Adaptive generalized projective synchronization of two different chaotic systems with unknown parameters

This paper presents a general method of the generalized projective synchronization and the parameter identification between two different chaotic systems with unknown parameters. This approach is based on Lyapunov stability theory, and employs a combination of feedback control and adaptive control. With this method one can achieve the generalized projective synchronization and realize the parameter identifications between almost all chaotic （hyperchaotic） systems with unknown parameters. Numerical simulations results are presented to demonstrate the effectiveness of the method.

Precision motion control for electro-hydraulic axis systems under unknown time-variant parameters and disturbances

This article focuses on asymptotic precision motion control for electro-hydraulic axis systems under unknown time-variant parameters,mismatched and matched disturbances.Different from the traditional adaptive results that are applied to dispose of unknown constant parameters only,the unique feature is that an adaptive-gain nonlinear term is introduced into the control design to handle unknown time-variant parameters.Concurrently both mismatched and matched disturbances existing in electro-hydraulic axis systems can also be addressed in this way.With skillful integration of the backstepping technique and the adaptive control,a synthesized controller framework is successfully developed for electro-hydraulic axis systems,in which the coupled interaction between parameter estimation and disturbance estimation is avoided.Accordingly,this designed controller has the capacity of low-computation costs and simpler parameter tuning when compared to the other ones that integrate the adaptive control and observer/estimator-based technique to dividually handle parameter uncertainties and disturbances.Also,a nonlinear filter is designed to eliminate the“explosion of complexity”issue existing in the classical back-stepping technique.The stability analysis uncovers that all the closed-loop signals are bounded and the asymptotic tracking performance is also assured.Finally,contrastive experiment results validate the superiority of the developed method as well.

Synchronization between two different noise-perturbed chaotic systems with unknown parameters

In this paper, a general method of synchronizing noise-perturbed chaotic systems with unknown parameters is proposed. Based on the LaSalle-type invariance principle for stochastic differential equations and by employing a combination of feedback control and adaptive control, some sufficient conditions of chaos synchronization between these noise-perturbed systems with unknown parameters are established. The model used in the research is the chaotic system, but the method is also applicable to the hyperchaotic systems. Unified system and noise-perturbed RSssler system, hyperchaotic Chen system and nolse-perturbed hyperchaotic RSssler system are taken for illustrative examples to demonstrate this technique.

Global exponential synchronization between Lü system and Chen system with unknown parameters and channel time-delay

This paper proposes a nonlinear feedback control method to realize global exponential synchronization with channel time-delay between the Lfi system and Chen system, which are regarded as the drive system and the response system respectiveiy. Some effective observers are produced to identify the unknown parameters of the Lii system. Based on the Lyapunov stability theory and linear matrix inequality technique, some sufficient conditions of global exponential synchronization of the two chaotic systems are derived. Simulation results show the effectiveness and feasibility of the proposed controller.

Hybrid Adaptive Synchronization of Hyperchaotic Systems with Fully Unknown Parameters

In this paper, an adaptive control scheme is developed to study the hybrid synchronization behavior between two identical and different hyperchaotic systems with unknown parameters. This adaptive hybrid synchronization controller is designed based on Lyapunov stability theory and an analytic expression of the controller with its adaptive laws of parameters is shown. The adaptive hybrid synchronization between two identical systems (hyperchaotic Chen system) and different systems (hyperchaotic Lorenz and hyperchaotic systems) are taken as two illustrative examples to show the effectiveness of the proposed method. Theoretical analysis and numerical simulations are shown to verify the results.

Modified Projective Synchronization of a New Hyperchaotic System with Known or Unknown Parameters

This work presents two different methods-nonlinear control method and adaptive control approach to achieve the modified projective synchronization of a new hyperchaotic system with known or unknown parameters.Based on Lyapunov stability theory,nonlinear control method is adopted when the parameters of driving and response systems are known beforehand;when the parameters are fully unknown,adaptive controllers and parameters update laws are proposed to synchronize two different hyperchaotic system and identify the unknown parameters.Moreover,the rate of synchronization can be regulated by adjusting the control gains designed in the controllers.The corresponding simulations are exploited to demonstrate the effectiveness of the proposed two methods.

Adaptive Anti-Synchronization Between Different Chaotic/Hyperchaotic Systems with Fully Unknown Parameters

The anti-synchronization between different chaotic/hyperchaotic systems with fully unknown parameters is considered in detail. Based on Lyapunov stability theory, the adaptive control schemes and parameter update rules are designed in this paper. Two numerical examples show the effectiveness and feasibility of the proposed method.

Unknown parameter’s variance-covariance propagation and calculation in generalized nonlinear least squares problem

The unknown parameter’s variance-covariance propagation and calculation in the generalized nonlinear least squares remain to be studied now, which didn’t appear in the internal and external referencing documents. The unknown parameter’s vari- ance-covariance propagation formula, considering the two-power terms, was concluded used to evaluate the accuracy of unknown parameter estimators in the generalized nonlinear least squares problem. It is a new variance-covariance formula and opens up a new way to evaluate the accuracy when processing data which have the multi-source, multi-dimensional, multi-type, multi-time-state, different accuracy and nonlinearity.

