基于离散滑模预测的欠驱动AUV三维航迹跟踪控制

针对欠驱动自主水下航行器（AUV）的模型不确定和外界海流干扰问题,为了实现欠驱动AUV的三维航迹跟踪控制,采用虚拟向导法建立空间运动误差离散化模型.基于递归滑模思想设计离散滑模预测控制器,利用滚动优化和反馈校正方法补偿了不确定项对滑模预测模型的影响.最后针对某欠驱动AUV进行了空间曲线跟踪控制仿真实验.结果表明,所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,保证了欠驱动AUV三维航迹跟踪系统的鲁棒性,实现了三维航迹的精确跟踪.     

Practical Robust Neural Path Following Control for Underactuated Marine Vessels with Actuators Uncertainties

This note tackles the problem of path‐following control for underactuated ships with actuators uncertainties and unknown environmental disturbances. A novel dynamical virtual ship (DVS) guidance principle is initially developed to deal with the assumption that “the reference path could be generated by a virtual ship”, which is normal but not workable in the current literature. In addition, a practical robust control scheme is proposed using feedforward approximation, dynamic surface control, and robust neural damping techniques. In the algorithm, the strong couplings among state variables, underactuated characteristic and the actuators uncertainties are considered to guarantee semiglobal uniform ultimate boundedness of the closed‐loop system. The outstanding advantages are that the control law has a concise form and is easy to implement in practice.The control inputs of interest are two actuator‐steerable variables: the main engine speed and the rudder angle. Although the uncertainties are compensated by NNs, no adaptive parameters are derived for merits of the robust neural damping technique. Finally, the experiment has demonstrated the effectiveness of the proposed scheme, comparing with the LOS self‐tuning fuzzy control.     

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.     

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.     

基于自适应Backstepping的欠驱动AUV三维航迹跟踪控制

为了实现欠驱动自治水下机器人（AUV）三维航迹跟踪控制,基于非完整系统理论分析了AUV缺少横向推进器时的欠驱动控制系统特性,并验证了欠驱动AUV存在加速度约束不可积性.基于李亚普诺夫稳定性理论,利用自适应Backstepping设计连续时变的航迹点跟踪控制器,以抑制外界海流的干扰.仿真实验表明,所设计的控制器能实现欠驱动AUV对一序列三维航迹点的渐近镇定,并且航迹跟踪的精确性和鲁棒性明显优于PID控制.     

Robust adaptive path following of underactuated ships

Robust path following is an issue of vital practical importance to the ship industry. In this paper, a nonlinear robust adaptive control strategy is developed to force an underactuated surface ship to follow a predefined path at a desired speed, despite the presence of environmental disturbances induced by wave, wind and ocean-current. The proposed controller is scalable and is designed using Lyapunov's direct method and the popular backstepping and parameter projection techniques. Along the way of proving closed-loop stability, we obtain a new stability result for nonlinear cascade systems with non-vanishing uncertainties. Interestingly, it is shown in this paper that our developed control strategy is easily extendible to situations of practical importance such as parking and point-to-point navigation. Numerical simulations using the real data of a monohull ship are provided to illustrate the effectiveness of the proposed methodology for path following of underactuated ships.     

自治飞艇直接自适应模糊路径跟踪控制

针对具有模型不确定和未知外部干扰的自治飞艇, 提出了直接自适应模糊路径跟踪控制方法. 该方法由路径跟踪控制和自适应模糊控制两部分组成. 首先基于飞艇的平面运动模型设计路径跟踪控制律, 包括制导律计算、偏航角跟踪和速度控制3 部分; 然后构造直接自适应模糊控制器逼近路径跟踪控制律中的不确定项. 稳定性分析证明所设计的控制律能使飞艇跟踪给定的期望路径, 跟踪误差收敛到原点的小邻域内. 仿真结果验证了所提出方法的有效性.

     

Model predictive and adaptive neural sliding mode control for three-dimensional path following of autonomous underwater vehicle with input saturation

With model uncertainties and input saturation, a novel control method is developed to steer an underactuated autonomous underwater vehicle that realizes the following of the planned path in three-dimensional (3D) space. Firstly, Serret–Frenet frame is applied as virtual target, and the path following errors model in 3D is built. Secondly, the control method which includes kinematic controller and dynamic controller was presented based on cascade control strategy. The kinematic controller, which is responsible for generating a series of constrained velocity signals, is designed based on model predictive control. The adaptive radial basis function neural network is used to estimate the model uncertainty caused by hydrodynamic parameters. Moreover, sliding mode control technology is applied in the design of dynamic controller to improve its robustness. Then, the control effect is compared with that of LOS guidance law and PID controller by simulation experiment. The comparison results show that the proposed algorithm can improve path following effect and reduce input saturation.

