一种新颖的滑模非线性比例积分混沌控制方法

一种新颖的滑模非线性比例积分控制方法（SMNPIC）被提出用于高精度驱动一类时变不确定混沌系统到任意期望轨道。SMNPIC区别于以前的滑模控制技术之处在于滑动模的一个非线性比例积分函数被包含在控制率中，因此不仅稳态误差和高频抖振都被减小，同时鲁棒性和快速性也能被保证。此外，SMNPIC实际上能被作为一类非线性PID控制器，不仅仅跟踪误差及其直到n-1阶微分被考虑，而且跟踪误差的积分也被考虑，因此有比传统PID更多的有用信息能被使用，因此更好的动静态特性能被获得。通过不确定Duffing-Holmes系统的仿真实例证实了SMNPIC用于混沌控制能获得好的性能。     

基于扩张状态观测器的永磁同步电机混沌系统自适应滑模控制

针对部分状态不可测的永磁同步电机混沌系统, 结合自适应滑模控制和扩张状态观测器理论, 提出一种基于扩张状态观测器的永磁同步电机自适应混沌控制方法, 取消了系统所有状态完全可测的限制. 通过坐标变换, 将永磁同步电机混沌模型变为更适宜控制器设计的Brunovsky标准形式. 在系统部分状态和非线性不确定项上界均未知的情况下, 基于扩张状态观测器估计系统未知状态及不确定项, 并设计自适应滑模控制器, 保证系统状态快速稳定收敛至零点. 仿真结果表明, 该控制器能够改善滑模控制的抖振问题以及提高系统鲁棒性. 关键词： 永磁同步电机 混沌控制 扩张状态观测器 自适应滑模     

混沌系统的自适应多变量广义预测控制与同步

提出了一种应用带时变遗忘因子的自适应多变量广义预测控制算法(β-MGPC)，对Lorenz，Rossler，Chua电路等典型混沌系统进行控制，使之跟踪任意给定参考信号，数值仿真表明了该控制算法的良好效果.这种方法的优点在于无需知道混沌系统的精确模型，便可实现混沌系统的大范围的跟踪和同步控制. 关键词： 混沌控制 混沌同步 自适应预测控制 时变遗忘因子     

带有控制输入扰动和死区的混沌系统自适应模糊控制

针对带有非线性输入和控制增益矩阵扰动的未知时变时滞混沌系统,提出了一种自适应模糊变结构控制方法.设计的控制器可以保证跟踪误差收敛到原点的可调邻域内,并且闭环系统所有信号有界.该方法对系统扰动的变化能保持较好的鲁棒性.最后以多卷波混沌系统为例的仿真实验进一步验证了该方法的有效性.     

通用标量混沌信号同步系统及其控制器的backstepping设计

以寻求通用标量混沌信号同步系统为目标,虚拟控制项加权为变量的严格反馈形式变参数系统为对象,采用带有调谐函数的自适应backstepping设计方法,设计了反馈控制器和相应的参数自适应律.采用所给控制器和参数自适应律,可以使通用系统的输出渐近同步于任意已知结构混沌系统的输出.理论与计算机仿真结果均证实了通用标量混沌信号同步系统及其控制器的有效性. 关键词： 混沌 严格反馈 backstepping设计 自适应控制     

基于线性状态反馈的混沌系统全局控制

针对一类混沌系统,通过分析其非线性向量场的结构特征,提出了一种全局快速控制混沌的新方法.利用线性状态反馈,分离出一个稳定的独立状态变量方程,使受控的混沌系统逐渐退化为稳定的线性系统,进而完全消除非线性抖振现象.此方法可应用到Lorenz系统族或具有类似结构的新混沌系统的控制中,相应设计出一种结构简单的控制器,保证闭环系统在原点全局渐近稳定.数值算例验证了所提出方法的快捷性和正确有效性. 关键词： 混沌系统 线性状态反馈 全局稳定     

基于LaSalle不变集定理自适应控制永磁同步电动机的混沌运动

基于LaSalle不变集定理,设计自适应混沌控制器对永磁同步电动机中的混沌进行控制.由于该控制器能自动跟踪平衡点,无需预先知道系统平衡点位置,不需要改变受控系统原有的结构, 因此设计直接简便,控制代价小,易于工程实现.仿真结果表明了该方法的有效性. 关键词： LaSalle不变集定理 自适应律 混沌控制 永磁同步电动机     

