A novel tracking control approach for unmanned underwater vehicles based on bio-inspired neurodynamics

A novel hybrid control approach is presented for trajectory tracking control of unmanned underwater vehicles in this paper. The kinematic and dynamic controllers are integrated by the proposed control strategy. The paper has two objectives. Firstly, an improved backstep method is proposed to generate the virtual velocity using a bio-inspired neurodynamics model in the kinematic controller. The bio-inspired neurodynamics model is intended to smooth the virtual velocity output to avoid speed jumps of the unmanned underwater vehicle caused by tracking errors and to meet the thruster control constraints. Secondly, a new sliding-mode method is added to the dynamic controller, which is robust against parameter inaccuracy and disturbances. The combined kinematic–dynamic control law is applied to the trajectory tracking problem of two different types of unmanned underwater vehicle. Finally, simulation results illustrate the performance of the proposed controller.     

船舶航向非线性系统的自适应动态面控制

针对船舶航向控制非线性系统模型中存在的不确定性和外界干扰的影响,采用动态面控制算法设计了一种鲁棒自适应控制器.由于在反步法设计过程中加入了一阶低通滤波器使得该方法无需对模型非线性多次微分,因而设计方法简单.所设计的鲁棒自适应控制器不仅能保证闭环系统的半全局渐近稳定,使得输出渐近跟踪期望轨迹;而且,跟踪误差可以通过控制器的设计参数加以调整,同时该算法还能克服可能存在的控制器奇异值问题.数字仿真结果表明,控制系统对给定航向的跟踪具有良好的动态特性,对系统的不确定性,具有较强的鲁棒性.     

基于ESO和动态逆的欠驱动船舶航迹跟踪控制设计

针对欠驱动船舶航迹跟踪控制问题,考虑船舶动态不确定性、未知时变外部扰动和速度不可测的情况,将输出重定义方法、扩张状态观测器(extended state observer, ESO)和动态逆控制方法相结合,设计欠驱动船舶航迹跟踪控制律。输出重定义方法用来解决系统欠驱动问题；构造ESO,估计由船舶动态不确定性、未知时变外部扰动以及船舶各自由度运动状态变量间的耦合构成的总扰动和船舶速度；基于上述,采用动态逆控制方法,设计航迹跟踪控制律,使欠驱动船舶跟踪期望航迹,并保证航迹跟踪闭环控制系统所有信号最终一致有界。以一艘欠驱动船舶为例进行仿真研究,仿真结果验证了所设计的航迹跟踪控制律的有效性和优越性。     

欠驱动自主水下航行器的镇定及跟踪

针对仅带有轴向推力及偏航力矩的欠驱动自主水下航行器（AUV）,研究了其在水平面内的轨迹跟踪及定点调节问题。基于Lyapunov直接法及串接一反步技术,通过采用一种带有动力学震荡器的跟踪误差变换,设计了一种统一的连续时变状态反馈控制律,并给出了参数自适应更新律以估计AUV的非线性阻力参数,使得AUV的位置及方向角的跟踪误差全局渐近收敛于零点左右的一个邻域内,该区域可以为任意小,并且,AUV的跟踪性能与外界干扰的大小无关。仿真结果证明所提出的方法是有效的。     

未知时变扰动和输入饱和下的智能船舶鲁棒非线性控制

复杂海况下环境多变并且船舶具有多耦合、强非线性的特点，针对智能船舶定位控制问题，考虑在未知时变扰动和输入饱和约束之下船舶的定位控制问题，结合非线性扰动观测器提出一种带辅助动态系统的鲁棒非线性控制算法。通过Lyapunov理论证明了所提出的非线性扰动观测器与控制器结合后闭环系统的稳定性和信号的一致最终有界性。利用非线性扰动观测器对环境中存在的海浪扰动进行有效的估计处理。最后，通过仿真验证了所提出的控制算法不仅能保证船舶期望的位置和艏向，而且提高了控制速度，具有较好的控制性能。     

Nonlinear model predictive dynamic positioning control of an underwater vehicle with an onboard USBL system

This paper presents a novel optimization-based approach for dynamic positioning (DP) of a fully actuated underwater vehicle equipped with an onboard ultrashort baseline transceiver to provide relative position information of two earth-fixed transponders near the vehicle. The DP system error is defined by the transponders’ positions compared to the desired values, which occur at the vehicle’s target pose (position and orientation). The proposed DP strategy is composed of two loops in a hierarchical structure. In the kinematic loop, the nonlinear model predictive control is used to generate the desired velocity by optimizing a cost function of the predictive trajectories under the constraints of velocity and transponder bearings over a limited time horizon. In the dynamic loop, the neural network model reference adaptive control with pseudo control hedging is utilized to ensure the asymptotical convergence of velocity tracking errors in the presence of uncertainties associated with unknown model parameters, currents and thruster dynamics. The effectiveness of the proposed control scheme is illustrated by comprehensive simulations.     

