Control of Unknown Chaotic Systems Based on Neural Predictive Control

We introduce the predictive control into the control of chaotic system and propose a neural networkcontrol algorithm based on predictive control. The proposed control system stabilizes the chaotic motion in an unknownchaotic system onto the desired target trajectory. The proposed algorithm is simple and its convergence speed is muchhigher than existing similar algorithms. The control system can control hyperchaos. We analyze the stability of thecontrol system and prove the convergence property of the neural controller. The theoretic derivation and simulationsdemonstrate the effectiveness of the algorithm.     

A New Approach for Controlling Chaos Based on Direct Optimizing Predictive Control

We introduce the predictive control theory into the study of chaos control and propose a direct optimizing predictive control algorithm based on a neural network model. The proposed control system stabilizes the chaotic motion in an unknown chaotic system onto the desired target trajectory. Compared with the existing similar algorithms, the proposed control system has faster response, so it requires much shorter time for the stabilization of the chaotic systems.The proposed approach can control hyperchaos and the algorithm is simple. The convergence of the control algorithm and the stability of the control system can be guaranteed. The theoretic analysis and simulations demonstrate the effectiveness of the algorithm.     

Hénon混沌系统广义预测函数控制快速收敛算法

提出一种具有收敛跟踪性能的Hénon混沌系统广义预测函数控制快速算法.基于已有的广义预测函数控制方法,依据混沌系统运动特性和实际中控制量变化趋势选取特殊形式的基函数,避免了控制律中的矩阵求逆计算,并在性能指标函数中引入确定的前馈增益矩阵,实现Hénon混沌系统对参考信号的收敛跟踪.仿真结果验证了该算法的有效性. 关键词： 广义预测控制 预测函数控制 Hénon混沌系统 前馈增益矩阵     

Control of chaotic systems using an on-line trained linear neural controller

We propose a control system including an on-line trained linear neural controller to control chaotic systems. The control system stabilizes a chaotic orbit onto an unstable fixed point without using the knowledge of the location of the point and the local linearized dynamics at the point. Furthermore, the control system can track the stabilized orbit to the unstable fixed point whose location and local dynamics vary slowly with a variation of the system parameter. This paper extends a previous paper (Konishi and Kokame, 1995) for more general situations and improves the neural controller proposed in the previous paper both to simplify the training algorithm and to guarantee the convergence of the neural controller. The stability analysis of the control system reveals that some unstable fixed points cannot be stabilized in the control system. Numerical experiments show that the control system works well for controlling high-dimensional chaotic systems.     

Hénon混沌系统广义预测控制无静差快速算法

提出了一种具有无静差跟踪性能的Hénon混沌系统广义预测控制快速算法.采用改进的时变遗忘因子递推最小二乘方法辨识混沌系统,通过在常规广义预测控制性能指标函数中引入前馈增益矩阵与柔化矩阵,并将MP神经元网络与BP算法相结合在线调整柔化因子,实现系统对参考信号的无静差快速跟踪.该算法避免了矩阵求逆计算,能够很好地跟踪参考信号.仿真结果验证了该方法的有效性. 关键词： 广义预测控制 Hénon混沌系统 前馈增益矩阵 柔化矩阵     

Hénon混沌系统的广义预测控制与同步快速算法

提出了一种带有终端滑模等式约束的Hénon混沌系统的广义预测控制快速算法.该方法将广义预测控制与离散滑模控制结合起来运用到Hénon混沌系统中，并采用柔化输入信号的方法，避免了广义预测控制算法中的高维矩阵求逆.占用内存小，计算速度快，并且具有较强的跟踪给定信号和抑制系统参数摄动和随机噪声的能力.仿真结果验证了该方法的有效性和快速性. 关键词： 广义预测控制 离散滑模控制 Hénon混沌系统 混沌同步     

非线性系统混沌运动的神经网络控制

设计前馈反传神经网络控制非线性系统混沌运动的新方法.根据扰动参数模型输入输出数据,按照非线性学习算法训练网络产生系统稳定所需的小扰动控制信号,去镇定混沌运动,使嵌入在混沌吸引子中的不稳定周期轨道回到稳定不动点上.Hnon映射数值仿真结果表明,这种方法控制非线性混沌系统响应速度快、控制精度高 关键词： 混沌控制 神经网络 吸引子 非线性     

Control of chaotic systems using targeting by extended control regions method

In this work, the previously proposed extended control regions (ECR) algorithm for targeting is improved by using individual neural networks for each activation region. The improved version, which exploits the short time predictability of the chaotic system more efficiently, gives better performance with respect to training time and average reaching time while maintaining the advantages of the previous method. Moreover, the simulation results revealed that the meaningful number of activation regions of the controller using improved ECR is nearly linearly related with the prediction horizon of the chaotic system to be targeted, which can be used as a criterion for choosing the number of activation region.     

