输入受限的混沌系统同步控制

在混沌系统的同步控制中, 由于混沌系统对初始状态的敏感性, 一旦两个混沌系统的状态初值偏差大, 其状态同步往往需要高幅值的控制律来达到, 这给同步控制实现带来了困难, 并且在同步控制中, 两个混沌系统的初始值通常是未知的. 本文考虑控制输入受限情况下的混沌同步控制问题, 基于符号函数的近似表示式, 将受限的控制输入建模为连续可微的光滑函数, 在每一个采样点将同步控制误差系统近似为局部最优线性模型并设计连续型线性二次型调节器(LQR)最优控制律. 为降低混沌同步控制律的幅值和维持同步系统采样时刻之间的动态, 设计了等价的离散最优控制律, 并通过调整LQR性能加权矩阵值, 确保同步控制信号不会超出其受限的上界. 最后对统一混沌模型下的三种不同混沌系统同步控制进行了仿真研究. 仿真结果验证了方法的有效性. 关键词： 统一混沌模型 符号函数 输入受限 同步控制     

Nonlinear dynamics extraction for time-delay systems using modular neural networks synchronization and prediction

It is shown that the nonlinear dynamics of chaotic time-delay systems can be reconstructed using a new type of neural network with two modules: one for nonfeedback part with input data delayed by the embedding time, and a second one for the feedback part with input data delayed by the feedback time. The method is applied to both simulated and experimental data from an electronic analog circuit of the Mackey–Glass system. Better results are obtained for the modular than for feedforward neural networks for the same number of parameters. It is found that the complexity of the neural network model required to reconstruct nonlinear dynamics does not increase with the delay time. Synchronization between the data and the model with diffusive coupling is also achieved. We have also shown by iterating the model from the present point that the dynamics can be predicted with a forecast horizon larger than the feedback delay time.     

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。     

A new approach of optimal control for a class of continuous-time chaotic systems by an online ADP algorithm

We develop an online adaptive dynamic programming （ADP） based optimal control scheme for continuous-time chaotic systems. The idea is to use the ADP algorithm to obtain the optimal control input that makes the performance index function reach an optimum. The expression of the performance index function for the chaotic system is first presented. The online ADP algorithm is presented to achieve optimal control. In the ADP structure, neural networks are used to construct a critic network and an action network, which can obtain an approximate performance index function and the control input, respectively. It is proven that the critic parameter error dynamics and the closed-loop chaotic systems are uniformly ultimately bounded exponentially. Our simulation results illustrate the performance of the established optimal control method.     

Adaptive regulation of uncertain chaos with dynamic neural networks

A novel adaptive control for uncertain nonlinear chaotic system is presented. A dynamical neural networks is used to perform ‘black box' identification. Based on the identifier, the state feedback control is employed to drive the unknown chaotic system toward the desired target. Simulations show the derived control via the neuro-identifier turns out to be very effective.     

不确定混沌系统的回归神经网络同步

将高阶连接的神经元融合到分布式回归神经网络，研究了出现非模型动态性时不确定混沌系统的辨识和同步问题。采用李雅谱诺夫稳定理论对高阶神经网络回归模型的权值进行学习更新，同时，获取整个系统稳定特性的分析结果，而且通过李雅谱诺夫方法设计出消除不确定混沌系统的同步误差的自适应控制律。最后将所提出的方法应用到不确定Rossler混沌系统的建模与同步     

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.     

陈氏混沌系统的自适应控制

通过简单的线性状态反馈方法,分别在系统参数已知和未知的情况下研究了陈氏混沌系统的控制问题.当参数已知时,给出了反馈增益的范围;当参数未知时,设计了一自适应控制器,它克服了一般的自适应控制器中控制律不连续的缺点.通过实验仿真证明了所给方法的有效性. 关键词： 陈氏混沌系统 混沌控制 自适应控制     

A new approach to design a control system for a FGR furnace using the combination of the CFD and linear system identification techniques

A new approach to design control systems for an industrial furnace with flue gas recirculation (FGR) is presented. To facilitate the control system design, a linear dynamic model is needed for the furnace. Full-scale computational fluid dynamics (CFD) simulations are used to generate the required small signal input and output data sets. Subsequently, a least squares based system identification technique is used to obtained the linear dynamic models. After model validation, feedback controller is designed based on these linear dynamic models. Finally, the performance of the designed closed-loop control system is also evaluated using both linear dynamic model and full-scale nonlinear CFD model. The comparison shows that the control system designed using the proposed approach can minimize the deviation of nitric oxides (NO) emission from the design point by minimize the dynamic NO formation, hence to prevent any excessive NO formation in the combustion process when the system subjects to disturbances.     

A nonlinear signal-based control method and its applications to input identification for nonlinear SIMO problems

The problem of control system design can be conceptualised as identifying an input signal to a plant (the system to be controlled) so that the corresponding output matches that of a pre-defined reference signal. Primarily, this problem is solved via well-known techniques based upon the principle of feedback design, an essential component for ensuring stability and robustness of the controlled system. However, feedforward design techniques also have a large part to play, whereby (in the absence of feedback control and assuming that the plant is stable) a model of the plant dynamics can be used to modify the reference signal so that the resultant feedforward input signal generates a plant output signal that is sufficiently close to the original reference signal.     

