Robust fuzzy 3D path following for autonomous underwater vehicle subject to uncertainties

This paper addresses a three-dimensional (3D) path following control problem for underactuated autonomous underwater vehicle (AUV) subject to both internal and external uncertainties. A two-layered framework synthesizing the 3D guidance law and heuristic fuzzy control is proposed to achieve robust adaptive following along a predefined path. In the first layer, a 3D guidance controller for underactuated AUV is presented to guarantee the stability of path following in the kinematics stage. In the second layer, a heuristic adaptive fuzzy algorithm based on the guidance command and feedback linearization Proportional-Integral-Derivative (PID) controller is developed in the dynamics stage to account for the nonlinear dynamics and system uncertainties, including inaccuracy modelling parameters and time-varying environmental disturbances. Furthermore, the sensitivity analysis of the heuristic fuzzy controller is presented. Against most existing methods for 3D path following, the proposed robust fuzzy control scheme reduces the design and implementation costs of complicated dynamics controller, and relaxes the knowledge of the accuracy dynamics modelling and environmental disturbances. Finally, numerical simulation results validate the effectiveness of the proposed control framework and illustrate the outperformance of the proposed controller as well.     

基于神经网络的水下机器人三维航迹跟踪控制

本文研究了水下机器人三维航迹跟踪控制问题.在充分考虑了模型中不确定水动力系数和外界海流干扰的基础上,提出了基于神经网络的自适应输出反馈控制方法.控制器由3部分组成:基于动态补偿器的输出反馈控制项、神经网络自适应控制项和鲁棒控制项.神经网络所需的自适应学习信号由线性观测器提供.基于Lyapunov稳定性理论证明了控制系统的稳定性.最后针对某AUV进行了空间三维航迹跟踪控制仿真实验,结果表明设计的控制器可以较好地克服时变非线性水动力阻尼对系统的影响,并对外界海流干扰有较好的抑制作用,可以实现三维航迹的精确跟踪.     

基于模糊混合控制的自治水下机器人路径跟踪控制

基于模糊混合控制策略,本文提出了一种用于非线性欠驱动自治水下机器人的鲁棒路径跟踪控制方法.利用Sugeno型模糊推理系统,将PD滑模控制器与非奇异终端滑模控制器光滑连接,构造了模糊混合控制器.它能充分融合这两类控制器的优势,无论系统远离平衡点还是在其附近,都能取得快速收敛的效果.如果,借助于非时间参考量,将该混合控制器用于自治水下机器人路径跟踪控制,将有利于提高它在不确定环境中的跟踪能力.最后,通过仿真计算结果验证了该控制策略的有效性.     

Reduced order H∞ control of an autonomous underwater vehicle

It is well known that the reduced-order (least-order) H_∞ control problem is a rank minimization problem subject to linear matrix inequality (LMI) constraints which is hard to solve due to the non-convexity of the objective and the trace heuristic is simple and efficient one among various heuristics that approximate the rank minimization problem. In this paper, a theoretical insight into the trade-off between the controller order and the performance of the closed-loop system is given. Autopilots (forward speed, heading and depth) for an autonomous underwater vehicle are synthesized based upon the trace heuristic. Simulation results as well as experimental results of heading control show that the reduced-order autopilots are desirable for practical implementation due to their simple structures and slight performance degradation.     

Three-dimensional optimal path planning for waypoint guidance of an autonomous underwater vehicle

In this paper, optimal three-dimensional paths are generated offline for waypoint guidance of a miniature Autonomous Underwater Vehicle (AUV). Having the starting point, the destination point, and the position and dimension of the obstacles, the AUV is intended to systematically plan an optimal path toward the target. The path is defined as a set of waypoints to be passed by the vehicle. Four criteria are considered for evaluation of an optimal path; they are “total length of path”, “margin of safety”, “smoothness of the planar motion” and “gradient of diving”. A set of Pareto-optimal solutions is found where each solution represents an optimal feasible path that cannot be outrun by any other path considering all four criteria. Then, a proposed three-dimensional guidance system is used for guidance of the AUV through selected optimal paths. This system is inspired from the Line-of-Sight (LOS) guidance strategy; the idea is to select the desired depth, presumed proportional to the horizontal distance of the AUV and the target. To develop this guidance strategy, the dynamic modeling of this novel miniature AUV is also derived. The simulation results show that this guidance system efficiently guides the AUV through the optimal paths.     

