非线性系统第一类模糊辨识器的设计及仿真

该文针对非线性系统的辨识问题,给出了第一类模糊辨识器的设计方案,该方案通过引入最优逼近误差的自适应律参数项,实时地调整参数来实现对非线性系统的辨识.采用此方法可使辨识器模型的输出很快收敛到真实系统,且辨识误差渐进收敛到零.该文根据此算法编写了便于仿真实现的MATLAB程序,且给出了此程序的解算流程图.最后对Rossler混沌系统的实例进行仿真,绘制了系统真实曲线和辨识器模型输出的估计值曲线,仿真结果说明了该方法在非线性系统辨识中的使用性和可行性.     

基于无功功率的感应电机转速自适应辨识

在感应电机直接转矩控制的基础上,提出了基于无功功率的转速自适应辨识模型,并运用MATLAB/SIMULINK工具进行仿真,仿真结果证明,该速度辨识器具有较好的辨识效果。     

不同阶分数阶混沌系统的同步与参数辨识

针对不确定分数阶混沌系统的同步和参数辨识问题,提出一种新的方法,即用不同阶分数阶系统来同步和参数辨识.利用主动控制和预控制量方法,基于分数阶混沌系统稳定性理论和自适应控制理论,设计控制器,实现不同阶分数阶混沌系统之间的同步和参数辨识.理论和仿真结果实现了不同阶Chen 系统间的同步和辨识,表明了该方法的有效性.     

基于规则熵函数法的结构自适应模糊辨识与控制

提出一种新的结构自适应模糊辨识器,其特点是采用“规则熵函数”法在线调节模糊逻辑系统参数,使规则前件参数趋于一致,从而进行规则合并,简化了系统结构。将该结构自适应模糊辨识器用于一类非线性系统的自适应控制,仿真结果验证了所提出方法的有效性。     

Web服务器上比例延迟保证的自适应控制方法

针对Web服务器一般采用离线辨识,使其反馈方法在Web QoS控制上应用的实时性往往不佳,提出了一种Web服务器自适应参考模型。该模型通过在线辨识,根据对象模型的变化及时更新模型参数和控制器参数,以尽快减小系统误差。最后,通过MATLAB仿真和实际网络测试表明,该控制器不仅在恶劣的网络环境下可维持较好的比例延迟保证,且相比于传统的控制方法具有一定的优越性。     

不确定混沌系统用分数阶系统同步与参数辨识

针对不确定整数阶混沌系统的同步和参数辨识问题,提出一种新的策略即用分数阶混沌系统来同步整数阶混沌系统并实现不确定参数的辨识。首先引入预控制量并利用主动控制构造同步误差方程,然后用分数阶混沌系统稳定性理论和自适应控制理论,设计同步控制器及参数的自适应率,最终实现整数阶混沌系统用分数阶混沌系统同步和参数辨识。数值仿真实现参数不确定整数阶Lorenz系统用分数阶Lorenz系统进行同步和参数辨识仿真,结果表明提出方法的有效性。     

磁悬浮球系统的非线性自适应控制方法

针对磁悬浮球系统被控对象变化时控制器自适应问题,提出了一种反馈线性化和在线参数辨识相结合的非线性自适应控制方法。基于状态反馈精确线性化方法建立磁悬浮球系统的数学模型,通过状态反馈设计了一种非线性控制器,并给出了控制器参数的在线辨识方法。MATLAB平台上在线实验结果表明,与反演滑模自适应控制方法相比,提出的方法无须在平衡位置近似线性化,可以在平衡位置实现对不同对象的自适应控制,且具有理想的稳态调节性能。     

二容水箱的模糊自适应PID控制研究

针对大滞后、非线性的二容水箱实验系统,进行了模糊自适应整定参数的PID控制策略研究.在该方法中,设计了模糊自适应PID控制,推导控制算法,并在MATLAB语言环境下编写仿真控制程序,仿真运行结果表明控制方案的有效性和正确性。     

基于自适应神经模糊推理系统的噪声消除方法

针对信号处理领域噪声消除的实际问题,提出了一种基于模糊推理的自适应神经网络控制方法.通过自适应神经模糊推理系统(ANFIS)对非线性系统的结构和参数进行辨识与自学习,采用混合学习算法,对前向参数和结论参数分别辨识,在提高精度的同时可加快训练收敛的速度,使控制系统具有良好动静态性和鲁棒性,实现了消除通信系统中噪声的目标,最后对基于ANFIS的噪声消除系统进行了建模和仿真,并与自适应神经网络滤波方法的结果对比,其结果证明了该方法的有效性.     

