Adaptive Backstepping Terminal Sliding Mode Control Method Based on Recurrent Neural Networks for Autonomous Underwater Vehicle

The trajectory tracking control problem is addressed for autonomous underwater vehicle(AUV) in marine environ?ment, with presence of the influence of the uncertain factors including ocean current disturbance, dynamic modeling uncertainty, and thrust model errors. To improve the trajectory tracking accuracy of AUV, an adaptive backstepping terminal sliding mode control based on recurrent neural networks(RNN) is proposed. Firstly, considering the inaccu?rate of thrust model of thruster, a Taylor’s polynomial is used to obtain the thrust model errors. And then, the dynamic modeling uncertainty and thrust model errors are combined into the system model uncertainty(SMU) of AUV; through the RNN, the SMU and ocean current disturbance are classified, approximated online. Finally, the weights of RNN and other control parameters are adjusted online based on the backstepping terminal sliding mode controller. In addition, a chattering?reduction method is proposed based on sigmoid function. In chattering?reduction method, the sigmoid function is used to realize the continuity of the sliding mode switching function, and the sliding mode switching gain is adjusted online based on the exponential form of the sliding mode function. Based on the Lyapu?nov theory and Barbalat’s lemma, it is theoretically proved that the AUV trajectory tracking error can quickly converge to zero in the finite time. This research proposes a trajectory tracking control method of AUV, which can e ectively achieve high?precision trajectory tracking control of AUV under the influence of the uncertain factors. The feasibility and e ectiveness of the proposed method is demonstrated with trajectory tracking simulations and pool?experi?ments of AUV.

Parallel Neural Network-Based Motion Controller for Autonomous Underwater Vehicles

A parallel neural network-based controller （PNNC） is presented for the motion control of underwater vehicles in this paper. It consists of a real-time part, a self-learning part and a desired-state programmer, and it is different from normal adaptive neural network controller in structure. Owing to the introduction of the self-learning part, on-line learning can be performed without sample data in several sample periods, resulting in high learning speed of the controller and good control performance. The desired-state programmer is utilized to obtain better learning samples of the neural network to keep the stability of the controller. The developed controller is applied to the 4-degree of freedom control of the AUV “IUV- IV” and is successful on the simulation platform. The control performance is also compared with that of neural network controller with different structures such as normal adaptive neural network and different learning methods. Current effects and surge velocity control are also included to demonstrate the controller＇ s performance. It is shown that the PNNC has a great possibility to solve the problems in the control system design of underwater vehicles.

Nonlinear trajectory tracking control of a new autonomous underwater vehicle in complex sea conditions

Autonomous underwater vehicles (AUVs) navigating in complex sea conditions usually require a strong control system to keep the fastness and stability. The nonlinear trajectory tracking control system of a new AUV in complex sea conditions was presented. According to the theory of submarines,the six-DOF kinematic and dynamic models were decomposed into two mutually non-coupled vertical and horizontal plane subsystems. Then,different sliding mode control algorithms were used to study the trajectory tracking control. Because the yaw angle and yaw angle rate rather than the displacement of the new AUV can be measured directly on the horizontal plane,the sliding mode control algorithm combining cross track error method and line of sight method was used to fulfill its high-precision trajectory tracking control in the complex sea conditions. As the vertical displacement of the new AUV can be measured,in order to achieve the tracking of time-varying depth signal,a stable sliding mode controller was designed based on the single-input multi-state system,which took into account the characteristic of the hydroplane and the amplitude and rate constraints of the hydroplane angle. Moreover,the application of dynamic boundary layer can improve the robustness and control accuracy of the system. The computational results show that the designed sliding mode control systems of the horizontal and vertical planes can ensure the trajectory tracking performance and accuracy of the new AUV in complex sea conditions. The impacts of currents and waves on the sliding mode controller of the new AUV were analyzed qualitatively and quantitatively by comparing the trajectory tracking performance of the new AUV in different sea conditions,which provides an effective theoretical guidance and technical support for the control system design of the new AUV in real complex environment.

Tracking Control of Autonomous Underwater Vehicles with Internal Moving Mass

追踪轨道的控制问题被调查为一自治在水下作为致动器用一个内部点团和一个后面的推进器搬到垂直飞机的车辆(AUV ) 。与点团的动力学结合了， AUV 作为一个 underactuated 系统被建模。一个基于 Lyapunov 的追踪控制器被使用 backstepping 途径稳定错误动力学并且强迫位置错误到起源的一位小邻居建议。模拟结果验证建议追踪的途径。

Adaptive PID control system for an autonomous underwater vehicle

The control system of an autonomous underwater vehicle （AUV） is introduced. According to control requirements of the AUV, a simple but practical adaptive PID control method is designed The semi-physical simulation is done to test the feasibility of the control system. The neural network idea and the structure of PID controller are referred to design the adaptive PID controller. An intelligent integral is introduced to improve control precision. Compaed with traditional PID con- trollers, the adaptive PID controller has simple structure, good online adjusting ability, fast convergence and good robustness. The simulation experiments also show that the adaptive PID control system has high precision and fine antijamming ability.