不确定混沌系统的径向基函数神经网络反馈补偿控制

对不确定混沌系统控制问题, 研究了一种基于径向基函数神经网络(radial basis function neural network, RBFNN)的反馈补偿控制方法. 该方法首先用RBFNN对混沌系统的动力学特性进行学习, 然后用训练好的RBFNN模型对混沌系统进行反馈补偿控制. 该方法的特点是不需要被控混沌系统的数学模型,可以快速跟踪任意给定的参考信号. 数值仿真试验表明了该控制方法不仅具有响应速度快、控制精度高, 而且具有较强的抑制混沌系统参数摄动能力和抗干扰能力.     

不确定混沌系统的多项式函数模型补偿控制

针对不确定混沌系统控制问题, 研究了一种基于共轭梯度法(conjugate gradient algorithm, CGA)的多项式函数模型 (polynomial-basis-functions model, PBFM)的补偿控制方法. 该方法首先用PBFM对混沌系统的动力学特性进行拟合, 然后用拟合好的PBFM模型对不确定混沌系统进行前馈补偿控制. 该方法的特点是不需要被控混沌系统的数学模型, 可以快速跟踪任意给定的参考信号. 数值仿真试验表明了该方法不仅具有响应速度快、控制精度高, 而且具有较强的抑制混沌系统参数摄动能力和抗干扰能力. 关键词： 混沌控制 多项式函数模型 共轭梯度法     

用基于输入-输出线性化的自适应模糊方法控制混沌系统

用基于输入输出线性化的自适应模糊方法控制了两类混沌系统.它不仅能使混沌系统实现稳定,而且还能实现对混沌吸引子内部任意周期轨迹和外部周期轨迹的有效跟踪.这一方法的重要特点是:不仅不要求混沌系统的精确模型,而且对不容易建立起数学模型而有实际物理意义的混沌系统也能进行有效控制. 关键词：     

一类非线性不确定系统的高阶积分自适应滑模控制

针对一类非线性不确定系统的滑模控制,结合有限时间收敛理论、高阶积分滑模理论与自适应控制理论,提出了一种高阶积分自适应滑模控制方法,改善了传统积分滑模存在的抖振问题。该方法通过对有限时间收敛反馈量的选取和高阶积分滑模面的设计,不但确保了系统状态误差在有限时间内的收敛性,而且还保证了系统具备对不确定性干扰的抑制能力。同时,采用自适应方法估计滑模控制的趋近速率,使得无须已知不确定项的边界范围。仿真结果验证了所提方法的有效性。     

Trajectory Planning of Robot Manipulator Based on RBF Neural Network

Robot manipulator trajectory planning is one of the core robot technologies, and the design of controllers can improve the trajectory accuracy of manipulators. However, most of the controllers designed at this stage have not been able to effectively solve the nonlinearity and uncertainty problems of the high degree of freedom manipulators. In order to overcome these problems and improve the trajectory performance of the high degree of freedom manipulators, a manipulator trajectory planning method based on a radial basis function (RBF) neural network is proposed in this work. Firstly, a 6-DOF robot experimental platform was designed and built. Secondly, the overall manipulator trajectory planning framework was designed, which included manipulator kinematics and dynamics and a quintic polynomial interpolation algorithm. Then, an adaptive robust controller based on an RBF neural network was designed to deal with the nonlinearity and uncertainty problems, and Lyapunov theory was used to ensure the stability of the manipulator control system and the convergence of the tracking error. Finally, to test the method, a simulation and experiment were carried out. The simulation results showed that the proposed method improved the response and tracking performance to a certain extent, reduced the adjustment time and chattering, and ensured the smooth operation of the manipulator in the course of trajectory planning. The experimental results verified the effectiveness and feasibility of the method proposed in this paper.     

