船舶航向非线性H∞逆优化输出反馈控制

为抑制船舶航向非线性优化控制中模型参数摄动和由状态观测器引入的不确定观测误差,提出了一种非线性H∞逆优化控制算法.首先,基于无源理论设计观测器以实现海浪滤波,该观测器无需海浪扰动的方差信息从而减少了观测器参数数量.然后,考虑模型参数摄动对观测误差的影响,给出了描述系统局部(全局)性态的局部(全局)H∞优化性能指标.在以广义黎卡提方程(GARE)对局部优化问题的求解的基础上,应用逆优化方法将全局H∞优化问题转化为构造闭环系统的Lyapunov函数问题,得到同时满足两种指标的优化控制器,并证明了稳定性.仿真结果证明了该算法的有效性.     

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.     

船舶航向自适应控制算法及仿真

本文介绍了一种以航向最佳跟踪为目标的控制参数直接优化的自适应控制算法，避免了对船舶系统参数的直接辨识，并根据船舶运动的数学模型进行了仿真，其结果表明该种算法具有良好的调节性能。     

垂直面欠驱动自治水下机器人定深问题的自适应输出反馈控制

针对垂直面欠驱动自治水下机器人(AUV)定深控制问题,本文仅使用可测量的深度和纵摇角信息,基于反步法设计自适应输出反馈控制器.为此首先设计观测器,实现不可测纵摇角速度反馈;再利用径向基神经网络对不确定水动力系数和纵荡、垂荡及纵摇角速度耦合产生的非线性结构进行补偿;采用自适应策略对纵荡和垂荡速度形成的有界干扰进行抑制.本文采用AUV一阶非完整模型,不以线性化为目的,放宽了纵摇角只能在小范围内变化的限制.最后通过理论证明和仿真实验表明该方法能够实现AUV深度和姿态控制,对未建模非线性动态和有界扰动具有很强的自适应性和鲁棒性.     

非线性严格反馈系统自适应非反步输出反馈控制

针对传统反步控制器设计方法存在复杂度爆炸、参数收敛难、控制奇异、需全系统状态已知等问题,提出一种新的可保证参数收敛的未知系统动态辨识和非反步输出反馈自适应控制方法.首先,通过定义新的状态变量和系统等价变换,将严格反馈系统状态反馈控制转化为标准系统的输出反馈控制,进而设计包含高阶微分器的自适应单步控制器,避免反步递推设计的问题;然后,采用两个神经网络对系统集总未知动态进行估计,避免传统控制方法在未知控制增益在线估计过零引发的奇异问题;最后,构造一种新的自适应算法在线更新神经网络权值确保其收敛到真实值,进而实现对未知系统动态的精准辨识.基于Lyapunov定理的分析表明,跟踪误差和估计误差均可收敛到零点附近紧集.基于液压伺服系统模型的对比仿真验证了所提出方法的有效性和优越性.     

基于自适应滑模的喷水推进船舶航向控制

关于船舶航行优化控制,针对喷水推进船舶的航向稳定性控制问题,根据模型中非线性水动力不确定性和外界干扰,提出了基于PID增益调节的自适应滑模控制方法, 并利用Lyapunov稳定性理论证明控制方法的稳定性.控制方法对于模型参数摄动和外界干扰有较好的鲁棒性,PID增益参数的选择可通过在线自适应学习获得,采用边界层方法对设计的滑模控制器的高频抖振加以合理抑制.以一艘喷水推进船舶为例,进行了航向改变的仿真,结果表明设计的控制器具有船舶操纵的良好动态性能,且具有超调小、鲁棒性强的优点,更加符合船舶航向实时控制的工程要求.     

基于ESO的船舶航向鲁棒自适应控制

针对具有内部未建模动态和外部不确定扰动的水面船舶设计一种鲁棒自适应航向控制器,并同时解决转向过程中的漂角补偿问题.基于二阶非线性Nomoto模型和一阶漂角模型,建立非积分链结构的漂角-航向非线性状态空间模型,将航向控制系统未建模动态与外部不确定扰动合并为复合扰动,应用扩张状态观测器估计模型中的未测量状态和系统复合扰动.基于Lyapunov稳定性理论和自适应反步法设计航向状态反馈控制规律,为避免反步法控制过程中的微分爆炸问题,采用动态面控制技术获取虚拟控制信号的近似导数.所提出的扩张状态观测器和航向控制算法能够保证闭环系统内所有误差信号一致最终有界,提高航向保持和转向过程中的航向跟踪精度.仿真结果验证了所提出的航向控制规律的有效性.     