Stability Analysis on Speed Control System of Autonomous Underwater Vehicle

The stability of the motion control system is one of the decisive factors of the control quality for Autonomous Underwater Vehicle（AUV）.The divergence of control,which the unstable system may be brought about,is fatal to the operation of AUV.The stability analysis of the PD and S-surface speed controllers based on the Lyapunov＇s direct method is proposed in this paper.After decoupling the six degree-of-freedom（DOF）motions of the AUV,the axial dynamic behavior is discussed and the condition is deduced,in which the parameters selection within stability domain can guarantee the system asymptotically stable.The experimental results in a tank and on the sea have successfully verified the algorithm reliability,which can be served as a good reference for analyzing other AUV nonlinear control systems.

Reinforcement learning based parameter optimization of active disturbance rejection control for autonomous underwater vehicle

This paper proposes a liner active disturbance rejection control(LADRC) method based on the Q-Learning algorithm of reinforcement learning(RL) to control the six-degree-of-freedom motion of an autonomous underwater vehicle(AUV).The number of controllers is increased to realize AUV motion decoupling.At the same time, in order to avoid the oversize of the algorithm, combined with the controlled content, a simplified Q-learning algorithm is constructed to realize the parameter adaptation of the LADRC controller.Finally, through the simulation experiment of the controller with fixed parameters and the controller based on the Q-learning algorithm, the rationality of the simplified algorithm, the effectiveness of parameter adaptation, and the unique advantages of the LADRC controller are verified.

Development of A Three-Dimensional Guidance System for Long-Range Maneuvering of A Miniature Autonomous Underwater Vehicle

The present paper introduces a three-dimensional guidance system developed for a miniature Autonomous Underwater Vehicle（AUV）. The guidance system determines the best trajectory for the vehicle based on target behavior and vehicle capabilities. The dynamic model of this novel AUV is derived based on its special characteristics such as the horizontal posture and the independent diving mechanism. To design the guidance strategy, the main idea is to select the desired depth, presumed proportional to the horizontal distance of the AUV and the target. By connecting the two with a straight line, this strategy helps the AUV move in a trajectory sufficiently close to this line. The adjacency of the trajectory to the line leads to reasonably short travelling distances and avoids unsafe areas. Autopilots are designed using sliding mode controller. Two different engagement geometries are considered to evaluate the strategy＇s performance： stationary target and moving target. The simulation results show that the strategy can provide sufficiently fast and smooth trajectories in both target situations.

面向海底管道巡检的AUV三维自适应路径跟踪

针对面向海底管道巡检任务的欠驱动自主水下航行器(AUV)三维路径跟踪控制问题进行研究。首先,在固定坐标系和机体坐标系下建立AUV的运动学模型,为设计适宜运动控制的三维路径跟踪控制器,建立流体坐标系,并在Serret-Frenet坐标系下建立AUV的路径跟踪误差模型;其次,将可变前视距离引入传统视线(LOS)制导策略,提出一种自适应LOS制导策略以提高制导精度,并基于此设计面向海底管道巡检的AUV三维路径跟踪控制器,使得AUV可有效跟踪设定的参数化路径;最后,基于李雅普诺夫稳定性理论证明了闭环系统的稳定性。仿真结果验证了所提面向海底管道巡检的三维路径跟踪控制器的有效性。研究成果能够改善AUV的操纵性,可为面向海底管道巡检的无人水下航行器路径跟踪控制方法提供参考。

A Novel Obstacle Avoidance Consensus Control for Multi-AUV Formation System

In this paper, the fixed-time event-triggered obstacle avoidance consensus control for a multi-AUV time-varying formation system in a 3D environment is presented by using an improved artificial potential field and leader-follower strategy(IAPF-LF). Firstly, the proposed fixed-time control can achieve the desired multi-AUV formation within a fixed settling time in any initial system state. Secondly, an event-triggered communication strategy is developed to govern the communication among AUVs, and the communication energy consumption can be decremented. The time-varying formation obstacle avoidance control algorithm based on IAPF-LF is designed to avoid static and dynamic obstacles, the desired formation is maintained in the presence of external disturbances, and there is no Zeno behavior under the fixed-time event-triggered consensus control strategy.The stability of the system is proved by the Lyapunov function and inequality scaling. Finally, simulation examples and water pool experiments are reported to verify the performance of the proposed theoretical algorithms.