船舶航向模糊滑模控制器的设计与仿真

In considering the characteristic of a rudder,the maneuvers of a ship were described by an unmatched uncertain nonlinear mathematic model with unknown virtual control coefficient and parameter uncertainties.In order to solve the uncertainties in the ship heading control,specifically the controller singular and paramount re-estimation problem,a new multiple sliding-mode adaptive fuzzy control algorithm was proposed by combining Nussbaum gain technology,the approximation property of fuzzy logic systems,and a multiple sliding-mode control algorithm.Based on the Lyapunov function,it was proven in theory that the controller made all signals in the nonlinear system of unmatched uncertain ship motion uniformly bounded,with tracking errors converging to zero.Simulation results show that the demonstrated controller design can track a desired course fast and accurately.It also exhibits strong robustness peculiarity in relation to system uncertainties and disturbances.     

Design of robust fuzzy controller for ship course-tracking based on RBF network and backstepping approach

1 Introduction1 Recently, adaptive control approaches based on fuzzy neural network (FNN) have been studied in a lot of papers [1–4]. FNN combines the capability of fuzzy reasoning in handling uncertain information and the capability of artificial neural…     

鱼雷型无人水下航行器鲁棒控制器设计

针对鱼雷型无人水下航行器航行过程中外界环境干扰复杂,航行体系统内部干扰严重的特殊问题,并同时考虑系统参数剧烈变化造成的系统不确定性,设计纵向鲁棒控制器。该控制器采用 PID控制器作为标称控制器控制标称受控对象。利用非线性状态观测器估计受控系统中的不确定性和外界环境干扰,并通过补偿控制律补偿,使整个闭环控制系统具有鲁棒性。将此方法应用于鱼雷型无人水下航行器鲁棒控制器的设计,可大大提高鱼雷型无人水下航行器航行过程中对干扰的抑制和对不确定性的适应能力,保证鱼雷型无人水下航行器在整个航行过程中的姿态稳定,以保证航行任务的完成。     

NTSM控制的AUV路径跟踪控制研究

针对非线性欠驱动自治水下机器人（Autonomous underwater vehicle，缩写为AUV），提出了一种基于非奇异终端滑模（Non—singular terminal sliding mode，缩写为NTSM）控制的鲁棒路径跟踪控制方法。在跟踪控制系统中，采用的参考变量为非时间量，摆脱了时间因素的影响，有利于提高AUV在不确定环境中的跟踪能力。应用指数趋近律进行NTSM控制器设计，能保证系统状态在有限时间内到达平衡点。数值仿真结果验证了该控制律的路径跟踪效能。     

水下遥作业系统的协调控制的研究

针对带缆水下机器人和机械手组成的水下遥作业系统协调作用实现预期运动的问题,改进了传统的滑模控制算法,用模糊算法动态调节滑模控制器的指数趋近律的两个参数,达到削弱抖震的效果,较好地跟踪系统的轨迹,实现水下机器人和机械手的协调控制。结果表明基于模糊动态调整滑模指数趋近律的方法能够改进系统的控制性能,在机器人和机械手共同运动时能克服系统的误差和外界扰动,达到较好的控制效果。     

基于多滑模面的船舶航向非线性系统终端滑模模糊控制(英文)

A terminal sliding mode fuzzy control based on multiple sliding surfaces was proposed for ship course tracking steering, which takes account of rudder characteristics and parameter uncertainty. In order to solve the problem, the controller was designed by employing the universal approximation property of fuzzy logic system, the advantage of Nussbaum function, and using multiple sliding mode control algorithm based on the recursive technique. In the last step of designing, a nonsingular terminal sliding mode was utilized to drive the last state of the system to converge in a finite period of time, and high-order sliding mode control law was designed to eliminate the chattering and make the system robust. The simulation results showed that the controller designed here could track a desired course fast and accurately. It also exhibited strong robustness peculiarly to system, and had better adaptive ability than traditional PID control algorithms.     

带不确定动态的欠驱动水面船舶鲁棒自适应路径跟踪控制(英文)

A robust adaptive control strategy was developed to force an underactuated surface vessel to follow a reference path,despite the presence of uncertain parameters and unstructured uncertainties including exogenous disturbances and measurement noise.The reference path can be a curve or a straight line.The proposed controller was designed by using Lyapunov’s direct method and sliding mode control and backstepping techniques.Because the sway axis of the vessel was not directly actuated,two sliding surfaces were introduced,the first one in terms of the surge motion tracking errors and the second one for the yaw motion tracking errors.The adaptive control law guaranteed the uniform ultimate boundedness of the tracking errors.Numerical simulation results were provided to validate the effectiveness of the proposed controller for path following of underactuated surface vessels.     

