未知多变量非线性系统自适应模糊预测控制

对一类未知多变量非线性系统提出了直接自适应模糊预测控制方法,此方法首先对被控对象提出了线性时变子模型加非线性子模型的预测模型,然后直接用模糊逻辑系统组成的向量来设计预测控制器,并基于时变死区函数对控制器中的未知向量和广义误差估计值中的未知矩阵进行自适应调整．文中证明了此方法可使广义误差向量估计值收敛到原点的一个邻域内．     

多变量非线性系统RBF直接广义预测研究

为了避免Diophantine方程求解和矩阵求逆运算,提高广义预测控制算法的实时性,对一类参数未知多变量非线性系统提出一种径向基函数神经网络的直接广义预测控制（GPC）算法。该算法将多变量非线性系统转换为多变量时变线性系统,然后利用径向基神经网络来逼近控制增量,对控制器参数向量,即网络权值中的未知向量基于跟踪误差进行自适应调整。理论证明,该方法可使跟踪误差收敛到原点的一个小邻域内。仿真结果验证了此算法的有效性。     

基于多模型和SVM逆系统单元机组解耦控制

火力单元机组协调控制系统是一个多变量、强耦合的控制系统,具有非线性、耦合和延迟等特性,其性能直接影响单元机组运行的安全性和经济性.为了有效解决火力单元机组协调控制系统的耦合特性和动态非线性,设计了基于多模型和支持向量机(SVM)逆系统的解耦控制方法,并进行了相应实验研究.针对一个300 MW单元机组的试验仿真模型,得到单元机组在5个典型工作点的线性化模型,然后对每个线性化模型分别设计SVM逆模型及其动态PID控制器,进而用模型线性组合成多模型全局控制系统.通过加权多项式选取合成的多模型控制方法,可以解决负荷大范围变化引起的非线性问题;支持向量机与逆系统的结合能很好地解决非线性系统的强耦合问题.仿真研究证明了这种控制算法设计的有效性和优越性.     

多层感知器分类行为的模糊线性分析

线性判别函数理论是线性分类器的分析基础,并不适合非线性分类器.本文把非线性激励函数视为隶属度函数,将非线性神经元及多层感知器分类行为的分析建筑在模糊集理论基础上,提出模糊线性判别函数与模糊判别边界、模糊分类等概念.并引出将隐层初始权向量均匀分布在权空间超球面上的初始化方法,明显提高了多层感知器的收敛性能.并提出了一种在多层感知器的类空间中构造最优超平面的简易新方法.     

具有多滞后非线性定常关联控制系统的镇定性研究

建立了多组多滞后定常非线性控制系统的结构 概念,采用李雅普诺夫函数分解等价法,由 Riccati矩阵微分方程的对称正定解矩阵构造正 定二次型Ｖ函数,给出了无滞后无扰动参数线性定常控制孤立子系统的镇定性,蕴含具有滞 后控制向量函数的扰动结构参数的多组多滞后区间系数定常非线性关联控制系统的关联鲁棒 镇定的一个充分条件,同时给出了扰动参数与滞后非线性项界线的估计公式．     

船舶航向非线性系统的多滑模自适应模糊控制

针对参数未知的船舶航向非线性控制系统数学模型,在考虑舵机伺服机构特性的情况下,船舶航向控制问题就成为一个虚拟控制系数未知的非匹配不确定非线性控制问题.基于多滑模设计方法和模糊逻辑系统的逼近能力,提出了一种多滑模自适应模糊控制算法,通过引入非连续投影算法和积分型Lyapunov函数,提高了系统在抑制参数漂移、控制器奇异等方面的能力.借助Lyapunov函数证明了所设计控制器使最终的闭环非匹配不确定船舶运动非线性系统中的所有信号有界,且跟踪误差收敛到零.仿真研究表明:该算法与传统的PID控制相比,具有较好的跟踪能力和自适应能力.     

基于多模型的非线性系统广义预测控制

对于复杂的离散时间非线性系统，提出一种基于多模型的广义预测控制方法．通过在平衡点附近建立线性模型，并用径向基函数神经网络来补偿匹配误差，形成了非线性系统的多模型表示，然后采用模糊识别方法作为切换法则，并结合广义预测控制构成了多模型广义预测控制器．通过对连续发酵过程的计算机仿真，表明了该方法的有效性．     

基于广义模糊基函数的多变量模糊模型及其辨识方法

利用模糊穴位映射理论，提出一种有效描述复杂多变量系统的模糊模型－－广义模糊基函数展开式，它可方便地处理多输入多输出系统的语言和系统信息，并可逼近任意非线性函数，是一种通用的多变量模糊逻辑系统模型。利用语言信息，提出一种新的自适应参数辨识方法－－改进的Ｗｉｄｒｏｗ－Ｈｏｆｆ学习规则，仿真结果验证了它的有效性。     

