严格反馈随机非线性系统的自适应逆最优控制

讨论了自适应逆最优控制问题可解定理，基于Ito微分规则和Backstepping方法，给出了具有标准Wiener噪声扰动和未知定常参数的严格反馈随机非线性系统的全局依概率渐近稳定和自适应逆最优控制策略的设计方法，该方法可同时获得控制律和自适应律。仿真结果表明了控制算法的有效性。     

具有不确定Wiener噪声随机非线性系统的自适应逆最优控制

针对一类具有不确定Wiener噪声扰动和未知定常参数的随机非线性系统,采用随 机微分方程描述系统,基于Backstepping算法,利用随机控制Lyapunov函数,研究了自适 应逆最优控制问题的可解定理,系统地给出了全局依概率渐近稳定和自适应逆最优控制策略 的设计方法.这种方法可同时获得控制律和自适应律,仿真结果表明该控制算法的有效性.     

含有非线性参数化的非完整系统的鲁棒自适应控制

针对一类含有强非线性漂移项和未知非线性参数的非完整系统, 提出了一种全局自适应状态反馈控制策略. 首先通过引入参数分离技术, 将非线性参数化系统转换为似然线性参数化系统. 然后引入反馈支配方法设计全局自适应稳定控制器, 同时, 为了避免系统出现不可控性, 设计了一种开关策略. 所设计的控制器能保证系统状态全局收敛到原点, 且其它信号保持有界. 仿真例子验证了算法的有效性和鲁棒性.     

一类非齐次高阶非线性系统的自适应控制设计

本文讨论了一类具有未知参数的非齐次高阶非线性系统的全局强稳定自适应控制器的设计问题,定义了一个时变未知参数和一列非齐次辅助函数,通过增加辅助函数的积分项的方法并结合自适应控制技术,放宽了目前已有结论中对非线性项的限制,给出了使系统全局强稳定的连续自适应控制器存在的一个充分条件,给出了它的自适应状态反馈控制器的递推设计方法,并通过一个例子验证了文章的理论结果.     

随机非线性系统鲁棒自适应控制的模块化设计

将线性系统中基于估计的模块化设计思想推广到随机非线性系统的控制设计之中,基于Ito∧微分规则和自适应B ackstepp ing算法,设计了全局依概率渐近稳定的输入状态稳定控制器.根据无源性定理,设计了无源辨识器模块,并确保扰动是有界的.仿真结果表明了该控制算法的有效性.     

一类未知参数非线性系统的自适应无源控制

对一类含有未知参数的本质非线性系统,通常的Backstepping方法无法应用.为此,提出一种基于状态反馈的自适应无源控制对策,通过对非线性系统进行反馈无源化控制,设计自适应无源镇定控制器和自适应无源输出跟踪控制器.设计中适当调节自适应无源控制器参数,能够保证闭环系统稳定且所有信号全局一致有界,从而解决了此类含有未知参数非线性系统的稳定性和输出跟踪问题.仿真算例验证了提出控制方案的有效性,表明系统具有较强的稳定性和跟踪特性.     

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.     

基于神经网络的不确定随机非线性时滞系统自适应有界镇定

针对一类不确定严格反馈随机非线性时滞系统的自适应有界镇定问题,利用神经网络参数化和Backstepping方法,提出一种新的且含较少学习参数的神经网络自适应控制策略,以保证系统半全局随机有界.稳定性分析证明闭环系统的所有误差信号概率意义下有界.仿真结果表明所提出控制器设计方法的有效性.     

一类随机非线性系统自适应跟踪的模块化设计

对参数严格反馈形式的非线性系统,在含有未知方差Wiener噪声干扰下,为增进系统的稳定性,采用基于估计的模块化设计思想,研究了其跟踪问题。对方差不确定噪声抑制的控制机制,设计了具有鲁棒稳定特性的输入状态稳定控制器,确保系统满足控制器和辨识器的分离设计。应用Swapping技术,设计滤波器将动态参数模型转化为静态模型,并且运用梯度算法设计了参数自适应律。控制器与辨识器结合最终使跟踪误差在概率意义下收敛,试验证明采用方法有效。     

随机非完整链式系统的自适应状态反馈镇定

基于反步技术,对带有不确定非线性项和不确定非线性系数的随机非完整链式系统,设计了自适应状态反馈镇定器.给出了能够保证系统依概率几乎渐近稳定到平衡点的切换控制.最后用仿真验证了控制器的有效性.     

一类非线性参数系统的鲁棒自适应控制

针对一类具有非线性参数和未知非线性的非线性系统, 提出了一种鲁棒自适应控制设计方法, 该方法能保证所有信号全局一致有界, 并且使所研究的非线性系统的范围大大扩大.     