基于L2干扰抑制的水下机器人三维航迹跟踪控制

为实现自治水下机器人(AUV)的三维航迹跟踪控制,考虑了非线性水动力阻尼对AUV系统的影响和外界海流干扰作用,提出了基于L2干扰抑制的鲁棒神经网络控制方法.该方法基于李雅普诺夫稳定性理论,设计神经网络控制器补偿非线性水动力阻尼和外界的海流干扰,再将神经网络的估计误差当做AUV系统的外部干扰用L2干扰抑制控制器予以消除.最后针对某AUV进行了螺旋线三维下潜跟踪控制仿真实验,结果表明设计的控制器可以较好地克服时变非线性水动力阻尼对系统的影响,并对外界海流干扰有较好的抑制作用,可以实现AUV三维航迹的精确跟踪.     

Design method for a new control system for an autonomous underwater vehicle using linear matrix inequalities

The independent administrative corporation Japan Agency for Marine–Earth Science and Technology (JAMSTEC) has developed a small light autonomous underwater vehicle (AUV) named marine robot experimental 1 (MR-X1).¹ The motion control of MR-X1 is considered in this article. Since the dynamics of MR-X1 mainly depends on its own speed, the motion control is a nonlinear control system. We propose a new controller design method for this system using linear matrix inequalities (LMIs). This algorithm gives a solution as a linear matrix inequality, and can be adapted to solve many LMIs simultaneously. LMIs can be obtained by substituting several speeds into the dynamics of the MR-X1. The proposed controller, which can be derived from the solution of the LMIs, was adapted to MR-X1 and showed good performance in experiments. This work was presented in part at the 11th International Symposium on Artificial Life and Robotics, Oita, Japan, January 23–25, 2006     

自治水下机器人心智模型

自治水下机器人(Autonomous Underwater Vehicle, AUV)在复杂的环境中作业,对其智能水平提出了很高的要求。结合Agent理论的研究成果,在信念 愿望 意图（BDI）逻辑的基础上拓展出自治水下机器人心智逻辑（AML）,用于建立AUV心智模型,在无损AUV自治性和反应性的前提下,增强了其主动性和社会性。定义了AML的语法和语义,证明了系统的可靠性和完备性,给出了AUV的心智活动过程。仿真实验结果证明了AUV心智模型的正确性和有效性。     

多自主水下航行器系统一致性编队跟踪控制

研究了自主水下航行器的编队路径跟踪问题.基于无源性理论与一致性跟踪理论,在仅有部分AUV获取编队速度信息情形下,设计一种分布式控制律,实现了集群AUV的一致性编队跟踪.控制律分为2个部分:一部分基于无源性同步原理,建立了协同误差到跟踪误差的无源性通道;另一部分为一致性协同跟踪控制器,保证每个AUV相对于虚拟领航者的不一致参考信息通过协商达到最终一致状态.文章应用Nested Matrosov定理证明了整个闭环系统的稳定性,仿真结果验证了上述方法的有效性和可行性.     

具有PID反馈增益的自主水下航行器反步法变深控制

本文针对海底地形测绘时自主水下航行器(autonomous underwater vehicle,AUV)的变深控制问题,提出具有PID增益调节的AUV深度控制方法,基于反馈增益的反步法设计控制器,避免了采用传统反步法导致控制器中存在虚拟控制量的高阶导数问题;基于李雅普诺夫稳定性理论设计控制器参数消除了部分非线性项,得到的控制器的线性部分为状态变量的线性组合,具有PID控制器参数调节的形式;针对存在建模不精确、外界干扰和测量噪声时的闭环系统鲁棒性进行分析,保证了误差系统在扰动作用时的一致最终有界性.最后通过仿真实验验证了本文设计的控制器的有效性.     