一类非线性系统的微分平滑反步自适应输出反馈控制

研究了一类含不确定参数且存在未知扰动的严反馈非线性系统输出反馈控制问题,设计了一种新型的反步递推(Backstepping)自适应控制器.为实现输出反馈,设计过程引入了虚拟的全维状态观测器.由于Backstepping的虚拟控制量与未知参数逼近值及其高阶导数有关,为此通过微分平滑算法对原系统进行相应的动态扩展.在稳定性分析中,利用Lyapunov定理,得到了系统全局一致有界稳定的条件,并求出系统的稳态跟踪误差.最后给出的仿真算例验证了本文方法的有效性和可行性.     

船舶动力定位非线性自适应反步控制器设计

针对扰动不确定非线性船舶动力定位问题,提出了一种带观测器的不确定扰动非线性船舶动力定位自适应输出反馈控制.设计了一个非线性观测器,从附有噪声的输出中估计出船舶位置以及运动速度.用滤波后的位置信号,针对扰动不确定非线性船舶设计带观测器的自适应反步控制器,该控制在Backstepping设计方法的基础上引入积分环节,对存在未知参数和动态不确定扰动的船舶能有效的改善系统性能.根据Lyapunov稳定性理论证明所设计的控制器是全局渐近稳定的,仿真结果验证了该方法的有效性.     

机器人自适应PID饱和输出反馈控制

针对机器人系统在仅有位置传感、驱动器饱和、存在建模不确定性及干扰等条件下的轨迹跟踪控制问题,提出了一种新的自适应PID控制方案。采用高精度滤波器估计机器人关节速度,采用带饱和函数的控制器限制输出力矩,采用自适应PID控制器补偿建模不确定性和干扰。通过Lyapunov直接法,证明系统的稳定性。最后以两关节机器人为例,给出仿真实验结果,验证了算法的有效性。     

Adaptive neural output feedback finite-time command filtered backstepping control for nonlinear systems with full-state constraints

In this paper, an adaptive neural finite-time control method via barrier Lyapunov function, command filtered backstepping, and output feedback is proposed to solve the tracking problem of uncertain high-order nonlinear systems with full-state constraints and input saturation. By utilizing the neural network (NN) to approximate unknown nonlinear functions, the finite-time command filters are used to filtering the virtual control signals and get the intermediate control signals in a finite time in the backstepping process. Because there are errors between the output of finite-time command filters and the virtual control signals, the error compensation signals are added to eliminate the influence of filtering errors. Based on the proposed control scheme, the states of the system can be constrained in the predetermined region, all signals in the system are bounded in finite time, and the tracking error can converge to the desired region in finite time. At last, a simulation example is given to show the effectiveness of the proposed control method.     

Adaptive backstepping output feedback control for SISO nonlinear system using fuzzy neural networks

In this paper, a new fuzzy-neural adaptive control approach is developed for a class of single-input and single-output （SISO） nonlinear systems with unmeasured states. Using fuzzy neural networks to approximate the unknown nonlinear functions, a fuzzy- neural adaptive observer is introduced for state estimation as well as system identification. Under the framework of the backstepping design, fuzzy-neural adaptive output feedback control is constructed recursively. It is proven that the proposed fuzzy adaptive control approach guarantees the global boundedness property for all the signals, driving the tracking error to a small neighbordhood of the origin. Simulation example is included to illustrate the effectiveness of the proposed approach.     

执行器有故障的多输入单输出系统的自适应输出反馈控制

对一类控制增益符号未知且执行器有故障的输出反馈多输入单输出非线性系统,提出了一种后推容错控制方案.该方案在系统状态不可量测的情况下,利用Nussbaum函数处理符号未知的常数增益,并通过构造K-滤波器来估计了系统不可量测的状态.在容错控制器设计过程中,引入变能量函数来处理利用虚拟控制律所无法抵消的部分.与现有研宄成果相比,放宽了未知增益需要上下界均为已知的假设条件.最后,通过选取合适的李雅普诺夫函数,证明了闭环系统所有信号半全局一致终结有界,且跟踪误差收敛到原点的一个小邻域内.仿真结果表明了所提控制方法的有效性.     

