particular, an outstanding advantage of the proposed neural network structure is simplicity. No matter how many neural network nodes are utilized, only one adaptive parameter that needs to be tuned online, which effectively reduces the computational burden and facilitates to implement the proposed controller in practice. The uniformly ultimate boundedness stability of the closed-loop system is established via Lyapunov analysis. Comparison studies are presented to demonstrate the effectiveness of the proposed composite neural-based dynamic surface control architecture.

Adaptive recurrent neural network motion control for observation class remotely operated vehicle manipulator system with modeling uncertainty

Precise motion control of remotely operated vehicles plays an important role in a great number of submarine missions. However, the high-performance operations are difficult to realize due to the uncertainty in system modeling with self-disturbance. On the basis of the multibody system dynamics, self-disturbances from the tether and manipulator have been systematically analyzed in order to transform them into observed forces. A novel S surface–based adaptive recurrent wavelet neural network control system has been proposed on the nonlinear control of underwater vehicles, with its recurrent wavelet neural network structure designed for the approximation of the uncertain dynamics. Moreover, a robust function has been proposed to improve system robustness and convergence. The comparison shows that the remotely operated vehicle operation performance including the three-dimensional path following and vehicle-manipulator coordinate control has been greatly improved.

The Study of the Seabed Side-Scan Acoustic Images Recognition Using BP Neural Network

In recent years, mankind has made great achievements in the marine exploration. Ocean contains abundant resources, and the seabed has recorded amount of basic Earth information. Therefore, a complete study of the seabed can help to form a full appreciation of underwater environment. The study of the seabed recognition method, as the most basic work of the study of the seabed, is gradually gaining the attention of researchers. As a main marine exploratory tool, the side-scan sonar is fast, accurate and convenient for seabed information collection. In this paper, lots of seabed acoustic images were applied to extract the seabed substrate characteristics using the gray covariance matrix method. An improved BP neural network model was involved into classify and identify the seabed characteristics. In addition, several algorithms for BP neural network were proposed for testing the recognition accuracy of side-scan acoustic images and the convergence rate. The results show that although several algorithms were easy to fall into the minimum value during training, which can lead to slow convergence rate and unable to meet the recognition accuracy standard, the trainlm function had a faster convergence rate and higher recognition accuracy.

Design of X-rudder autonomous underwater vehicle’s quadruple-rudder allocation with Lévy flight character

This article addresses one quadruple-rudder allocation method for an autonomous underwater vehicle (AUV) equipped with X rudder, in which all of the rudders can be operated independently. By considering X rudder’s character, one X-rudder AUV’s control information frame is designed. It contains the anti-normalization method based on virtual rudders and one quadruple-rudder allocation with Lévy flight character. This quadruple-rudder allocation method has the advantages of Lévy flight and avoids the shaking problem. One contrast simulation of a lawn mower path following mission in three-dimensional (3D) space is performed. The results of simulation show that the quadruple-rudder allocation with Lévy flight character can offer accurate and reliable control ability. Besides that, compared to the quadruple-rudder allocation based on pseudoinverse and fixed point iteration, the method designed with Lévy flight character can achieve the same mission with less X rudder’s operation.

A novel approach to integrate potential field and interval type-2 fuzzy learning for the formation control of multiple autonomous underwater vehicles

Underwater vehicles coordination and formation have attracted increasingly attentions since their great potential on the real-world applications. However, usually such vehicles are underactuated and with very different environmental difficulties, which are different from those vehicles (robots) on the land. This study proposes a novel approach to integrate potential field and interval type-2 fuzzy learning algorithm for autonomous underwater vehicles formation control based on formation system framework. For the system nonlinearity and complicated environment, support vector machine has been applied to generate optimal rules for the type-2 fuzzy systems. This approach can generate optimal and reasonable formation rules on the face of different situations through classification. Furthermore, reinforcement learning has been combined with fuzzy systems to deal with limited communication state during formation. Therefore, autonomous underwater vehicles can not only execute actions through the evaluation, but also can avoid coupling character between communication state and potential field. Finally, simulations and experiments results have been extensively performed to validate the proposed methods.