系统辨识及自适应控制系统算法仿真实现

辨识及自适应控制理论在系统建模和控制工程中都得到了广泛应用。结合增广最小二乘法和加权最小方差控制算法,将虚拟仪器技术应用于系统辨识及自适应控制中,通过与Matlab的程序接口调用Matlab子程序对系统响应信号进行计算辨识与控制。系统采用Lab-VIEW和Matlab的混合编程,实现了动态显示和交互分析的有机结合。结果表明,辨识参数误差较小,达到了自适应的控制要求。该仿真可用于工程研究,也可用于教学实验。     

Adaptive leaderless consensus control of uncertain multiagent systems with unknown control directions

This article solves the leaderless consensus problem of a class of uncertain nonlinear multiagent systems with unknown control directions and unknown system parameters. Without using the Nussbaum function approach, a novel control scheme is proposed by means of the switching mechanism. The control algorithm guarantees that consensus errors converge to the origin asymptotically, and the amplitude of the control signals is much smaller compared with those using Nussbaum functions. The simulation results illustrate the effectiveness of the proposed algorithm.     

Indirect adaptive fuzzy control for a class of nonaffine nonlinear systems with unknown control directions

This article presents an indirect adaptive fuzzy control scheme for a class of nonlinear uncertain nonaffine systems with unknown control directions. The nonlinear nonaffine system is first transformed into an affine form by using a Taylor series expansion, and then fuzzy systems are employed to approximate the equivalent affine system’s unknown nonlinearities. By modifying the estimated input control gain and using a novel smooth robust control term, a stable well-defined adaptive controller is proposed. Simulation results are provided to illustrate the efficiency of the proposed scheme.     

Distributed learning and cooperative control for multi-agent systems

This paper presents an algorithm and analysis of distributed learning and cooperative control for a multi-agent system so that a global goal of the overall system can be achieved by locally acting agents. We consider a resource-constrained multi-agent system, in which each agent has limited capabilities in terms of sensing, computation, and communication. The proposed algorithm is executed by each agent independently to estimate an unknown field of interest from noisy measurements and to coordinate multiple agents in a distributed manner to discover peaks of the unknown field. Each mobile agent maintains its own local estimate of the field and updates the estimate using collective measurements from itself and nearby agents. Each agent then moves towards peaks of the field using the gradient of its estimated field while avoiding collision and maintaining communication connectivity. The proposed algorithm is based on a recursive spatial estimation of an unknown field. We show that the closed-loop dynamics of the proposed multi-agent system can be transformed into a form of a stochastic approximation algorithm and prove its convergence using Ljung’s ordinary differential equation (ODE) approach. We also present extensive simulation results supporting our theoretical results.     

Adaptive chaos synchronization in Chua's systems with noisy parameters

Using the Lyapunov stability theory an adaptive control is proposed for chaos synchronization between two Chua systems which have stochastically time varying unknown coefficients. The stochastic variations of the coefficients around their unknown mean values are modeled through Gaussian white noise produced by the Wiener process. It is shown that using the proposed adaptive control the mean square of synchronization error converges to an arbitrarily small bound around zero depending on the controller feedback gain. Simulation results indicate that the proposed adaptive controller has a high performance in synchronization of chaotic Chua circuits in noisy environment.     

不确定切换线性系统状态和未知输入估计方法

针对具有未知输入的不确定切换线性系统, 在平均驻留时间切换下, 讨论其状态和未知输入估计方法. 通过等价变换解耦切换系统的未知输入, 以构造降维切换系统. 进而, 设计切换观测器实现对原系统的状态估计, 并求解具有线性矩阵不等式限制的最优化问题, 得到观测器存在的充分条件. 在基于函数微分数值解方法求得系统输出微分的基础上, 提出一种切换系统未知输入的估计方法. 最后通过一个数值实例验证了所提出方法的有效性.

