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在分析相平面e˙e的基础上,提出了一种仿人智能控制(HSIC)特征模型的新算法;将系统动态过程划分为若干运行模式,在系统响应性能指标与动态过程分区之间间接地建立了相互联系.控制结构上采用开闭环控制,即开环为主导的闭环控制,其控制模态简单.同时,将模糊控制与HSIC相结合,提出了一种新的智能控制器——模糊HSIC控制器.模糊控制器输入输出各语言变量的论域和HSIC特征模型新算法的相平面分区有直接的联系,其控制规则表可以由新算法的逻辑控制规则直接构建.仿真结果表明,HSIC特征模型新算法便于构建模糊控制器,且模糊控制器有较好的鲁棒性和抗干扰能力. 相似文献
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离散型模糊系统的稳定线性监督控制设计 总被引:3,自引:0,他引:3
在对现有的T-S型模糊系统的稳定性结果进行
分析的基础上,研究了状态空间形式下离散型模糊系统在子空间上的线性分解,基于该线性
分解设计一线性监督控制器使模糊闭环系统稳定,从而用简单的线性系统理论完成了对复杂
非线性系统的控制.仿真结果证明了该线性监督控制器的有效性. 相似文献
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一类非线性离散系统的直接自适应模糊控制 总被引:1,自引:0,他引:1
针对一类含延迟非线性离散系统,提出了一种直接自适应模糊控制器设计的新方案.将系统用T-S模糊模型来表示,并基于并行分布补偿(PDC)基本思想设计了一种具有未知参数的模糊控制器,同时采用梯度下降算法对该控制器的参数进行在线辨识.通过输入到状态稳定(ISS)方法,证明了系统输出和参考输出的误差有界且满足一定的平均性能.仿真表明本方法的有效性. 相似文献
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一种基于组合型模糊控制的主动队列管理算法 总被引:1,自引:0,他引:1
计算机网络具有的复杂性和动态特性使传统控制理论难以进行主动队列管理(Active Queue Management, AQM)算法的设计和分析.本文在模糊集合和模糊系统理论的基础上设计了一个主动队列管理算法CF(Combination Fuzzy control).其中模糊控制器I根据瞬时队列的长度和变化值计算控制量;模糊控制器II根据系统负载因子计算控制增益.通过选择模糊控制器参数,模糊控制系统与使用PI(Proportional Integral)控制器的系统具有相同的局部稳定性.最后通过仿真对CF、PI和单模糊控制器的性能进行了比较. 相似文献
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一种基于人工免疫原理的最优模糊神经网络控制器 总被引:1,自引:0,他引:1
提出了一种基于人工免疫原理的最优RBF模糊神经网络控制器设计方案.首先给出了控制器结构,其次将免疫进化算法用于控制器参数的优化,设计了一种满足二次型性能指标的最优RBF模糊神经网络控制器.将该控制器用于控制实际倒立摆系统,并采用状态变量合成方法以大大减少模糊规则的数目,实验结果验证了该控制器的有效性. 相似文献
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交通信号自适应模糊控制器的设计及稳定性分析 总被引:7,自引:1,他引:7
针对城市交通路口的信号控制,提出一种自适应模糊控制器,并对其稳定性进行分析.通过控制器给出路口实时信号配时,根据红灯相位的等候车辆平均损失和绿灯相位释放车辆的平均增益,给出了模糊控制器的自适应算法,以实时修正其模糊规则.在自适应模糊控制器的稳定性分析中,采用模糊控制系统闭环模型的模糊关系矩阵,证明在路口车辆随机产生的情况下,模糊控制系统是稳定的.仿真结果表明,自适应模糊控制器比全感应控制器、简单模糊控制器更能适应路口交通流的变化,极大地改善了系统性能. 相似文献
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一种新型多变量模糊自适应控制系统的研究 总被引:11,自引:0,他引:11
针对多变量非线性系统,提出了一种基于动态耦合特性的两级串联结构的模糊自校正控制器,并提出了基于动态灵敏度矩阵和在线测量的自学习算法.同时利用一种智能梯度法确定自校正学习迭代步长.仿真结果表明,算法的收敛性和系统的稳定性均有所改善.利用这些控制策略,可以较好地解决化工反应器等复杂对象的过程控制问题 相似文献
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Chi-Hsu Wang Tsung-Chih Lin Tsu-Tian Lee Han-Leih Liu 《IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics》2002,32(5):583-597
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. 相似文献
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非线性系统的间接自适应模糊输出反馈监督控制 总被引:1,自引:0,他引:1
In this paper, an indirect adaptive fuzzy output feedback controller with supervisory mode for a class of unknown nonlinear systems is developed. The proposed approach does not need the availability of the state variables, moreover, a supervisory controller is appended to the adaptive fuzzy controller to force the state to be within the constraint set. Therefore, if the adaptive fuzzy controller cannot maintain the stability, the supervisory controller starts to work to guarantee stability. On the other hand, if the adaptive fuzzy controller works well, the supervisory controller will be deactivated. The overall adaptive fuzzy control scheme guarantees the stability of the whole closed-loop systems. The simulation results confirm the effectiveness of the proposed method. 相似文献
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TONG Shao-Cheng CHAI Tian-You 《自动化学报》2005,(2)
In this paper, an indirect adaptive fuzzy output feedback controller with supervisory mode for a class of unknown nonlinear systems is developed. The proposed approach does not need the availability of the state variables, moreover, a supervisory controller is appended to the adaptive fuzzy controller to force the state to be within the constraint set. Therefore, if the adaptive fuzzy controller cannot maintain the stability, the supervisory controller starts to work to guarantee stability. On the other hand, if the adaptive fuzzy controller works well, the supervisory controller will be de-activated. The overall adaptive fuzzy control scheme guarantees the stability of the whole closed-loop systems. The simulation results confirm the effectiveness of the proposed method. 相似文献
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In this paper, an observer-based direct adaptive fuzzy-neural network (FNN) controller with supervisory mode for a certain class of high order unknown nonlinear dynamical system is presented. The direct adaptive control (DAC) has the advantage of less design effort by not using FNN to model the plant. By using an observer-based output feedback control law and adaptive law, the free parameters of the adaptive FNN controller can be tuned on-line based on the Lyapunov synthesis approach. 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 de-activated. The overall adaptive scheme guarantees the global stability of the resulting closed-loop system in the sense that all signals involved are uniformly bounded. Simulation results also show that our initial control effort is much less than those in previous works, while preserving the tracking performance 相似文献