基于T—S模糊模型的多变量预测控制

提出一种基于T-S模糊模型的多输入多输出预测控制策略.T-S模糊模型用于描述对象的非线性动态特性,模糊规则将非线性系统划分为多个局部子线性模型.为提高预测控制性能,采用多步线性化模型构成多步预报器,从而将预测控制中的非线性优化问题转化为一个线性二次寻优问题.串接贮槽液位控制系统的仿真结果表明,多步线性化模型预测控制性能优于单步线性化模型预测控制性能.     

模糊多模型协调控制系统设计及仿真

针对一个500 MW单元机组的仿真模型,首先依据滑压曲线对模型线性化得到其平衡工作点的线性化模型,并用隶属度函数连结成T-S模糊模型;然后针对各线性化模型设计了V-规范型多变量内模控制器;最后,依据T-S模糊模型结构设计了以多变量内模控制器作为局部模型控制器的模糊多模型协调控制系统.这种控制系统既可以克服模型工作点变化所带来的非线性问题,同时又可以保证控制回路间的有效解耦.仿真研究证明了控制系统设计的有效性.     

Adaptive fuzzy decentralized control for interconnected MIMO nonlinear subsystems

This paper describes an adaptive fuzzy control strategy for decentralized control for a class of interconnected nonlinear systems with MIMO subsystems. An adaptive robust tracking control schemes based on fuzzy basis function approach is developed such that all the states and signals are bounded. In addition, each subsystem is able to adaptively compensate for disturbances and interconnections with unknown bounds. The resultant adaptive fuzzy decentralized control with multi-controller architecture guarantees stability and convergence of the output errors to zero asymptotically by local output-feedback. An extensive application example of a three-machine power system is discussed in detail to verify the effectiveness of the proposed algorithm.     

A fuzzy basis function vector-based multivariable adaptivecontroller for nonlinear systems

In this paper, a new fuzzy basis function vector (FBFV) approach for the adaptive control of multivariable nonlinear systems is presented. With this method, the nonlinear plant is first linearized. The linearized bias and uncertainties as well as disturbances are assumed to be included in the model structure and their upper bound will be adaptively learned by the FBFV method. The output of the FBFV is used as the parameters of the robust controller in the sense that both the robustness and the asymptotic error convergence can be obtained for the multivariable nonlinear system. The effectiveness of the proposed analysis and design method is illustrated with a simulated example.     

基于自适应神经网络的不确定非线性系统的模糊跟踪控制

提出了一种基于模糊模型和自适应神经网络的跟踪控制方法.在系统具有未知不确定非线性特性的情况下,首先利用T＿S模糊模型对系统的已知特性进行近似建模,对基于模糊模型的模糊H∞跟踪控制律进行输出跟踪控制.并在此基础上,进一步采用RBF神经网络完全自适应控制,通过在线自适应调整RBF神经网络的权重、函数中心和宽度,从而有效地消除系统的未知不确定性和模糊建模误差的影响,保证了非线性闭环系统的稳定性和系统的H∞跟踪性能,而不要求系统的不确定项和模糊建模误差满足任何匹配条件或约束.最后,将所提出的方法应用到一非线性混沌系统,仿真结果表明了所提出的方案不仅能够有效地稳定该混沌系统,而且能使系统输出跟踪期望输出.     

An Adaptive Control Using Fuzzy Basis Function Expansions for a Class of Nonlinear Systems

An adaptive control using fuzzy basis function expansions is proposed for a class of nonlinear systems in this paper. It is shown that two system uncertainty bounds are approximated in a compact set by using fuzzy basis function expansion networks in the Lyapunov sense, and the outputs of the fuzzy networks are then used as the parameters of the controller to adaptively compensate for the effects of system uncertainties. Using this scheme, not only strong robustness with respect to unknown system dynamics and nonlinearities can be obtained, but also the output tracking error between the plant output and the desired reference output can be guaranteed to asymptotically converge to zero. Simulation results are provided to demonstrate the effectiveness, simplicity and practicality of the proposed control scheme.     

Fuzzy adaptive output feedback control for a class of switched non-triangular structure nonlinear systems with time-varying delays

This paper investigates an adaptive fuzzy output feedback control design problem for switched nonlinear system in non-triangular structure form. The discussed system contains unknown nonlinear dynamics, unmeasured states and unknown time-varying delays under a batch of switching signals. Fuzzy logic systems are utilised to learn unknown nonlinear dynamics and construct a fuzzy switched nonlinear observer. By combining the property of fuzzy basis function with Lyapunov–Krasovskii functional and the command filter, a novel observer-based fuzzy adaptive backstepping schematic design algorithm is presented. Furthermore, the stability of the closed-loop control system is proved via Lyapunov stability theory and average dwell time method. The simulation results are presented to verify the validity of the proposed control scheme.     