     

一类非线性时滞系统的自适应模糊动态面控制

针对一类具有未知方向增益函数的严格反馈非线性时滞系统, 提出了一种自适应模糊动态面控制(Dynamic surface control, DSC)算法. 通过利用DSC设计技术和Lyapunov-Krasovskii函数, 该算法不仅克服了计算膨胀的问题, 而且补偿了未知的时滞. 采用Nussbaum函数解决了虚拟控制增益的符号问题, 并且避免了控制器的奇异性. 所设计的控制器保证了闭环系统所有的状态和信号是半全局有界的, 并且通过选择合适的设计参数可使跟踪误差为任意小. 仿真结果表明了所提出控制器的有效性.     

Stable Neuro-Adaptive Control for Robots with the Upper Bound Estimation on the Neural Approximation Errors

An indirect adaptive control approach is developed in this paper for robots with unknown nonlinear dynamics using neural networks (NNs). A key property of the proposed approach is that the actual joint angle values in the control law are replaced by the desired joint angles, angle velocities and accelerators, and the bound on the NN reconstruction errors is assumed to be unknown. Main theoretical results for designing such a neuro-controller are given, and the control performance of the proposed controller is verified with simulation studies.     

Distributed adaptive output consensus control of second-order systems containing unknown non-linear control gains

In this paper, we address the output consensus problem of tracking a desired trajectory for a group of second-order agents on a directed graph with a fixed topology. Each agent is modelled by a second-order non-linear system with unknown non-linear dynamics and unknown non-linear control gains. Only a subset of the agents is given access to the desired trajectory information directly. A distributed adaptive consensus protocol driving all agents to track the desired trajectory is presented using the backstepping technique and approximation technique of Fourier series (FSs). The FS structure is taken not only for tracking the non-linear dynamics but also the unknown portion in the controller design procedure, which can avoid virtual controllers containing the uncertain terms. Stability analysis and parameter convergence of the proposed algorithm are conducted based on the Lyapunov theory and the algebraic graph theory. It is also demonstrated that arbitrary small tracking errors can be achieved by appropriately choosing design parameters. Though the proposed work is applicable for second-order non-linear systems containing unknown non-linear control gains, the proposed controller design can be easily extended to higher-order non-linear systems containing unknown non-linear control gains. Simulation results show the effectiveness of the proposed schemes.     

Bearing parameter identification of rotor–bearing system using clustering-based hybrid evolutionary algorithm

A new bearing parameter identification methodology based on global optimization scheme using measured unbalance response of rotor–bearing system is proposed. A new hybrid evolutionary algorithm which is a clustering-based hybrid evolutionary algorithm (CHEA), is proposed for global optimization scheme to improve the convergence speed and global search ability. Clustering of individuals by using a neural network is introduced to evaluate the degree of mature of genetic evolution. After clustering-based genetic algorithm (GA), local search is carried out for each cluster to judge the convexity of each cluster. Finally, random search is adapted for extrasearching to find a potential global candidate, which could be missed in GA and local search. The proposed methodology can identify not only unknown bearing parameters but also unbalance information of disk by simply setting them as unknown parameters. Numerical example and experimental results were used to verify the effectiveness of the proposed methodology.     

时变时滞非仿射大系统的分散自适应控制

针对一类具有未知时变时滞的非仿射互联大系统基于神经网络的逼近能力, 提出了一种分散自适应神经网络控制方案。该方案利用中值定理对未知非仿射函数进行分离; 利用分离技术和Young's不等式放宽了对未知时滞及时滞互联不确定项的限制, 同时大大减少了在线调节参数的数量。此外, 利用Lyapunov Krasovskii 泛函补偿了未知时滞带来的不确定性。通过理论分析, 证明了闭环系统所有信号是有界的, 输出跟踪误差收敛到原点的一个小邻域内。最后, 仿真结果验证了所提控制方案的有效性。