变质心自主水下航行器定深控制研究

对变质心自主水下航行器（AUV）定深控制问题进行了研究。建立了相应的纵平面的变质心AUV的运动数学模型。在此基础上,基于滑模控制理论设计了变质心控制系统,控制质量块的偏移运动,进而控制AUV定深运动。仿真结果表明：该控制器可满足AUV定深控制的需要,使定深误差趋近于零,并对建模误差具有一定的鲁棒性。     

Design and analysis of an underwater inductive coupling power transfer system for autonomous underwater vehicle docking applications

We develop a new kind of underwater inductive coupling power transfer （ICPT） system to evaluate wireless power transfer in autonomous underwater vehicle （AUV） docking applications. Parameters that determine the performance of the system are systematically analyzed through mathematical methods. A circuit simulation model and a finite element analysis （FEA） sim- ulation model are developed to study the power losses of the system, including copper loss in coils, semiconductor loss in circuits, and eddy current loss in transmission media. The characteristics of the power losses can provide guidelines to improve the effi- ciency of ICPT systems. Calculation results and simulation results are validated by relevant experiments of the prototype system. The output power of the prototype system is up to 45 W and the efficiency is up to 0.84. The preliminary results indicate that the efficiency will increase as the transmission power is raised by increasing the input voltage. When the output power reaches 500 W, the efficiency is expected to exceed 0.94. The efficiency can be further improved by choosing proper semiconductors and coils. The analysis methods prove effective in predicting the performance of similar ICPT systems and should be useful in designing new systems.     

Robust adaptive trajectory tracking control of underactuated autonomous underwater vehicles with prescribed performance

In this paper, a novel robust adaptive trajectory tracking control scheme with prescribed performance is developed for underactuated autonomous underwater vehicles (AUVs) subject to unknown dynamic parameters and disturbances. A simple error mapping function is proposed in order to guarantee that the trajectory tracking error satisfies the prescribed performance. A novel additional control based on Nussbaum function is proposed to handle the underactuation of AUVs. The compounded uncertain item caused by the unknown dynamic parameters and disturbances is transformed into a linear parametric form with only single unknown parameter called virtual parameter. On the basis of the above, a novel robust adaptive trajectory tracking control law is developed using dynamic surface control technique, where the adaptive law online provides the estimation of the virtual parameter. Strict stability analysis indicates that the designed control law ensures uniform ultimate boundedness of the AUV trajectory tracking closed‐loop control system with prescribed tracking performance. Simulation results on an AUV in two different disturbance cases with dynamic parameter perturbation verify the effectiveness of our adaptive trajectory tracking control scheme.     

自主式水下航行器三维路径跟踪的神经网络H∞鲁棒自适应控制方法

本文研究了存在模型不确定以及外界未知扰动情况下的自主式水下航行器(AUV)的三维路径跟踪控制问题. 针对此问题, 首先利用时标分离原理及正交投影Serret-Frenet坐标系建立了描述AUV质心运动及姿态运动的的仿射非线性数学模型. 其次, 在控制器设计中运用神经网络H∞鲁棒自适应算法克服了模型的不确定性及扰动, 同时在控制器设计中利用了主导输入的思想, 降低了闭环系统的复杂度, 减少了实时计算工作量, 便于工程应用. 基于Lyapunov理论的分析保证了系统的稳定性. 仿真结果表明, 路径跟踪控制律可以保证AUV沿期望路径运动, 并且具有良好的动态性能.     

水下机器人广义预测控制算法及能耗问题研究

对水下机器人,本文把输出量的变化作为系统优化目标,提出了一种广义预测控制算法,使其能够抑制各种噪声引起的输出波动,从而减少了系统的能量消耗.水下机器人艏向速度在线控制水池实验的结果验证了在满足设定控制指标的条件下能够抑制水下机器人艏向速度波动和减少能量消耗.     