Soft Sensor for Inputs and Parameters Using Nonlinear Singular State Observer in Chemical Processes

Chemical processes are usually nonlinear singular systems.In this study,a soft sensor using nonlinear singular state observer is established for unknown inputs and uncertain model parameters in chemical processes,which are augmented as state variables.Based on the observability of the singular system,this paper presents a simplified observability criterion under certain conditions for unknown inputs and uncertain model parameters.When the observability is satisfied,the unknown inputs and the uncertain model parameters are estimated online by the soft sensor using augmented nonlinear singular state observer.The riser reactor of fluid catalytic cracking unit is used as an example for analysis and simulation.With the catalyst circulation rate as the only unknown input without model error,one temperature sensor at the riser reactor outlet will ensure the correct estimation for the catalyst circulation rate.However,when uncertain model parameters also exist,additional temperature sensors must be used to ensure correct estimation for unknown inputs and uncertain model parameters of chemical processes.

具有未知时变参数城轨列车的精准停车鲁棒控制

列车运行情况复杂多变,使得列车质量、基本运行阻力及线路状况等参数难以精确获得且随着列车运行存在明显的时变特性。列车模型参数的不确定性和时变特性会对列车的精准停车性能产生重要的影响。在动车组多质点动力学模型的基础上,根据Lyapunov再设计方法设计了一种非线性列车鲁棒控制器。该控制器将系统中的不确定性因素视为等价干扰,并设计相应的鲁棒补偿项。根据参数的先验区间,设计的鲁棒补偿项能够抑制模型估计误差,使得列车的位置和速度跟踪误差快速收敛,最终实现精准停车的控制目标;同时,也证明了系统的最终一致有界稳定性。最后,选取城市轨道交通场景进行仿真验证,仿真结果表明:本文提出的控制方法在列车模型参数未知且时变情景下也能实现对期望停车曲线的快速、精确追踪,表现出较强的鲁棒特性。

基于神经网络参数自学习的阻抗控制

针对机器人在打磨过程中环境刚度和位置未知,传统的阻抗控制难以有效保持打磨质量的问题,提出了一种基于调节参数神经网络自学习的阻抗控制。由于基于李雅普诺夫稳定性理论设计的阻尼参数补偿方法中调节参数的选取直接影响系统的控制性能,根据阻尼补偿的数学描述,构建神经网络,用于其参数自适应调节,设计不同的激励函数用于反映阻尼在多种因素影响下变化的特征。通过所搭建的神经网络在线学习,实现参数的优化,以适应打磨过程环境变化。在斜面、平面及曲面等不同环境下,考虑其刚度突变、刚度动态变化、期望力动态变化等因素的仿真实验,结果表明所提出的控制方法与传统控制方法相比,具有更小的超调和稳态误差,并能够适应环境参数未知的情况,明显提高打磨质量和效率。

新生参数未知的ET-GM-PHD编队目标跟踪方法

为了解决多个编队在新生目标参数不明确背景下的目标跟踪问题,提出了一种新生目标相关参数未知的编队目标跟踪算法,它可以有效地提高跟踪性能,从而更好地满足需求。通过采用高斯混合概率假设密度(ET-GM-PHD)滤波框架,对PHD参数进行预测,进而分析各个高斯分量之间的相关性,从而筛选出最优的量测集合,最终消除大多数的噪声干扰。再利用在编队目标运动区域内量测分布密集的特性,通过目标检测方法找到其位置并跟踪。最后,仿真实验表明,在新生目标相关参数未知的情况下,提出的方法能够快速有效地跟踪编队目标,具有较好的跟踪性能。

DETERMINATION OF AN UNKNOWN NONHOMOGENEOUS TERM p(t)IN A STEFAN PROBLEM

A one-phase Stefan problem for the nonhomogeneous heat equation with the source term depending on an unknown parameter p(t) is considered. The existence and uniqueness of the solution (p, s, u) are also demonstrated.

Fixed-Time Adaptive Time-Varying Matrix Projective Synchronization of Time-Delayed Chaotic Systems with Different Dimensions

This paper deals with the fixed-time adaptive time-varying matrix projective synchronization(ATVMPS)of different dimensional chaotic systems(DDCSs)with time delays and unknown parameters.Firstly,to estimate the unknown parameters,adaptive parameter updated laws are designed.Secondly,to realize the fixed-time ATVMPS of the time-delayed DDCSs,an adaptive delay-unrelated controller is designed,where time delays of chaotic systems are known or unknown.Thirdly,some simple fixed-time ATVMPS criteria are deduced,and the rigorous proof is provided by employing the inequality technique and Lyapunov theory.Furthermore,the settling time of fixed-time synchronization(Fix-TS)is obtained,which depends only on controller parameters and system parameters and is independent of the system’s initial states.Finally,simulation examples are presented to validate the theoretical analysis.

