基于RBF神经网络的一类不确定非线性系统自适应H控制

基于RBF神经网络提出了一种H_∞ 自适应控制方法.控制器由等效控制器和H_∞ 控制器两部分组成.用RBF神经网络逼近非线性函数,并把逼近误差引入到网络权值的自适应律中用以改善系统的动态性能.H_∞ 控制器用于减弱外部干扰及神经网络的逼近误差对跟踪的影响.所设计的控制器不仅保证了闭环系统的稳定性,而且使外部干扰及神经网络的逼近误差对跟踪的影响减小到给定的性能指标.最后给出的算例验证了该方法的有效性.     

基于模糊观测器的非线性MIMO系统H2/H∞模糊状态反馈控制

研究了非线性系统H₂/H_∞模糊状态反馈控制问题.采用局部线性化方法,用T-S模糊线性模型逼近非线性系统,用模糊观测器重构系统状态.在希望的H_∞干扰抑制约束下,通过最小化H₂控制性能指标,实现了模糊状态反馈次优控制.通过将优化问题转化成2个特征值问题(EVP),应用线性矩阵不等式(LMI)优化方法求解,使问题的求解大大简化.所设计的闭环系统在平衡点是局部二次型稳定的,系统抗扰性能和动态性能均较好.     

基于模糊模型的时滞不确定系统的模糊H∞鲁棒反馈控制

讨论了一类具有状态和控制时滞的不确定非线性系统的模糊H_∞ 状态反馈控制问题. 采用具有时滞的不确定Takagi-Sugeno(T-S)模糊模型对非线性系统进行建模, 提出了一套基于LMI的模糊鲁棒控制器的系统设计方法, 给出了模糊H_∞状态反馈控制器存在的充分条件, 以保证闭环模糊系统渐近稳定并满足从干扰输入到控制输出的H_∞范数界约束. 示例仿真表明了该方法的有效性.     

一类带有时滞的广义系统的H∞控: 一种LMI方法

利用线性矩阵不等式方法研究了一类带有时滞的广义系统的H_∞ 控制问题. 在一定条件下, 一个时滞奇异系统可以转化成由一个微分方程和一个代数方程组成的系统, 基于线性矩阵不等式方法给出了使这类系统H_∞干扰抑制性能指标满足要求的无记忆状态反馈控制设计.     

一类不确定非线性系统的鲁棒H∞控制

在一种工程应用背景之下, 考虑了一类非线性系统的鲁棒H_∞ 控制问题, 不确定性范数有界, 基于HJI不等式研究了状态反馈控制器设计问题, 使得闭环系统满足H_∞ 干扰抑制性能要求.     

基于混合灵敏度函数的H∞控制器参数模糊优化方法

给出了SISO系统H_∞控制器参数优化过程,提出了一种系统性能模糊评价优化设计方法,根据系统动态特性通过模糊优化控制策略动态校正控制器结构参数.结果表明,本方法对模型不确定性具有良好的鲁棒性适应性和动态特性,控制品质优于一般H_∞控制和常规PID,Smith预估器.     

不确定Delta算子系统的鲁棒H∞重构控制

讨论一类含有参数不确定和执行器故障的Delta算子系统鲁棒H_∞ 重构控制设计问题. 通过利用故障检测与隔离(FDI)技术, 在考虑不可检测故障执行器输入为能量有界的干扰信号情形下, 基于H_∞ 干扰抑制的思想, 给出了系统可鲁棒H_∞ 镇定的充分条件. 所设计的控制器可使闭环系统鲁棒稳定, 而且对可允许的不确定性和执行器故障具有一定的H_∞ 性能. 数值例子说明了本文设计方法的有效性.     

不确定模糊Delta算子系统的鲁棒H∞控制

研究了一类T-S模糊Delta算子系统的鲁棒H_∞控制问题.首先利用LMI形式给出了模糊Delta算子系统鲁棒镇定的充分条件,然后构造了可使闭环系统鲁棒稳定且对可允许的参数变化满足H_∞性能的状态反馈控制律.本文结果统一了连续与离散模糊系统的鲁棒H_∞镇定设计结论,数值算例说明了方法的可行性.     