Hénon混沌系统的自适应预测函数控制快速算法

提出了一种带有预测函数的Hénon 混沌系统的广义预测控制快速算法.首先用时变遗忘因子的递推最小二乘方法辨识混沌系统,然后在广义预测控制的基础上引入了预测函数控制方法,并充分利用了预测信息的补偿作用.这种算法克服了广义预测控制中求解逆矩阵的缺点,提高了系统响应的速度,并且具有较强的跟踪给定信号、抑制系统参数摄动和随机噪声的能力.仿真结果验证了该方法的有效性. 关键词： 广义预测控制 预测函数 Hénon 混沌系统 参数辨识     

Hénon混沌系统的自适应广义预测控制快速算法

提出了一种带有约束矩阵的Hénon 混沌系统的广义预测控制快速算法,首先用带有遗忘因子的递推最小二乘参数辨识法来逼近混沌系统,然后该算法引入了一种约束矩阵,约束矩阵的引入避免了矩阵求逆的计算,占用内存小,计算速度快,并且具有较强的跟踪给定信号和抑制系统参数摄动和随机噪声的能力.仿真结果验证了该方法的有效性. 关键词： 广义预测控制 约束矩阵 Hénon混沌系统 参数辨识     

Chaotic system optimal tracking using data-based synchronous method with unknown dynamics and disturbances

We develop an optimal tracking control method for chaotic system with unknown dynamics and disturbances. The method allows the optimal cost function and the corresponding tracking control to update synchronously. According to the tracking error and the reference dynamics, the augmented system is constructed. Then the optimal tracking control problem is defined. The policy iteration(PI) is introduced to solve the min-max optimization problem. The off-policy adaptive dynamic programming(ADP) algorithm is then proposed to find the solution of the tracking Hamilton–Jacobi–Isaacs(HJI) equation online only using measured data and without any knowledge about the system dynamics. Critic neural network(CNN), action neural network(ANN), and disturbance neural network(DNN) are used to approximate the cost function, control, and disturbance. The weights of these networks compose the augmented weight matrix, and the uniformly ultimately bounded(UUB) of which is proven. The convergence of the tracking error system is also proven. Two examples are given to show the effectiveness of the proposed synchronous solution method for the chaotic system tracking problem.     

混沌系统的遗传神经网络控制

提出遗传神经网络控制混沌新方法.将小扰动技术和周期控制技术结合起来，用遗传算法训练神经网络，使之成为混沌控制器.对Henon映射和Logistic映射的仿真结果说明控制器能产生小扰动控制序列信号，将混沌振荡转变成规则运动状态.该方法无需了解动态系统数学模型，具有一定抗噪声干扰能力，可将它推广应用到其他混沌系统的控制中. 关键词： 遗传算法 神经网络 混沌 周期控制     

基于快速全线性预测控制的混沌系统控制与同步

针对连续时间混沌(超混沌)系统的控制问题, 提出了一种基于扩张状态观测器的快速全线性广义预测控制算法. 利用线性扩张状态观测器估计和补偿混沌(超混沌)系统的非线性动力学和存在的不确定性, 将原始对象近似转化为积分器形式, 随后针对单积分器设计广义预测控制, 解决了预测控制计算量大的问题. 阶跃系数矩阵可以直接得到解析解, 而对于未来输出的预测则可以根据最近两个时刻的输出采样值直接计算得到, 避免了使用自校正算法和在线求解丢番图方程. 该线性算法可以直接应用于非线性对象的控制系统设计. 将该算法应用于典型Lorenz混沌系统的控制中, 数学仿真结果验证了有效性.     