Quantum State Preparation and Protection by Measurement-Based Feedback Control Against Decoherence

We consider an open quantum system subjected to a noise channel under measurement-based feedback control and two prototypical classes of decoherence channels are considered: phase damping and generalized amplitude damping. Based on quantum trajectory theory, we obtain an extended master equation for the dynamics of the reduced system in the presence of feedback control. For a qubit system we analytically solve this master equation and obtain the solution of the state vector dynamics. Then we propose an effective feedback control scheme for preparing an arbitrary quantum pure state. We also study how to protect two nonorthogonal states effectively, and find that projective measurement with unbiased basis is not optimal for this task, while weak measurement with biased basis could realize the best protection of two nonorthogonal states. Furthermore, the inefficiencies in the feedback process are also discussed.     

Delayed feedback control via minimum entropy strategy in an economic model

In this paper minimum entropy (ME) algorithm for controlling chaos, is applied to the Behrens-Feichtinger model, as a discrete-time dynamic system which models a drug market. The ME control is implemented through delayed feedback. It is assumed that the dynamic equations of the system are not known, so the proper feedback gain cannot be obtained analytically from the system equations. In the ME approach the feedback gain is obtained and adapted in such a way that the entropy of the system converges to zero, hence a fixed point of the system will be stabilized. Application of the proposed method with different economic control strategies is numerically investigated. Simulation results show the effectiveness of the ME method to control chaos in economic systems with unknown dynamic equations.     

Controllability and observability of glucose insulin glucagon system in humans

Artificial pancreas is one of the solutions to control the type1 diabetes mellitus now days. Efforts are being made to develop the artificial pancreas. The controllability and observability of the glucose insulin glucagon dynamical model is calculated which is the modified form of composite model of glucose insulin glucagon dynamics for type 1 diabetes mellitus is considered. The concept of controllability and observability for the linearized control system is used to design a feedback control. This model completely describes the glucagon affects the safety of artificial pancreas to overcome the risk of hyperglycemia. Composite model of glucagon-glucose dynamics and its extension is treated for type 1 diabetes mellitus to check the linear controllability and observability. Two cases are discussed on the basis of input of the system. For case I, insulin as the only input and glucose as an output and in case II insulin and glucagon as inputs and glucose as only output. The model is observable but not controllable in each case. This may show vital part in the progress of fully automatic artificial pancreas and stabilize the control loop system for the glucose insulin glucagon (GIG) pump to control the type 1 diabetes mellitus.     

Chen系统的自适应追踪控制

利用非线性单输入控制器实现混沌Chen系统的自适应追踪控制.根据Chen系统的结构特点选取合适的反馈方式，设计单输入自适应控制器，使Chen系统自适应追踪参数未知的Rossler系统的某一变量，并由Lyapunov直接方法证明误差信号渐近稳定于零.数值仿真结果表明该控制方法可行，且可以实现未知参数的辨识.     

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 network control algorithm based on predictive control. The proposed control system stabilizes the chaotic motion in an unknown chaotic system onto the desired target trajectory. The proposed algorithm is simple and its convergence speed is much higher than existing similar algorithms. The control system can control hyperchaos. We analyze the stability of the control system and prove the convergence property of the neural controller. The theoretic derivation and simulations demonstrate the effectiveness of the algorithm.     

Feedback Induced Instability and Chaos in Semiconductor Lasers and Their Applications

Semiconductor laser with feedback is an excellent model for nonlinear optical system which shows chaotic dynamics. It is interesting not only from the fundamental physical study but also application standpoints of view. The dynamics of feedback induced instability and chaos, especially for optical feedback, and their applications are reviewed in this paper. The model of such a system is described by the laser rate equations. At first the dynamic behaviors of feedback induced instability and chaos in semiconductor lasers are discussed on the basis of the theory and experiments. Instability and chaos may be stabilized by the method of chaos control. Then we apply the method to suppress the noise induced by the feedback in a semiconductor laser. The synchronization of chaos between two similar systems is also an important issue in chaos applications and we discuss secure communications based on chaos synchronization. Some other examples of applications of feedback induced chaos are also described.     

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.     

时滞惯性神经网络的稳定性和分岔控制

针对一类二阶时滞惯性神经网络模型,提出一种基于时滞反馈的分岔控制方法.利用时滞微分方程动力学理论,给出反馈控制系统的稳定性以及发生Hopf分岔的判别条件.数值仿真显示所设计的控制器不仅能有效延迟网络分岔的发生,还能扩大稳定域并改善网络的收敛速度.     

LiNbO3马赫曾德尔调制器任意偏置工作点锁定技术的研究

在传统LiNbO3马赫曾德尔调制器偏置控制理论的基础上,提出了一种基于快速傅里叶变换(FFT)算法和低频小信号扰动的谐波响应反馈控制方法,实现了一种与调制器输入光功率及插入损耗无关、任意工作点可偏置并锁定的精密光电控制系统。利用比例积分微分（PID）控制方法,实现了任意工作点偏移小于0.5°的控制精度,实验结果亦证明该控制系统能够实现强度调制器任意工作点的偏置及锁定。这些结果对于将LiNbO3强度调制器稳定在最佳工作点并实现高速长距光纤通信系统是很有帮助的。     

应用于流管实验装置的声源控制方法

流管试验中由于管道响应不同频率下声压起伏很大,影响测量精度,为此在流管实验装置中发展了一种声源反馈控制方法,以管道内的声压信号为反馈信号,通过改变声源系统中信号源输出信号来调节不同频率下管道内的声压大小。本文分析了反馈次数,反馈信号采集卡量程以及反馈采样时间等因素对反馈控制结果的影响。该方法原理简单,易于实现,其控制思想可应用于其他实验装置的声源控制中。