Online learning control of surface vessels for fine trajectory tracking

This paper presents an adaptive neural network (NN) controller for fine trajectory tracking of surface vessels with uncertain environmental disturbances. Regarding to the new demands for fine trajectory tracking, especially to the requirement of high-accuracy tracking in limited working space, the proposed NN controller is designed to contain a tracking error control component and a velocity error control component, aiming to converge both types of error to zero, separately. It utilizes radial basis functions to approximate a vessel’s unknown nonlinear dynamics. Therefore, there is no need of any explicit knowledge of the vessel. The online learning ability is obtained during the stability analysis using the backstepping technique and the Lyapunov theory. Theoretical results guarantee both the convergence of tracking error and velocity error and the boundedness of NN update. Through simulation and tracking performance study based on the CyberShip II model, the proposed controller is verified effective in fine trajectory tracking.     

深弹舵机电动加载系统的滑模模糊自适应控制研究

针对某型深弹舵机电动加载控制系统存在跟踪精度、参数不确定性和干扰问题,将滑模变结构与模糊自适应控制相结合,设计了一种滑模自适应控制方案。在滑模控制中引入自适应参数调节律和模糊控制规则,采用自适应律实时调节控制器,采用模糊控制消除抖颤。仿真结果表明,滑模模糊自适应控制方法不仅改善了舵机电动加载系统的跟踪精度,而且还有效地消除外界干扰、抑制抖振,具有很强的鲁棒性。     

Global robust and adaptive output feedback dynamic positioning of surface ships

A constructive method was presented to design a global robust and adaptive output feedback controller for dynamic positioning of surface ships under environmental disturbances induced by waves, wind, and ocean currents. The ship’s parameters were not required to be known. An adaptive observer was first designed to estimate the ship’s velocities and parameters. The ship position measurements were also passed through the adaptive observer to reduce high frequency measurement noise from entering the control system. Using these estimate signals, the control was then designed based on Lyapunov’s direct method to force the ship’s position and orientation to globally asymptotically converge to desired values. Simulation results illustrate the effectiveness of the proposed control system. In conclusion, the paper presented a new method to design an effective control system for dynamic positioning of surface ships.     

GDROV运动控制中模糊滑模控制方法的研究

GDROV是用于堤坝探测的水下机器人,设计上属于开架式机器人,其精确的数学模型很难获得.本文采用模糊逻辑与滑模控制相结合的方法,通过模糊逻辑动态调整滑模控制器的指数趋近律的参数,对水下机器人进行控制,解决了滑模控制中所存在的抖振现象和数学模型的不精确问题.首先建立了GDROV的水动力模型,然后在滑模控制的基础上提出用模糊逻辑来动态调整滑模控制器的指数趋近律的参数,最后通过仿真试验和水池试验验证了该控制器对模型的不确定性和外部扰动具有较强的鲁棒性,以及良好的跟踪性.     

基于输入状态线性化的船舶航迹系统鲁棒控制及仿真

本文将Hoo环增益成形鲁棒控制与输入状态精确反馈线性化结合,针对船舶航迹控制系统的非线性数学模型,采用Nomoto船舶模型,给出了一种鲁棒控制器的设计方法。这种算法是对船舶航迹非线性系统进行输入一状态线性化的基础上,然后与闭环增益成形算法相结合而设计出来的控制律。以某船为对象,利用Matlab／Simulink工具箱对新的控制器进行数值仿真,结果表明该控制规律有比较理想的控制效果,在加入扰动后,该控制器能使船舶行驶在预设航迹上,对外界风浪干扰有较强的鲁棒性。     

AUV fuzzy neural BDI

The typical BDI （belief desire intention） model of agent is not efficiently computable and the strict logic expression is not easily applicable to the AUV （autonomous underwater vehicle） domain with uncertainties. In this paper, an AUV fuzzy neural BDI model is proposed. The model is a fuzzy neural network composed of five layers ： input （ beliefs and desires）, fuzzification, commitment, fuzzy intention, and defuzzification layer. In the model, the fuzzy commitment rules and neural network are combined to form intentions from beliefs and desires. The model is demonstrated by solving PEG （pursuit-evasion game） , and the simulation result is satisfactory.     

动力定位船舶全速度路径跟踪CB导引自适应闭环控制

针对仅使用槽道推进器提供横向推力的动力定位船舶路径跟踪控制问题,建立慢变环境干扰影响下的非线性船舶数学模型,设计带有自适应干扰补偿的反步控制算法来消除环境干扰的影响。引入平行目标接近(CB)导引算法为跟踪控制生成期望速度矢量信号,通过与所提出的自适应反步控制算法相结合,得到不受船舶驱动特性限制的全速度范围动力定位船舶导引跟踪控制算法,应用李雅普诺夫稳定性理论证明系统跟踪误差渐进收敛到零。仿真结果表明通过调整导引算法参数可以调节船舶跟踪过程表现,并可以得到较好的控制精度。