欠驱动船舶神经网络自适应路径跟踪控制

针对模型参数未知的欠驱动船舶路径跟踪问题,将神经网络技术与反演设计法相结合,提出一种神经网络稳定自适应控制方法。首先根据运动学误差方程和线性变换确定辅助的前进速度和艏摇角,然后利用神经网络逼近技术对模型中任意不确定因素进行补偿,设计自适应控制律,使得实际的前进速度和艏摇角分别收敛到辅助值。应用Lyapunov函数证明了船舶路径跟踪闭环系统的误差信号最终一致有界。仿真结果表明,利用设计的控制律可以迫使欠驱动船舶跟踪曲线和直线路径,并且具有较强的鲁棒性。     

建模参数不确定的欠驱动船舶路径跟踪控制

针对欠驱动船舶路径跟踪控制中,建模参数时变引起的不确定性问题,提出一种非线性动态神经模糊控制算法。该算法在控制过程中同时调整控制器结构和参数,能确保船舶几何位置的准确跟踪并克服不确定性的影响。仿真结果验证了算法的有效性。     

一类欠驱动系统的自适应跟踪控制器设计北大核心CSCD

针对一类具有不确定参数的欠驱动系统,提出了一种自适应跟踪控制设计方法。为了解决模型的不确定参数,利用了模型的相似性结构信息合成了自适应跟踪控制器,并且基于Lyapunov稳定性理论证明了该自适应跟踪控制器能够保证系统跟踪误差渐近收敛到零以及涉及到的其他变量有界。最后的数值仿真结果验证了所提方法的有效性及可行性。     

基于自适应扰动观测器的自主船舶协同路径跟踪控制

为实现未知环境扰动下不确定欠驱动自主船舶的协同路径跟踪控制,本文提出了一种基于自适应扰动观测器的鲁棒控制算法.该算法采用径向基函数神经网络(RBFNNs)逼近模型参数不确定,并利用最小学习参数化(MLP)技术对神经网络的权重及逼近误差进行压缩,所设计观测器不需要环境扰动上界的精确信息.进一步,基于代数图论对船间通信进行建模,设计了一种分散式协同控制律,有效地降低了通信负载.凭借Lyapunov稳定性理论证明了闭环系统内信号的有界性,且能通过对设计参数的调节使跟踪误差的收敛界为任意小.最后采用数值仿真试验验证了所提出算法的有效性和优越性.     

欠驱动飞艇平面路径跟踪控制

针对欠驱动飞艇模型,提出一种基于制导向量场的平面路径跟踪控制方法.首先,基于牛顿-欧拉方程建立欠驱动飞艇动力学模型;然后,基于向量场理论构造制导向量场以获得期望偏航角,结合反步法设计路径跟踪控制律,并通过稳定性分析证明所设计的控制律能够使路径跟踪误差收敛到零而且闭环系统状态有界;最后,通过仿真对比了所提出方法与已有方法的控制效果,仿真结果验证了所提出方法的有效性和优越性.     

欠驱动船舶的运动规划和全局指数跟踪控制

针对目前欠驱动船舶航迹跟踪控制难以实现跟踪任意可行航迹问题,提出一种运动规划方法。利用多项式拟合,并结合船舶动力学模型,通过离散期望点规划出操作性可实现的全部期望姿态。同时,为实现欠驱动船舶的航迹快速跟踪控制,提出一种全局指数航迹跟踪控制律。引入微分同胚变换,建立两个级联的子系统构成的航迹跟踪误差动态方程;基于反步法的设计原理,运用Lyapunov直接方法对变换后的误差系统设计了全局指数航迹跟踪控制律。仿真结果验证了所提出的全局指数航迹跟踪控制律能够有效实现跟踪任意可行航迹。     

欠驱动飞艇三维路径跟踪控制

针对欠驱动飞艇的路径跟踪控制问题,提出了一种基于制导向量场的三维路径跟踪控制方法.首先,引入向量场理论.接着基于牛顿–欧拉方程建立欠驱动飞艇动力学模型.基于所提模型和向量场理论构造制导向量场以获得期望姿态角和期望速度.然后结合反步法和PD控制设计路径跟踪控制器,用指令滤波器对控制器设计过程中虚拟控制的导数进行估计,避免了复杂的解析计算.所设计的控制器是由制导向量场子系统、姿态稳定环和速度跟踪环组成的内外环结构.稳定性分析证明了飞艇的路径跟踪误差最终一致有界.最后仿真结果验证了所提出方法的有效性.     

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.     

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.     

Global robust adaptive path following of underactuated ships

We propose a method for designing a global robust adaptive controller that forces an underactuated ship to follow a reference path under both constant and time-varying disturbances induced by waves, wind and ocean-currents. Both linear and nonlinear damping terms are included to cover both low- and high-speed applications. All nonlinear damping coefficients are assumed unknown but lie in a known compact set. The new results are derived using a choice of an appropriate body-fixed frame origin, a smooth approximation of nonsmooth damping terms, several nonlinear coordinate changes, the backstepping technique, and utilization of the ship dynamic structure. Experiments on a model ship illustrate the results.     

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.     

Global tracking for underactuated ships with bounded feedback controllers

In this paper, we present a global state feedback tracking controller for underactuated surface marine vessels. This controller is based on saturated control inputs and, under an assumption on the reference trajectory, the closed-loop system is globally asymptotically stable. It has been designed using a 3 degrees of freedom benchmark vessel model used in marine engineering. The main feature of our controller is the boundedness of the control inputs, which is an essential consideration in real life. In absence of velocity measurements, the controller works and remains stable with observers and can be used as an output feedback controller. Simulation results demonstrate the effectiveness of this method.     

Practical formation control of multiple underactuated ships with limited sensing ranges

This paper presents a constructive method to design cooperative controllers that force a group of N underactuated ships with limited sensing ranges to perform a desired formation, and guarantee no collisions between the ships. These ships do not have an independent actuator in the sway axis. The desired formation is stabilized at any sufficiently smooth reference trajectories, including fixed points and nonadmissible trajectories for the ships. The formation control design is based on several nonlinear coordinate changes, the transverse function approach, the backstepping technique, the Lyapunov direct method, and smooth and p-times differentiable step functions. These functions are introduced and incorporated into novel potential functions to solve the collision avoidance problem without the need of switchings despite the ships’ limited sensing ranges. Simulations illustrate the results.     

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.     

基于动态结构自适应神经网络的不确定时滞系统跟踪控制

针对一类不确定时滞非线性系统,提出一种自适应跟踪控制器.首先采用Lyapunov-Krasovskii函数设计时滞补偿器,并构造其中的参数调节规律.再针对建模误筹及小确定非线性,引入动态结构自适应神经网络,其隐层神经元个数可以随着跟踪误差的增大而在线增加,以提高逼近精度.最后,用仿真示例表明本文所提方法是有效的.