基于MPC-MRAC的用于子母式AUV投放的子AUV控制算法

针对水下搜救、水下集群作业等场合对负载能力、响应速度和多体协同的特殊要求,提出一种子母式自主水下航行器(AUV)概念。针对子母式AUV中的子AUV平台进行投放过程设计与分析,并对不同运动模式下的子AUV进行受力分析和建模。根据子AUV运动特性设计一套基于模型预测控制-模型参考自适应控制算法(MPC-MRAC)的子AUV控制算法,并进行仿真。结果显示:该算法具有较快的响应速度和控制稳定性,适用于子AUV投放后的自主运动控制;其响应速度比反演滑模控制算法快42.48%,比比例积分微分(PID)控制算法快87.05%;控制精度比反演滑模控制算法高77.54%,比PID控制算法高76.19%。针对该系统设计制作样机并开展湖上试验,结果表明,子AUV控制算法具有良好的可行性和可靠性,可顺利实现子AUV投放后运行的安全可靠。子AUV在出舱后经过短时间姿态变化,成功与母AUV分离并顺利进行定深任务,最终达到近似等距分布的效果。研究成果可为用于子母式AUV投放的子AUV提供控制算法基础。

基于无模型自抗扰的AUV深度控制研究

针对自主式水下航行器(AUV)在海洋中易受海流海浪扰动的问题,本文提出一种基于自抗扰的无模型自适应深度控制方法(ADR-MFAC)。首先,通过引入无模型自适应控制律,解决AUV难以建立精确的数学模型的问题;其次,设计出AUV深度控制器,结合自抗扰控制算法来进一步提高控制器干扰性能,采用跟踪微分器对输入信号进行跟踪,并利用线性状态观测器来估计系统所受扰动。最后,通过仿真和海试验证了ADRMFAC的控制性能:与PID控制相比,ADR-MFAC在保持较高控制精度的同时,超调量减小了51%,深度调节时间减小了21.4%,证明了ADR-MFAC在受到海流干扰时也可以实现稳定的深度控制,算法具有较强的鲁棒性。

Fixed-time Sliding Mode Formation Control of AUVs Based on a Disturbance Observer

In this paper,we investigate formation tracking control of autonomous underwater vehicles(AUVs)with model parameter uncertainties and external disturbances.The external disturbances due to the wind,waves,and ocean currents are combined with the model parameter uncertainties as a compound disturbance.Then a disturbance observer(DO)is introduced to estimate the compound disturbance,which can be achieved within a finite time independent of the initial estimation error.Based on a DO,a novel fixed-time sliding control scheme is developed,by which the follower vehicle can track the leader vehicle with all the states globally stabilized within a given settling time.The effectiveness and performance of the method are demonstrated by numerical simulations.

State Feedback Sliding Mode Control without Chattering by Constructing Hurwitz Matrix for AUV Movement

This paper presents a new method to eliminate the chattering of state feedback sliding mode control （SMC） law for the mobile control of an autonomous underwater vehicle （AUV） which is nonlinear and suffers from unknown disturbances system. SMC is a well-known nonlinear system control algorithm for its anti-disturbances capability, while the chattering on switch surface is one stiff question. To dissipate the well-known chattering of SMC, the switching manifold is proposed by presetting a Hurwitz matrix which is deducted from the state feedback matrix. Meanwhile, the best switching surface is achieved by use of eigenvalues of the Hurwitz matrix. The state feedback control parameters are not only applied to control the states of AUV but also connected with coefficients of switching surface. The convergence of the proposed control law is verified by Lyapunov function and the robust character is validated by the Matlab platform of one AUV model.

基于拉盖尔函数的AUV自适应预测轨迹跟踪

针对复杂水文环境下无人自治水下机器人(AUV)轨迹跟踪实时性较慢、精度较低的问题,设计了一种基于拉盖尔函数的自适应预测轨迹跟踪控制方法。首先,基于预测控制将轨迹跟踪问题转化为二次规划设计;其次,为解决AUV轨迹变化以及外部干扰所造成的控制量突变,融合递推最小二乘法设计了自适应预测控制器;接着,采用拉盖尔函数重构控制器,以解决运算量过大导致的系统响应速度变慢的问题;最后,仿真证明了该方法能够提升系统的响应速度、抗干扰性和鲁棒性。