     

Globally Stable Adaptive Dynamic Surface Control for Cooperative Path Following of Multiple Underactuated Autonomous Underwater Vehicles

The cooperative path following problem of multiple underactuated autonomous underwater vehicles (AUVs) involves two tasks. The first one is to force each AUV to converge to the desired parameterized path. The second one is to satisfy the requirement of a cooperative behavior along the paths. In this paper, both of the tasks have been further studied. For the first one, a simplified path following controller is proposed by incorporating the dynamic surface control (DSC) technique to avoid the calculation of derivatives of virtual control signals. Besides, in order to handle the uncertain dynamics, a new type of neural network (NN) adaptive controller is derived, and then an NN based energy‐efficient path following controller is firstly proposed, which consists of an adaptive neural controller dominating in the neural active region and an extra robust controller working outside the neural active region. For the second one, in order to reduce the amount of communications between multiple AUVs, a distributed estimator for the reference common speed is firstly proposed as determined by the communications topology adopted, which means the global knowledge of the reference speed is relaxed for the problem of cooperative path following. The overall algorithm ensures that all the signals in the closed‐loop system are globally uniformly ultimately bounded (GUUB) and the output of the system converges to a small neighborhood of the reference trajectory by properly choosing the design parameters. Simulation results validate the performance and robustness of the proposed strategy.     

基于非线性迭代滑模的欠驱动UUV三维航迹跟踪控制

为实现欠驱动无人水下航行器(Unmanned underwater vehicle, UUV)在未知海流干扰作用下的三维航迹跟踪控制, 提出一种基于工程解耦思想设计的非线性迭代滑模航迹跟踪控制器. 基于虚拟向导的方法,建立UUV空间航迹跟踪误差方程;采用迭代方法设计非线性滑模控制器, 无需对UUV模型参数不确定部分和海流干扰进行估计,避免了舵的抖振现象以及减小了稳态误差与超调问题. 仿真实验表明,设计的控制器对欠驱动UUV系统的模型参数摄动及海流干扰变化不敏感、 且设计参数易于调节,可以实现三维航迹的精确跟踪.     

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.     

基于迭代滑模增量反馈的欠驱动AUV地形跟踪控制

为实现欠驱动自治水下机器人(AUV)在未知海流干扰作用下的地形跟踪控制,提出一种基于非线性迭代滑模增量反馈的航迹跟踪控制器.基于虚拟向导的方法,建立AUV垂直面航迹跟踪误差方程.采用迭代方法,设计滑模增量反馈控制器,无需对AUV模型参数不确定部分和海流干扰进行估计,这样避免了AUV俯仰舵的抖振现象,并且减小了输出反馈控制的稳态误差与超调问题.仿真实验表明,所设计的控制器对AUV系统的模型参数摄动及海流干扰变化不敏感,所设计的参数易于调节.     

欠驱动AUV模糊神经网络L2增益鲁棒跟踪控制

提出基于模糊神经网络欠驱动水下自主机器人(AUV)的L2增益鲁棒跟踪控制方法,该方法通过在线学习逼近动力学模型的不确定项.控制器克服了由于缺少横向推力对跟踪误差的影响,在考虑未知海流干扰情况下,实现了系统对模糊神经网络逼近误差的L2增益小于γ.利用Lyapunov稳定性理论证明了闭环控制系统误差信号一致最终有界.最后,通过精确模型参数和参数扰动仿真实验验证了该控制方法具有很好的跟踪效果和较强的鲁棒性.     

Adaptive robust fuzzy control for path tracking of a wheeled mobile robot

This paper proposes an adaptive robust fuzzy control scheme for path tracking of a wheeled mobile robot with uncertainties. The robot dynamics including the actuator dynamics is considered in this work. The presented controller is composed of a fuzzy basis function network (FBFN) to approximate an unknown nonlinear function of the robot complete dynamics, an adaptive robust input to overcome the uncertainties, and a stabilizing control input. The stability and the convergence of the tracking errors are guaranteed using the Lyapunov stability theory. When the controller is designed, the different parameters for two actuator models in the dynamic equation are taken into account. The proposed control scheme does not require the accurate parameter values for the actuator parameters as well as the robot parameters. The validity and robustness of the proposed control scheme are demonstrated through computer simulations. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Adaptive Proportional-Derivative Sliding Mode Control Law With Improved Transient Performance for Underactuated Overhead Crane Systems

In this paper, an adaptive proportional-derivative sliding mode control (APD-SMC) law, is proposed for 2D underactuated overhead crane systems. The proposed controller has the advantages of simple structure, easy to implement of PD control, strong robustness of SMC with respect to external disturbances and uncertain system parameters, and adaptation for unknown system dynamics associated with the feedforward parts. In the proposed APD-SMC law, the PD control part is used to stabilize the controlled system, the SMC part is used to compensate the external disturbances and system uncertainties, and the adaptive control part is utilized to estimate the unknown system parameters. The coupling behavior between the trolley movement and the payload swing is enhanced and, therefore, the transient performance of the proposed controller is improved. The Lyapunov techniques and the LaSalle's invariance theorem are employed in to support the theoretical derivations. Experimental results are provided to validate the superior performance of the proposed control law.      