基于滑模变结构控制及预测跟踪的光电吊舱控制方法北大核心CSCD

光电吊舱在稳像和目标跟踪过程中存在摩擦力矩、不平衡力矩等干扰力矩,从而影响速度环响应精度;另一方面,吊舱视频跟踪器图像传输和处理造成的延迟也会造成跟踪滞后,因此必须进行延时补偿。提出基于预测跟踪的滑模变结构控制方法,采用微分预测跟踪器实现对视频跟踪器的延迟补偿,采用预测跟踪器估计出的目标运动角速率构成自适应补偿参数,以调整滑模变结构控制量,改进的滑模控制算法在补偿干扰力矩的同时抑制了抖振现象。仿真及实验结果表明:改进的控制方法能够有效补偿干扰力矩和跟踪器延迟造成的误差,相对于传统PID控制,其跟踪误差减小为原来的1/3,并且该算法已经在相关系统上得到应用。     

Smooth control of HIV/AIDS infection using a robust adaptive scheme with decoupled sliding mode supervision

In this paper, a robust adaptive controller subject to decoupled sliding mode controller as a supervisory controller has been implemented on the HIV infection dynamic model. A five-state dynamic model of HIV is utilized which the measurement of the CD4+T cells and the viral load counts are necessary to estimate all its parameters. Decoupled sliding mode control is a variable structure controller having significant and appropriate features, such as best tracking and regulation performance and robustness and elevate the performance of the controller. Generally, due to the importance of applied treatment strategy to mitigate viral escape, sliding mode control is utilized in accordance with PI control to deliver necessary control inputs. To achieve the least possible chattering, effectual methods such as the transfer function is used. To update the gains of PI control, an adaptation law is then employed. The results demonstrate the suitable performance of the controller via providing proper tracking performance, and also, elimination of the chattering problem and decrease the time of treatment. The number of infected CD4+ T-cells and the number of free virus particles can be controlled in less than five days. The proposed controller is capable of controlling the dynamic behavior of the virus concentration for different patients with the same control scheme.     

An observer based asymptotic trajectory control using a scalar state for chaotic systems

A state-observer based full-state asymptotic trajectory control (OFSTC) method requiring a scalar state is presented to asymptotically drive all the states of chaotic systems to arbitrary desired trajectories. It is no surprise that OFSTC can obtain good tracking performance as desired due to using a state-observer. Significantly OFSTC requires only a scalar state of chaotic systems. A sinusoidal wave and two chaotic variables were taken as illustrative tracking trajectories to validate that using OFSTC can make all the states of a unified chaotic system track the desired trajectories with high tracking accuracy and in a finite time. It is noted that this is the first time that the state-observer of chaotic systems is designed on the basis of Kharitonov's Theorem.     

Adaptive tracking control for a class of uncertain chaotic systems

The paper is concerned with adaptive tracking problem for a class of chaotic system with time-varying uncertainty, but bounded by norm polynomial. Based on adaptive technique, it proposes a novel controller to asymptotically track the arbitrary desired bounded trajectory. Simulation on the Rossler chaotic system is performed and the result verifies the effectiveness of the proposed method.     

Adaptive chattering free variable structure control for a class of chaotic systems with unknown bounded uncertainties

A new adaptive control scheme is developed for a class of chaotic systems with unknown bounded uncertainties. It is implemented by using variable structure control. The concept of extended systems is used such that continuous control input is obtained to avoid chattering phenomenon as frequently in the conventional variable structure systems. Furthermore, it is worthy of note that the proposed adaptive control scheme does not involve any information about the bounds of uncertainties. Thus, the limitation of knowing the bounds of uncertainties in advance is certainly released. A numerical simulation is included to verify the validity of the developed adaptive chattering free variable structure control.     

考虑柔性关节的机械臂自适应运动控制策略研究

具有柔性关节的轻型机械臂因其自重轻、响应迅速、操作灵活等优点,取得了广泛应用。针对具有柔性关节的机械臂系统的关节空间轨迹跟踪控制系统动力学参数不精确的问题,提出一种结合滑膜变结构设计的自适应控制器算法。通过自适应控制的思想对系统动力学参数进行在线辨识,并采用Lyapunov方法证明了闭环系统的稳定性。仿真结果表明,该控制策略保证了机械臂系统对期望轨迹的快速跟踪,具有良好的跟踪精度,系统具有稳定性。     

A simple asymptotic trajectory control of full states of a unified chaotic system

A simple full-state asymptotic trajectory control (FSATC) scheme is proposed to asymptotically drive full states of a unified chaotic system (UCS) to arbitrary desired trajectories. The FSATC uses only information, i.e. one state of the UCS. A sinusoidal wave and two chaotic variables are taken as illustrative tracking trajectories to verify that using the proposed FSATC can make full UCS states track desired trajectories with high tracking accuracy in a finite time.