不确定性关联大系统的分散鲁棒状态反馈H_∞控制

研究同时存在系统矩阵、控制输入矩阵的不确定和系统关联不确定变化以及外界干扰的关联大系统的分散H_∞ 控制问题, 提出一种较为全面的局部状态反馈分散H_∞ 控制器的设计方法. 局部控制器的求解只需递推地利用一系列局部代数Riccati方程. 最后通过实例表明该方法的有效性.     

一种基于模糊神经网络的双足机器人混杂控制

针对双足机器人控制问题,提出了一种基于模糊神经网络的混杂控制方法.该种方法将模糊神经网络融入了逆系统和H∞控制方法中,一方面将模糊神经网络的构造误差看作系统的干扰,利用H∞控制对干扰进行抑制.另一方面利用模糊神经网络对系统模型进行逼近,为逆系统的构建和H∞控制率的设计提供了有效的系统信息.本文分析了闭环系统的稳定性问题,证明了在采用本文提出的模糊神经网络和自适应算法后可以抑制L2增益.     

Fuzzy neural network quadratic stabilization output feedback control for biped robots via H/sub /spl infin// approach

A novel fuzzy neural network (FNN) quadratic stabilization output feedback control scheme is proposed for the trajectory tracking problems of biped robots with an FNN nonlinear observer. First, a robust quadratic stabilization FNN nonlinear observer is presented to estimate the joint velocities of a biped robot, in which an H/sub /spl infin// approach and variable structure control (VSC) are embedded to attenuate the effect of external disturbances and parametric uncertainties. After the construction of the FNN nonlinear observer, a quadratic stabilization FNN controller is developed with a robust hybrid control scheme. As the employment of a quadratic stability approach, not only does it afford the possibility of trading off the design between FNN, H/sub /spl infin// optimal control, and VSC, but conservative estimation of the FNN reconstruction error bound is also avoided by considering the system matrix uncertainty separately. It is shown that all signals in the closed-loop control system are bounded.     

基于递归模糊神经网络的机器人鲁棒H_∞跟踪控制

利用递归模糊神经网络来逼近机器人系统中的非线性函数,提出了一种具有自适应能力的H∞控制策略.该控制策略能够减弱机器人系统的外扰,并把模糊神经网络的重构误差对系统的影响控制在指定的范围内.同时又能保证闭环系统的所有信号都是有界的.为了验证基于递归模糊神经网络的H∞控制策略的有效性,将其与计算力矩控制方法进行比较,仿真结果表明,在存在外扰的情况下,所提出的控制策略具有比计算力矩控制方法更好的跟踪性能.     

柔索驱动并联机构的二型模糊神经逆控制

由于建立精确数学模型的困难以及控制过程中各种不确定性的存在, 柔索驱动并联机构的水平调节具有一定的难度. 针对该问题, 提出了一种基于二型模糊神经网络的逆控制方案. 该控制方案中的二型模糊神经网络实现了对水平调节过程逆动态的逼近以及对各种不确定性的处理. 采用迭代最小二乘算法对二型模糊神经网络区间权重进行了优化. 最后, 将基于二型模糊神经网络的逆控制方案在实际的控制对象上进行了实验, 并与其相对应的基于一型模糊神经网络的逆控制方案进行了比较. 实验结果表明所提出的控制方案是有效的且采用二型模糊神经网络时能获得更好的控制效果.     

Fuzzy neural network sliding mode control for long delay time systems based on fuzzy prediction

Delay time, which may degrade the control performance, is frequently encountered in various control processes. The fuzzy neural network sliding mode controller (FNNSMC), which incorporates the fuzzy neural network (FNN) with the sliding mode controller (SMC), is developed to control the long delay system with unknown model based on fuzzy prediction algorithm in the paper. According to the characteristics of the long delay systems, we simulate the manual operating process and predict the delayed error and its derivative based on the information of the input and output variables of the process, and then feedback these prediction values to the FNN and train the FNN with the regulation function by the idea of sliding mode control until the better control results are obtained. The FNNSMC has more robustness due to the abilities of the learning and reasoning and can eliminate the drawbacks of the general SMC, namely the chattering in the control signal and the needing knowledge of the bounds of the disturbances and uncertainties. Simulation examples demonstrate the advantages of the proposed control scheme.     