Policy iteration optimal tracking control for chaotic systems by using an adaptive dynamic programming approach

A policy iteration algorithm of adaptive dynamic programming(ADP) is developed to solve the optimal tracking control for a class of discrete-time chaotic systems. By system transformations, the optimal tracking problem is transformed into an optimal regulation one. The policy iteration algorithm for discrete-time chaotic systems is first described. Then,the convergence and admissibility properties of the developed policy iteration algorithm are presented, which show that the transformed chaotic system can be stabilized under an arbitrary iterative control law and the iterative performance index function simultaneously converges to the optimum. By implementing the policy iteration algorithm via neural networks,the developed optimal tracking control scheme for chaotic systems is verified by a simulation.     

混沌系统控制与同步的细菌觅食和差分进化混合算法

将混沌系统的控制与同步问题转化为函数优化问题,提出CDEM算法.把差分进化算法与细菌觅食算法相融合,提高DE算法的收敛性,并利用遗传变异算子保持种群多样性;利用CDEM算法求解混沌系统的控制与同步问题.以典型的离散混沌系统Hénon Map为例进行数值试验,结果表明算法有效、稳定,并分析各个参数对该算法的影响.     

Approximation-error-ADP-based optimal tracking control for chaotic systems with convergence proof

In this paper, an optimal tracking control scheme is proposed for a class of discrete-time chaotic systems using the approximation-error-based adaptive dynamic programming （ADP） algorithm. Via the system transformation, the optimal tracking problem is transformed into an optimal regulation problem, and then the novel optimal tracking control method is proposed. It is shown that for the iterative ADP algorithm with finite approximation error, the iterative performance index functions can converge to a finite neighborhood of the greatest lower bound of all performance index functions under some convergence conditions. Two examples are given to demonstrate the validity of the proposed optimal tracking control scheme for chaotic systems.     

基于GA参数整定的时变时滞系统灰色预测控制

针对一类数学模型未知且存在时变时滞的复杂系统,提出一种基于遗传算法参数整定的灰色预测控制方法。该方法采用BP神经网络对系统的时变时滞进行辨识,利用灰色预测算法对系统的输出进行预测,进而使用基于遗传算法整定PID控制器对系统进行输出反馈控制。该方法将灰色预测算法与遗传算法相结合,有效提高了控制器的自适应性。通过仿真实例,结果表明该方法能够对具有大时滞、大惯性、模型不确定等特点的复杂系统进行有效地控制。该方法是可行的、有效的。     

混沌系统的变论域模糊控制算法研究

混沌运动和混沌控制受到广泛关注,本文利用模糊控制实现混沌系统的控制.针对模糊控制器精度不高,模糊控制中规则数量与控制精度之间的矛盾,提出了一种基于蚁群优化算法的变论域模糊控制器.通过分析变论域模糊控制中的2类伸缩因子,指出其中1类并不能满足广义伸缩因子条件.提出利用蚁群优化算法对伸缩因子智能寻优的方法,在对蚁群算法改进的基础上,构成一种基于蚁群算法的变论域模糊控制,将设计的控制器用于Duffing混沌系统的控制.仿真结果表明,提出的控制算法在收敛速度和稳态性能上要略优于其他控制方式. 关键词： 混沌 变论域模糊控制 伸缩因子 蚁群优化算法     

Rössler超混沌系统多变量广义预测控制快速算法

提出了一种Rössler超混沌系统多变量广义预测控制快速算法.采用改进的时变遗忘因子递推最小二乘方法辨识混沌系统,通过在常规多变量广义预测控制性能指标函数中引入前馈增益矩阵与柔化矩阵,实现系统对参考信号的快速跟踪.该算法降低了求逆矩阵的维数,从而减少了计算量.仿真结果验证了该方法的有效性和实用性. 关键词： 广义预测控制 Rö ssler超混沌系统 前馈增益矩阵 柔化矩阵     

一种新型广义RBF神经网络在混沌时间序列预测中的研究

提出了一种新颖的广义径向基函数神经网络模型,其径向基函数(RBF)的形式由生成函数确定.然后,给出了易实现的梯度学习算法,同时为了进一步提高网络的收敛速度和网络性能,又给出了基于卡尔曼滤波的动态学习算法.为了验证网络的学习性能，采用基于卡尔曼滤波算法的新型广义RBF网络预测模型对Mackey-Glass混沌时间序列和Henon映射进行了仿真.结果表明,所提出的新型广义RBF神经网络模型能快速、精确地预测混沌时间序列,是研究复杂非线性动力系统辨识和控制的一种有效方法. 关键词： 广义径向基函数神经网络 卡尔曼滤波 梯度下降学习算法 混沌时间序列 预测