时滞取值概率未知下的线性时滞系统辨识方法

在大多数系统辨识方法中,通常假设时变时滞在其可能的取值范围内服从均匀分布.但是这种假设是非常受限的且在实际过程中常常无法得到满足.因此在时滞取值概率条件未知的情况下,针对一类线性时变时滞系统提出有效的辨识方法.利用期望最大化(Expectation maximization,EM)算法将拟研究的辨识问题公式化,期望最大化算法通过不断地迭代执行期望步骤和最大化步骤得到优化的参数估计.在期望步骤中,将未知的时变时滞当作隐含变量来处理并且假设可能的取值范围已知.在每一个采样时刻,时滞的变换由一个概率向量控制,并且该向量中的每一个元素是未知的,将其当作待估计的未知参数处理.在算法的每次迭代过程中,计算时滞的后验概率密度函数(Probability density function,PDF),并在此基础上构造代价函数(Q-函数).在最大化步骤中,通过不断优化(Q-函数)来估计想要的参数,包括模型参数、噪声参数、控制概率向量中的每一个元素和未知的时滞.最后通过一个数值例子验证提出算法的有效性.

改进滑模面的自适应终端滑模混沌同步控制

为了提高混沌同步误差在滑模面上的收敛速度,提出了一种改进的终端滑模面,并设计了自适应终端滑模控制律,改善了混沌同步控制性能。在终端滑模面中引入系统状态量的幂指函数积分项,提高了误差沿滑模面的收敛速度。针对同时考虑系统中存在未知参数、不确定项及外部扰动的混沌系统的同步控制问题,设计了自适应终端滑模控制律,实现了混沌同步系统的滑模控制,并对系统的稳定性进行了证明。将所设计的滑模控制方法应用于混沌同步控制系统中,相比于传统终端滑模控制方法,改进方法能够明显提高误差的收敛速度。仿真实验验证了所提方法的有效性。

基于改进UKF的非线性结构荷载和参数同步识别

传统无迹卡尔曼滤波(unscented Kalman filter,简称UKF)方法在非线性结构参数识别过程中,通常要求结构的输入已知,当非线性结构的输入难以测量或测量误差较大时,该方法的应用将受到限制。为了对未知激励作用下的非线性结构参数进行识别,提出一种基于改进UKF的非线性结构荷载和参数同步识别方法。该方法在系统状态更新过程中,利用结构响应和参数的当前预测值,对输入荷载进行初步估计,并结合系统状态的估计值对输入荷载进行识别。为降低测量噪声对非线性系统识别结果的影响,采用在UKF方法中嵌入卡尔曼滤波器(Kalman filter,简称KF),对测量噪声协方差矩阵进行同步优化,确保非线性结构荷载和参数识别的精确性。分别对地震激励下的单自由度和5自由度Bouc‑Wen滞回模型进行数值模拟,验证了该方法的可行性和准确性。结果表明,改进的UKF方法能够有效地实现非线性结构未知荷载和模型参数的同步识别。

非视距和未知传输参数环境下的RSS-TOA目标定位算法

在未知传输时间和发射功率的情况下,本文研究了非视距环境下基于RSS-TOA混合测量值的目标定位问题。首先为解决非视距误差会显著降低定位性能这一难题,根据最坏情况下的定位性能优化准则和鲁棒最小二乘法准则构建了未知视距和非视距路径分布状态下的目标定位优化问题。然后为了有效解决非凸和高度非线性的原定位问题,采用二阶锥松弛技术将该问题转化为凸优化问题,并通过内点法有效得到最优解。最后仿真结果验证了所提出的定位算法优于已有的定位算法。

阀控非对称液压缸位置控制系统自适应鲁棒控制策略

针对一类模型中存在未知非线性函数以及未知参数的阀控非对称液压缸电液位置系统,设计了一种自适应鲁棒控制策略以提高系统的跟踪精度和鲁棒性能.通过引入动态面技术,液压系统的非线性控制器设计过程可以被极大地简化,同时,传统的反步法所固有的复杂爆炸问题可以被有效地避免.通过设计基于不连续投影方法的自适应律,系统中的未知参数可以被有效地估计并补偿到控制器中.利用Lyapunov稳定性理论对闭环系统的稳定性进行了分析,Simulink的仿真结果表明所提出的算法能够有效地提高系统的跟踪性能.

未知输入条件下的结构物理参数识别研究

研究在输入信息未知条件下识别结构物理参数的问题。根据建筑结构风荷载的作用特点，提出了一类时域识别算法，用于高层建筑结构的结构物理参数识别。文中对算法给出了理论证明。数值分析算例表明：不考虑量测噪声时，文中方法可给出结构参数的精确识别结果；