一类非线性时滞系统自适应控制研究

针对一类非线性时滞系统，本文提出一种自适应控制器的设计方案，采用backstepping和domination方法构建了一个无记忆自适应控制器。放松了对非线性时滞函数的要求(例如全局Lipschitz条件)，实现了对给定目标轨线的全局渐近跟踪，保证了闭环系统所有信号全局一致有界：基于Lyapunov—Krasoviskii泛函方法证明了闭环系统的稳定性。仿真结果说明了这种控制方法的可行性和优点。     

Global adaptive finite‐time stabilization for a class of p‐normal nonlinear systems via an event‐triggered strategy

This article addresses the problem of global adaptive finite‐time control for a class of p‐normal nonlinear systems via an event‐triggered strategy. A state feedback controller is first designed for the nominal system by adding a power integrator method. Then, by the skillful design of adaptive dynamic gain mechanism, a novel event‐triggered controller is constructed for uncertain nonlinear system without homogeneous growth condition. It is proved that the global finite‐time stabilization of p‐normal nonlinear systems is guaranteed and the Zeno phenomenon is excluded. Finally, two examples are presented to indicate the effectiveness of the proposed control scheme.     

Global adaptive tracking for a class of nonlinear time-delay systems

This paper aims to extend the newly developed global adaptive regulation scheme to adaptive tracking for a general class of nonlinear systems with both parameter uncertainties and unknown time delay for all the system states. With a simple yet novel decomposition of some coupling functions derived from the introduction of a reference signal, a backstepping tracking control strategy is proposed which possesses the feature that for each design step, a dynamic gain is introduced to generate an additional term to counteract the system nonlinearities and parameter uncertainties. As a result, a memoryless adaptive state-feedback controller is obtained to achieve the global adaptive tracking.     

Adaptive control of triangular systems with nonlinearparameterization

The paper deals with adaptive control of uncertain nonlinear triangular systems whose uncertainty is characterized by nonlinear parameterization of unknown constant vectors. Robust adaptive control techniques are incorporated in the backstepping control design with flat zones to tackle the nonlinear parameterization together with a novel smooth projection algorithm for parameter estimation. The proposed algorithm guarantees the global boundedness of all the variables and the convergence of the tracking error to any prespecified small limit     

一类具有未建模动态的非线性系统模糊自适应鲁棒控制

针对一类单输入单输出未建模动态不确定非线性系统,提出一种模糊自适应backstepping控制方法.设计中利用模糊逻辑系统逼近系统的未知函数,应用非线性阻尼项抵消系统的非线性不确定项,通过引入一个动态信号克服未建模动态.该模糊自适应控制方法保证了整个闭环系统的有界性,输出信号可调节到零的小邻域内.仿真结果进一步验证了该方法的有效性.     

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.     

Robust adaptive control of a class of nonlinear systems including actuator hysteresis with Prandtl-Ishlinskii presentations

This paper deals with robust adaptive control of a class of nonlinear systems preceded by unknown hysteresis nonlinearities. By using a Prandtl-Ishlinskii model with play and stop operators, we attempt to fuse the model of hysteresis with the available control techniques without necessarily constructing a hysteresis inverse. A robust adaptive control scheme is therefore proposed. The global stability of the adaptive system and tracking a desired trajectory to a certain precision are achieved. Simulation results attained for a nonlinear system are presented to illustrate and further validate the effectiveness of the proposed approach.     

一种基于视觉特性的彩色图像增强算法

为了增强图像暗区域部分,基于人类视觉系统的全局和局部自适应调节原理,提出一种彩色图像增强方法。该方法主要包括全局自适应亮度调节、局部对比度增强和颜色恢复3个部分。全局亮度调节采用直方图非线性自适应拉伸来增强暗区域的亮度;局部对比度增强利用当前点与区域像素之间的关系,调节当前点的亮度,以增强图像局部对比度;通过一种自适应的非线性颜色恢复算法恢复图像色彩。通过大量图像对比实验分析表明,本文方法可以自适应有效快速地实现图像增强。     

Adaptive control of nonlinearly parameterized systems: the smooth feedback case

Studies global adaptive control of nonlinearly parameterized systems with uncontrollable linearization. Using a parameter separation technique and the tool of adding a power integrator, we develop a feedback domination design approach for the explicit construction of a smooth adaptive controller that solves the problem of global state regulation. In contrast to the existing results in the literature, a key feature of our adaptive regulator is its minimum-order property, namely, no matter how big the number of unknown parameters is, the order of the dynamic compensator is identical to one, and is therefore minimal. As a consequence, global state regulation of feedback linearizable systems with nonlinear parameterization is achieved by one-dimensional adaptive controllers, without imposing any extra (e.g., convex/concave) conditions on the unknown parameters.