Adaptive hierarchical control for output feedback systems

In this paper, we address the problem of adaptive hierarchical control for a class of so-called uncertain output feedback systems. The proposed approach is to design an adaptive output interface dynamic by estimating the uncertainties. With the interface connected to the uncertain nonlinear system and a linear abstract system, the system could track approximately the abstraction. Finally, two examples are presented to illustrate our approach.     

Adaptive output feedback quantised tracking control for stochastic nonstrict-feedback nonlinear systems with input saturation

This paper is concerned with the problem of adaptive output feedback quantised tracking control for a class of stochastic nonstrict-feedback nonlinear systems with asymmetric input saturation. Especially, both input and output signals are quantised by two sector-bounded quantisers. In order to solve the technical difficulties originating from asymmetric saturation nonlinearities and sector-bounded quantisation errors, some special technique, approximation-based methods and Gaussian error function-based continuous differentiable model are exploited. Meanwhile, an observer including the quantised input and output signals is designed to estimate the states. Then, a novel output feedback adaptive quantised control scheme is proposed to ensure that all signals in the closed-loop system are 4-moment (2-moment) semi-globally uniformly ultimately bounded while the output signal follows a desired reference signal. Finally, the effectiveness and applicability of the design methodology is illustrated with two simulation examples.     

具有未建模动态系统的自适应动态面输出反馈控制

针对一类具有未建模动态和动态扰动且状态不可量测的非线性系统,利用神经网络逼近未知函数设计K-滤波器重构系统状态,提出一种自适应输出反馈控制策略。通过对未建模动态的新刻画,避免动态信号的引入。采用动态面设计方法,取消理论分析中产生的未知连续函数的估计,降低设计的复杂性。利用Lyapunov方法证明了闭环系统的所有信号是半全局一致终结有界的,并通过仿真结果验证了所提出方案的有效性。     

A passive output feedback controller with wave filter for marine vehicles

An output feedback controller with a wave filter for regulation of nonlinear marine vehicles is derived. Only measurements of position and attitude are needed. Asymptotic stability for the position and attitude around the desired values, and the velocity about zero is proven by applying Lyapunov stability analysis. Even though the wave filter in this paper has a notch filter structure, it is incorporated in the controller such that the system from the vehicle's velocity to the control input is passive. This is opposed to conventional notch filters which are usually designed separately from the controller itself, possibly complicating the stability analysis. Finally, a simulation study of a ship illustrates the design procedure of the controller. © 1998 John Wiley & Sons, Ltd.     

Adaptive output feedback control of uncertain nonlinear systems based on dynamic surface control technique

An adaptive output feedback control approach is studied for a class of uncertain nonlinear systems in the parametric output feedback form. Unlike the previous works on the adaptive output feedback control, the problem of ‘explosion of complexity’ of the controller in the conventional backstepping design is overcome in this paper by introducing the dynamic surface control (DSC) technique. In the previous schemes for the DSC technique, the time derivative for the virtual controllers is assumed to be bounded. In this paper, this assumption is removed. It can be proven that the resulting closed‐loop system is stable in the sense that all the signals are semi‐global uniformly ultimately bounded and the system output tracks the reference signal to a bounded compact set. A simulation example is given to verify the effectiveness of the proposed approach. Copyright © 2011 John Wiley & Sons, Ltd.     

Adaptive output feedback tracking control of stochastic nonlinear systems with dynamic uncertainties

In this paper, adaptive output feedback tracking control is developed for a class of stochastic nonlinear systems with dynamic uncertainties and unmeasured states. Neural networks are used to approximate the unknown nonlinear functions. K‐filters are designed to estimate the unmeasured states. An available dynamic signal is introduced to dominate the unmodeled dynamics. By combining dynamic surface control technique with backstepping, the condition in which the approximation error is assumed to be bounded is avoided. Using It ô formula and Chebyshev's inequality, it is shown that all signals in the closed‐loop system are bounded in probability, and the error signals are semi‐globally uniformly ultimately bounded in mean square or the sense of four‐moment. Simulation results are provided to illustrate the effectiveness of the proposed approach. Copyright © 2014 John Wiley & Sons, Ltd.