基于深度学习的AUV水下视觉导引检测方法

自主水下航行器(AUV)自主对接与回收技术主要通过导引定位的方式实现AUV与对接装置的自主归航、接近、对接及锁紧等动作。为满足AUV水下自主对接过程中实时性、高精确性和鲁棒性等要求,提出一种基于深度学习的水下视觉导引检测方法。针对复杂水下场景下传统图像处理方法检测效果不佳的问题,使用基于YOLOv5的深度学习视觉导引检测方法对导引光源以及对接装置进行检测。首先,将目标图像数据接入YOLOv5模型进行迭代训练,将训练得到的最优模型参数保存用于后续实时检测;然后,在水下自主对接过程中, AUV使用机器人操作系统平台实时读取水下数据并调用YOLO服务对水下图像进行检测,输出导引光源以及对接装置位置信息;同时通过位置解算,将检测得到的中心点坐标转化到AUV相机坐标系下;最后将解算得到的AUV与对接装置的相对位置与AUV的航行方向持续反馈给AUV,进行引导直至对接完成。在海试中对水下视觉导引的实际检测准确率为97.9%,检测单帧耗时为45 ms,试验结果表明该方法满足自主对接与回收技术中对水下对接精度及实时性要求,具有实际应用价值。

基于线性自抗扰控制的AUV深度控制研究

自主式水下航行器具有非线性、时变性、耦合性强且易受干扰等特点,为进一步提高AUV的深度控制效果,建立六自由度空间运动模型和执行机构响应模型,将线性自抗扰控制方法引入航行器深度控制,使用基于正切Sigmoid函数的跟踪微分器优化了线性自抗扰控制方法。将上述方法应用于深度控制实验中,并从控制精度和操舵能耗两方面评价其控制效果。仿真结果表明,在无干扰条件下,与S面控制方法相比,改进后的线性自抗扰方法在具有较高控制精度的同时平均操舵能耗指标下降了55.9%,且在内外扰动作用下保持了较高的控制精度和较少的操舵能耗,体现了较好的鲁棒性和节能特性。

A Practical Specialization of MDA/MBSE Approach to Develop AUV Controllers

The model-driven architecture(MDA)/model-based systems engineering(MBSE)approach,in combination with the real-time Unified Modeling Language(UML)/Systems Modeling Language(SysML),unscented Kalman filter(UKF)algorithm,and hybrid automata,are specialized to conveniently analyze,design,and implement controllers of autonomous underwater vehicles(AUVs).The dynamics and control structure of AUVs are adapted and integrated with the specialized features of the MDA/MBSE approach as follows.The computation-independent model is defined by the specification of a use case model together with the UKF algorithm and hybrid automata and is used in intensive requirement analysis.The platform-independent model(PIM)is then built by specializing the real-time UML/SysML’s features,such as the main control capsules and their dynamic evolutions,which reflect the structures and behaviors of controllers.The detailed PIM is subsequently converted into the platform-specific model by using open-source platforms to quickly implement and deploy AUV controllers.The study ends with a trial trip and deployment results for a planar trajectory-tracking controller of a miniature AUV with a torpedo shape.

S^2 BHCA-Multiple AUVs cooperation oriented control architecture

Oceanographic survey, or other similar applications should be the applications of multiple AUVs. In this paper, the skill & simulation based hybrid control architecture (S2BHCA) as the controller's design reference was proposed. It is a multi-robot cooperation oriented intelligent control architecture based on hybrid ideas. The S2 BHCA attempts to incorporate the virtues of the reactive controller and of the deliberative controller by introducing the concept of the "skill". The additional online task simulation ability for cooperation is supported, too. As an application, a multiple AUV control system was developed with three "skills" for the MCM mission including two different cooperative tasks. The simulation and the sea trials show that simple task expression, fast reaction and better cooperation support can be achieved by realizing the AUV controller based on the S2 BHCA.

Depth Control for AUV Based on RS-LSSVM

Aimed at uncertainties and model's impreciseness, nonlinearity and time-variability of depth control system in autonomous underwater vehicle (AUV), a depth predictive control method was put forward based on rough set (RS) and least squares support vector machine (LSSVM). By using RS theory, the monitor data attribute of AUV was reduced to eliminate the redundant information and to improve efficiency. Then, LSSVM model was trained by using the reduced rules, and its parameters were optimized by using chaos theory for the higher accurate control. Taken an AUV typed NPS Phoenix as an example, its depth step response, horizontal rudder and pitch change were simulated. The simulation results show that the method improves the model's accuracy and has better real-time response, fault-tolerant ability, reliability and strong anti-interfere capability.