基于RBF神经网络的一类不确定非线性系统自适应H∞控制

基于RBF神经网络提出了一种H∞自适应控制方法.控制器由等效控制器和H∞控制器两部分组成.用RBF神经网络逼近非线性函数,并把逼近误差引入到网络权值的自适应律中用以改善系统的动态性能.H∞控制器用于减弱外部干扰及神经网络的逼近误差对跟踪的影响.所设计的控制器不仅保证了闭环系统的稳定性,而且使外部干扰及神经网络的逼近误差对跟踪的影响减小到给定的性能指标.最后给出的算例验证了该方法的有效性.     

Adaptive hybrid intelligent control for uncertain nonlinear dynamical systems

A new hybrid direct/indirect adaptive fuzzy neural network (FNN) controller with a state observer and supervisory controller for a class of uncertain nonlinear dynamic systems is developed in this paper. The hybrid adaptive FNN controller, the free parameters of which can be tuned on-line by an observer-based output feedback control law and adaptive law, is a combination of direct and indirect adaptive FNN controllers. A weighting factor, which can be adjusted by the tradeoff between plant knowledge and control knowledge, is adopted to sum together the control efforts from indirect adaptive FNN controller and direct adaptive FNN controller. Furthermore, a supervisory controller is appended into the FNN controller to force the state to be within the constraint set. Therefore, if the FNN controller cannot maintain the stability, the supervisory controller starts working to guarantee stability. On the other hand, if the FNN controller works well, the supervisory controller will be deactivated. The overall adaptive scheme guarantees the global stability of the resulting closed-loop system in the sense that all signals involved are uniformly bounded. Two nonlinear systems, namely, inverted pendulum system and Chua's (1989) chaotic circuit, are fully illustrated to track sinusoidal signals. The resulting hybrid direct/indirect FNN control systems show better performances, i.e., tracking error and control effort can be made smaller and it is more flexible during the design process.     

基于CAFSA-FNN的扭矩加载系统控制研究*

针对磁粉制动器扭矩加载系统的非线性和滞后性,提出了一种基于混沌人工鱼群-模糊神经网络(CAFSA-FNN)PID控制器。该控制器采用基于Mamdani模型的模糊神经网络来整定PID控制器的控制参数,并结合混沌人工鱼群算法离线粗调和BP算法在线细调来学习和调整模糊神经网络的参数。利用Matlab进行离线仿真优化,在此基础上使用PID控制器、模糊神经网络控制器、人工鱼群-模糊神经网络控制器以及本文设计的控制器进行磁粉制动器扭矩加载实验,实验结果证明了该控制器的稳定性、快速性和有效性,能够解决滞后性问题。     

Hybrid Adaptive Type‐2 Fuzzy Tracking Control of Chaotic Oscillation Damping of Power Systems

In this paper a novel hybrid direct/indirect adaptive fuzzy neural network (FNN) moving sliding mode tracking controller for chaotic oscillation damping of power systems is developed. The proposed approach is established by providing a tradeoff between the indirect and direct FNN controllers. It is equipped with a novel moving sliding surface (MSS) to enhance the robustness of the controller against the present system uncertainties and unknown disturbances. The major contribution of the paper arises from the new simple tuning idea of the sliding surface slope and intercept of the MSS. This study is novel because the approach adopted tunes the sliding surface slope and intercept of MSS using two simple rules simultaneously. One advantage of the proposed approach is that the restriction of knowing the bounds of uncertainties is also removed due to the adaptive mechanism. Moreover, the stability of the control system is also presented. The proposed controller structure is successfully employed to damp the complicated chaotic oscillations of an interconnected power system, when such oscillations can be made by load perturbation of a power system working on its stability edges. Comparative simulation results are presented, which confirm that the proposed hybrid adaptive type‐2 fuzzy tracking controller shows superior tracking performance.     

Robust tracking control of a unicycle-type wheeled mobile manipulator using a hybrid sliding mode fuzzy neural network

This article presents a robust tracking controller for an uncertain mobile manipulator system. A rigid robotic arm is mounted on a wheeled mobile platform whose motion is subject to nonholonomic constraints. The sliding mode control (SMC) method is associated with the fuzzy neural network (FNN) to constitute a robust control scheme to cope with three types of system uncertainties; namely, external disturbances, modelling errors, and strong couplings in between the mobile platform and the onboard arm subsystems. All parameter adjustment rules for the proposed controller are derived from the Lyapunov theory such that the tracking error dynamics and the FNN weighting updates are ensured to be stable with uniform ultimate boundedness (UUB).