Robust adaptive control of nonlinearly parametrized multivariable systems with unmatched disturbances

A new robust adaptive control scheme is developed for nonlinearly parametrized multivariable systems in the presence of parameter uncertainties and unmatched disturbances. The developed control scheme employs a new integrated framework of a functional bounding technique for handling nonlinearly parametrized system dynamics, an adaptive parameter estimation algorithm for dealing with parameter uncertainties, a nonlinear feedback controller structure for stabilization of interconnected system states, and a robust adaptive control design for accommodating unmatched disturbances. It is proved that such a new robust adaptive control scheme is capable of ensuring the global boundedness and mean convergence of all closed‐loop system signals. A complete simulation study on an air vehicle system with nonlinear parametrization in the presence of an unmatched wind disturbance is conducted, and its results verify the effectiveness of the proposed robust adaptive control scheme.     

基于反步自适应神经网络的船舶航迹控制

针对欠驱动船舶在稳定航速条件下轨迹跟踪问题,提出了一种基于自适应神经网络与反步法相结合的控制算法.该算法将实际的欠驱动船舶视为模型完全未知的非线性系统,利用神经网络的函数逼近特性实现控制器中非线性部分的在线估计,采用同时调整输入层-隐层、隐层-输出层间的权值阵的方法进行神经网络权值调整.通过选取积分型Lyapunov函数证明了闭环系统的稳定性.仿真实验表明该控制策略具有良好的跟踪特性,可以实现对期望航迹的精确跟踪.     

非完整移动机械手鲁棒自适应模糊控制

考虑系统存在的参数、外界扰动和未建模动态等不确定性,研究非完整移动机械手的鲁棒自适应控制器设计方法.基于用旋量理论建立的非完整移动机械手的动力学模型,设计了移动平台子系统的运动控制器,然后应用非线性反步控制技术和模糊逻辑系统的通用逼近性,用参数化线性模糊逻辑系统逼近非完整移动机械手动力学模型中的不确定项,基于Lyapu...     

Robust adaptive fuzzy sliding mode trajectory tracking control for serial robotic manipulators

The ever increasingly stringent performance requirements of industrial robotic applications highlight significant importance of advanced robust control designs for serial robots that are generally subject to various uncertainties and external disturbances. Therefore, this paper proposes and investigates the design and implementation of a robust adaptive fuzzy sliding mode controller in the task space for uncertain serial robotic manipulators. The sliding mode control is well known for its robustness to system parameter variations and external disturbances, and is thus a highly desirable and cost-effective approach to achieve high precision control task for serial robots. The proposed controller is designed based on a fuzzy logic approximation to accomplish trajectory tracking with high accuracy and simultaneously attenuate effects from uncertainties. In the controller, the high-frequency uncertain term is approximated by using a fuzzy logic system while the low-frequency term is adaptively updated in real time based on a parametric adaption law. The control efficacy and effectiveness of the proposed control algorithm are comparatively verified against a recently proposed conventional controller. The test results demonstrate that the proposed controller has better trajectory tracking performances and is more robust against large disturbances than the conventional controller under the same operating conditions.     

A time differences of arrival‐based homing strategy for autonomous underwater vehicles

A new sensor‐based homing integrated guidance and control law is presented to drive an underactuated autonomous underwater vehicle (AUV) toward a fixed target, in 3‐D, using the information provided by an ultra‐short baseline (USBL) positioning system. The guidance and control law is first derived at a kinematic level, expressed on the space of the time differences of arrival (TDOAs), as directly measured by the USBL sensor, and assuming the plane wave approximation. Afterwards, the control law is extended for the dynamics of an underactuated AUV resorting to backstepping techniques. The proposed Lyapunov‐based control law yields almost global asymptotic stability (AGAS) in the absence of external disturbances and is further extended, keeping the same properties, to the case where known ocean currents affect the motion of the vehicle. Simulations are presented and discussed that illustrate the performance and behavior of the overall closed‐loop system in the presence of realistic sensor measurements and actuator saturation. Copyright © 2009 John Wiley & Sons, Ltd.