利用模糊系统的直升机的自适应控制

针对直升机动力学为非线性,且存在不确定因素和状态变化,设计利用模糊系统的自适应控制器.设计的控制器是系统的输出跟踪参考模型输出的直接调整模糊控制器参数的自适应控制器.又利用Lyapunov函数保证了闭环控制系统的稳定性并推导最优的自适应规律.实验结果表明,有外部扰动的情况下所设计的自适应控制器比模糊控制器对直升机控制具有良好的动态响应和稳定性,是一种非常有效的控制方法.     

Recursive Back‐Stepping Design of An Adaptive Fuzzy Controller for Strict Output Feedback Nonlinear Systems

In this paper, a back‐stepping adaptive fuzzy controller is proposed for strict output feedback nonlinear systems. The unknown nonlinearity and external disturbances of such systems are considered. We assume that only the output of the system is available for measurement. As a result, two filters are constructed to estimate the states of strict output feedback systems. Since fuzzy systems can uniformly approximate nonlinear continuous functions to arbitrary accuracy, the adaptive fuzzy control theory combined with a tuning function scheme is developed to derive the control laws of strict output feedback systems that possess unknown functions. Moreover, the H∞ performance condition is introduced to attenuate the effect of the modeling error and external disturbances. Finally, an example is simulated in order to confirm the applicability of the proposed method.     

Adaptive fuzzy control systems with dynamic structure

This article proposes a novel fuzzy system, referred to as a dynamic structure fuzzy system, to address tracking control problems for unknown nonlinear dynamical systems. The fuzzy system is employed to reconstruct the unknown nonlinearities of dynamic systems. In the dynamic structure fuzzy system, the number of fuzzy rules can be either increased or decreased over time based on the required approximation accuracy. The advantage of the dynamic structure fuzzy system is that a suitable-sized fuzzy system can be found to avoid overfitting or underfitting data sets. By using Gaussian radial basis function (GRBF) as a membership function, adaptation laws are presented for tuning all parameters of the parameterized fuzzy system, including the output weights, the widths and the centers of the GRBF's. Global boundedness of the overall control scheme is guaranteed in the sense of Lyapunov. The tracking error converges to the required precision through the adaptive control scheme derived by the Lyapunov synthesis approach. Simulations performed on an underwater vehicle system demonstrate the effectiveness of our scheme.     

Event-triggered adaptive fuzzy control for nonstrict-feedback nonlinear systems with output hysteresis

This paper concentrates upon the issue of adaptive fuzzy tracing control for a class of nonstrict-feedback nonlinear systems output with hysteresis via an event-triggered strategy. To handle the difficulty caused by the nonstrict nonlinear systems, the variable separation technique is introduced. The design difficulty of output hysteresis is addressed by employing a hysteresis inverse function and Nussbaum function to compensate unmeasurable state signal. Meanwhile, the fuzzy logic system (FLS) is used to estimate the unknown function at each step of recursion. Moreover, by devising the relative threshold event-triggered mechanism (ETM), the frequency of actuators and controllers can be largely decreased. Thus, the adaptive fuzzy event-triggered tracing control strategy is proposed by combining the barrier Lyapunov function and backstepping technique. With the proposed scheme, it is theoretically demonstrated that all signals in the closed-loop system are bounded, and the tracing errors are driven to a small neighborhood of the origin under the output constraint. Eventually, two examples demonstrate the efficacy of the proposed control strategy.     

输出约束的有限时间自适应区间二型模糊输出反馈PMSM伺服控制

针对永磁同步电机驱动的伺服系统在不确定性摩擦和未知负载的影响下难以达到高精度的控制效果,提出一种基于区间二型模糊系统的带有输出约束的有限时间自适应输出反馈控制方案.首先,构建一个基于非线性扰动观测器的区间二型模糊状态观测器,分别完成对于未知扰动和速度的估计,区间二型模糊系统完成对于非线性摩擦的逼近;然后,在此基础上,结合滤波误差补偿机制和有限时间技术,引入障碍Lyapunov函数和反步控制技术设计输出约束的自适应区间二型模糊输出反馈控制器;最后,根据Lyapunov稳定性理论提出严格的稳定性分析,保证闭环系统的所有信号均是有限时间内有界的,并通过数值仿真和实验验证了所提出方法的有效性.