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In this article, an enhanced direct adaptive fuzzy robot controller is developed to overcome problems of high‐frequency oscillations across the boundary of the constraint set and large control signals. The direct adaptive fuzzy robot control algorithm employs tracking errors of the joint motion to drive the parameter adaptation. The predominant concern of the adaptation law is to reduce the tracking errors, and closed‐loop stability is ensured by appending a supervisory controller. This adaptive controller, appended with the supervisory controller, does not require the exact robot dynamics, but only the boundary of the dynamics. Theoretical results and simulation studies on a two‐link robot manipulator show that by modifying the activation function of the supervisory controller, the enhanced direct adaptive fuzzy robot controller is as robust as before and the problems of high‐frequency oscillations across the boundary of the constraint set and large control signals are alleviated. ©1999 John Wiley & Sons, Inc. 相似文献
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A fuzzy logic based supervisory hierarchical control scheme for real time pressure control 总被引:1,自引:4,他引:1
This paper describes a supervisory hierarchical fuzzy controller (SHFC) for regulating pressure in a real-time pilot pressure control system. The input scaling factor tuning of a direct expert controller is made using the error and process input parameters in a closed loop system in order to obtain better controller performance for set-point change and load disturbances. This on-line tuning method reduces operator involvement and enhances the controller performance to a wide operating range. The hierarchical control scheme consists of an intelligent upper level supervisory fuzzy controller and a lower level direct fuzzy controller. The upper level controller provides a mechanism to the main goal of the system and the lower level controller delivers the solutions to a particular situation. The control algorithm for the proposed scheme has been developed and tested using an ARM7 microcontroller-based embedded target board for a nonlinear pressure process having dead time. To demonstrate the effectiveness, the results of the proposed hierarchical controller, fuzzy controller and conventional proportional-integral (PI) controller are analyzed. The results prove that the SHFC performance is better in terms of stability and robustness than the conventional control methods. 相似文献
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Hai-Bo Jiang 《国际自动化与计算杂志》2010,7(4):484-491
The problem of indirect adaptive fuzzy and impulsive control for a class of nonlinear systems is investigated. Based on the approximation capability of fuzzy systems, a novel adaptive fuzzy and impulsive control strategy with supervisory controller is developed. With the help of a supervisory controller, global stability of the resulting closed-loop system is established in the sense that all signals involved are uniformly bounded. Furthermore, the adaptive compensation term of the upper bound function of the sum of residual and approximation error is adopted to reduce the effects of modeling error. By the generalized Barbalat's lemma, the tracking error between the output of the system and the reference signal is proved to be convergent to zero asymptotically. Simulation results illustrate the effectiveness of the proposed approach. 相似文献
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Many published papers show that a TSK-type fuzzy system provides more powerful representation than a Mamdani-type fuzzy system. Radial basis function (RBF) network has a similar feature to the fuzzy system. As this result, this article proposes a dynamic TSK-type RBF-based neural-fuzzy (DTRN) system, in which the learning algorithm not only online generates and prunes the fuzzy rules but also online adjusts the parameters. Then, a supervisory adaptive dynamic RBF-based neural-fuzzy control (SADRNC) system which is composed of a DTRN controller and a supervisory compensator is proposed. The DTRN controller is designed to online estimate an ideal controller based on the gradient descent method, and the supervisory compensator is designed to eliminate the effect of the approximation error introduced by the DTRN controller upon the system stability in the Lyapunov sense. Finally, the proposed SADRNC system is applied to control a chaotic system and an inverted pendulum to illustrate its effectiveness. The stability of the proposed SADRNC scheme is proved analytically and its effectiveness has been shown through some simulations. 相似文献
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Stable adaptive fuzzy control of nonlinear systems 总被引:13,自引:0,他引:13
A direct adaptive fuzzy controller that does not require an accurate mathematical model of the system under control, is capable of incorporating fuzzy if-then control rules directly into the controllers, and guarantees the global stability of the resulting closed-loop system in the sense that all signals involved are uniformly bounded is developed. The specific formula for the bounds is provided, so that controller designers can determine the bounds based on their requirements. The direct adaptive fuzzy controller is used to regulate an unstable system to the origin and to control the Duffing chaotic system to track a trajectory. The simulation results show that the controller worked without using any fuzzy control rules, and that after fuzzy control rules were incorporated the adaptation speed became much faster. It is shown explicitly how the supervisory control forces the state to remain within the constraint set and how the adaptive fuzzy controller learns to regain control 相似文献