Experiments in the coordinated control of an underwater arm/vehicle system

The addition of manipulators to small autonomous underwater vehicles (AUVs) can pose significant control challenges due to hydrodynamic interactions between the arm and the vehicle. Experiments conducted at the Monterey Bay Aquarium Research Institute (MBARI) using the OTTER vehicle have shown that dynamical interactions between an arm and a vehicle can be very significant. For the experiments reported in this paper, a single-link arm was mounted on OTTER. Tests showed that for 90-degree, two-second repetitive slews of the arm, the vehicle would move as much as 18 degrees in roll and 14 degrees in yaw when no vehicle control was applied.Using a new, highly accurate model of the arm/vehicle hydrodynamic interaction forces, which was developed as part of this research, a coordinated arm/vehicle control strategy was implemented. Under this model-based approach, interaction forces acting on the vehicle due to arm motion were predicted and fed into the vehicle controller. Using this method, station-keeping capability was greatly enhanced. Errors at the manipulator end point were reduced by over a factor of six when compared to results when no control was applied to the vehicle and by a factor of 2.5 when compared to results from a standard independent arm and vehicle feedback control approach. Using the coordinated-control strategy, arm end-point settling times were reduced by a factor three when compared to those obtained with arm and vehicle feedback control alone. These dramatic performance improvements were obtained with only a five-percent increase in total applied thrust.     

水下机器人高度信息融合与欠驱动地形跟踪控制

研究了配置高度计和多普勒速度计(DVL)的欠驱动水下机器人地形跟踪控制问题.采用Takagi-Sugeno推理方法对高度计和DVL两种传感器的高度信息进行融合,提高了高度信息感知能力.将地形跟踪分为速度控制和深度控制问题,分别使用S面控制方法设计速度控制器和反步法设计欠驱动深度控制器.最后,通过实际海洋实验对研究的方法进行了验证,实验结果表明该文提出的方法是有效的.     

一种基于混沌过程神经元水下机器人运动控制方法

由于系统的强非线性以及不确定性,同时考虑到港湾环境下水声信号的噪声大,水下机器人进行精确作业时的运动控制一直是其实用化过程中困挠人们的问题。过程神经网络是传统神经网络的拓展,它增加了一个对于时间的聚合算子,使网络同时具有时空二维信息处理能力,从而更好地模拟了生物神经元的信息处理机制。水下机器人运动控制系统的输入、输出均是随时间连续变化的过程量。在基本神经元模型上,结合S函数和预先规划思想,建立水下机器人过程神经元运动控制模型,参数学习过程中,将遍历性的渐变混沌噪声引入其中,增强控制器全局优化能力。仿真试验表明,该新型控制模型,对于水下机器人的运动非线性控制器具有设计简单、响应速度快、超调小、鲁棒性好等各种优点。     

基于鲁棒Restless Bandits模型的多水下自主航行器任务分配策略

针对水下监测网络中多自主航行器（AUV）协同信息采集任务分配问题进行了研究。首先，为了同时考虑系统中目标传感器的节点状态与声学信道状态对AUV任务分配问题的影响，构建了水声监测网络系统的综合模型；其次，针对水下存在的多未知干扰因素并考虑了模型产生不精确的情况，基于强化学习理论将多AUV任务分配系统建模为鲁棒无休止赌博机问题（RBP）。最后，提出鲁棒Whittle算法求解所建立的RBP，从而求解得出多AUV的任务分配策略。仿真结果表明，在干扰环境下与未考虑干扰因素的分配策略相比，在系统分别选择1、2、3个目标时，鲁棒AUV分配策略对应的系统累计回报值参数的性能分别提升了5.5%、12.3%和9.6%，验证了所提方法的有效性。     

Hierarchical inversion‐based output tracking control for uncertain autonomous underwater vehicles using extended Kalman filter

In this study, a hierarchical inversion‐based output tracking controller (HIOTC) is developed for an autonomous underwater vehicle (AUV) subject to random uncertainties (e.g., current disturbances, unmodeled dynamics, and parameter variations) and noises (e.g., process and measurement noises). The proposed HIOTC respectively utilizes a combination of feedforward and feedback controls in a hierarchical structure based on the kinematic and dynamic models of the system. Moreover, to obtain uncontaminated or unavailable states for implementing the proposed control law, the extended Kalman filter (EKF) is employed to estimate the system states. Then, the position outputs, orientation, and velocity of the AUV are reached with guaranteed asymptotic stability. The robustness of the proposed HIOTC is verified through injection of random uncertainties into the system model. The closed‐loop stability of the proposed individual subsystems is respectively guaranteed to have uniformly ultimately bounded (UUB) performance based on the Lyapunov stability criteria. In addition, the asymptotic tracking of the overall system is demonstrated using Barbalat's lemma. Finally, the feasibility and effectiveness of the proposed control scheme are evaluated through computer simulations and it is shown that the overall system achieves good asymptotic tracking performance.