一类复杂控制系统分散自适应鲁棒控制器设计

基于模糊逻辑系统具有充分利用语言信息和逼近连续函数性质的思想,分析研究了一类非线性不确定复杂系统的自适应控制问题.利用系统的数学模型和模糊逻辑系统对不确定性的输出信息,设计出了复杂系统的分散自适应鲁棒控制器和模糊逻辑系统参数估计的自适应律,在较弱的假设条件下,证明了这种控制器使被控系统的状态及参数估计误差一致终极有界.仿真实例表明,所提出的方法是有效的.     

基于自适应模糊逻辑系统的一类混沌系统同步控制

针对一类带有未知函数和干扰的混沌系统,进行了基于自适应模糊逻辑系统的自适应同步控制器的设计。首先基于模糊逼近原理,通过对该混沌系统中未知函数的输入输出进行采样,根据采样数据信息设计出具有参数自适应功能的Mamdani型模糊逻辑系统;然后利用该模糊逻辑系统给出一种带有参数自适应的驱动响应同步控制器设计方法;最后通过数值仿真算例表明了该方法的有效性。     

一类基于模糊逻辑系统的非线性系统自适应跟踪控制

利用非线性不确定系统的动态数学模型和模糊逻辑系统，对不确定性的输出信息，设计出被控系统的自适应鲁棒跟踪控制器和模糊逻辑系统参数估计的自适应律．在较弱的假设条件下，证明了这种控制器能使被控系统的状态及参数估计误差一致终极有界．仿真实例表明，所提出的方法是有效的．     

非线性离散时间系统的自适应模糊补偿控制

针对一类非线性离散时间系统,提出一种自适应模糊逻辑补偿控制方案.控制律由跟踪控制律和逼近误差补偿控制律两部分组成,利用模糊逻辑系统对系统参数扰动和外界干扰进行自适应补偿,由模糊滑模控制律实现对模糊逻辑系统逼近误差的进一步补偿.所设计的控制器可保证闭环系统一致最终有界.将该控制器用于月球探测车动态转向系统中,仿真结果表明了该方法的有效性.     

基于分变量模糊蕴涵关系的无规则模糊逻辑系统的自适应控制应用

常用的基于模糊if-then规则的模糊逻辑系统在进行控制设计应用时常会遇到规则爆炸问题,为此,提出了一种新的无规则模糊逻辑系统。首先利用从属于系统各分变量论域上开覆盖的单位分解,将模糊知识库中的专家模糊语言值信息进行整合,得到相应的分变量模糊蕴涵关系,再由模糊推理及解模糊化得到系统输出;然后,针对一类非线性不确定系统,先利用伸缩器和饱和器构造出扩展的无规则模糊逻辑系统,再设计被控系统的自适应稳定控制器;最后通过仿真算例验证了该方法的有效性。这种方法的优越性在于新的模糊逻辑系统的构造与自适应律的设计是分离的,在线估计的参数数目明显减少,有效地解决了规则爆炸问题。     

一类基于模糊逻辑系统的非线性不确定系统自适应观测器设计

利用模糊逻辑系统具有充分利用专家语言信息和逼近连续函数的性质, 分析和研究了一类具有未知不确定界的非线性系统的自适应状态观测器设计问题. 观测器和自适应律的构成直接利用了系统的数学结构信息和模糊逻辑系统对不确定性的输出信息, 在较弱的假设条件下, 这种观测器与被控系统状态间的误差及各参数估计误差一致终极有界. 最后的仿真实例说明了所采用的方法的有效性.     

一类多变量非线性系统的自适应模糊控制

对一类不确定多变量非线性系统提出了自适应模糊控制方法.该方法采用模糊逻辑系统组成的矩阵逼近控制器中的逆矩阵以避免控制器的奇异性问题,同时用误差补偿项对模糊逻辑系统的逼近误差进行补偿.证明该方法不但能使闭环系统的所有信号有界,而且通过适当调整控制器及自适应律中设计参数,可使跟踪误差收敛到原点的小邻域内.仿真结果验证了方法的有效性.     

带有伸缩器和饱和器的一类非线性系统模糊自适应控制设计

本文针对一类带有未知齐次函数的非线性系统,首先基于伸缩器和饱和器的概念,将T-S型模糊逻辑系统的输入–输出进行改造而形成扩展模糊逻辑系统,然后利用扩展模糊逻辑系统给出一种带有可调伸缩因子参数的模糊自适应控制器的设计方法.由于该方法不依赖模糊规则数目,因而不仅能有效减少在线估计的参数数目,而且能够保证被控系统的状态一致终极有界.最后所给数值仿真算例说明了该设计方法的有效性.     

基于性能指标约束的一类输入死区非线性系统最优控制

针对一类考虑指定性能和带有输入死区约束的严格反馈非线性系统,本文提出了一种自适应模糊最优控制方法.采用模糊逻辑系统逼近系统的未知非线性函数及代价函数,利用backstepping方法及命令滤波技术,设计前馈控制器.针对仿射形式的误差系统,结合自适应动态规划技术,设计最优反馈控制器.采用指定性能控制方法,将系统跟踪误差约束在指定范围内.利用死区斜率信息解决具有死区输入的非线性系统的控制问题.基于Lyapunov稳定性理论,证明闭环系统内所有信号是一致最终有界的.最后仿真结果验证了本文方法的可行性和有效性.     

不确定系统的鲁棒稳定性

针对一类不确定非线性系统,基于王立新1994年提出的监督控制方案提出了一种SISO系统的模型参考自适应模糊控制器设计的新方案.该方案同时利用广义多线性模糊逻辑系统的逼近能力和I型模糊逻辑系统的逼近能力,不仅能保证闭环系统状态全局稳定,而且有效避免了控制增益可能出现的奇异性问题.理论分析证明了跟踪误差收敛到零,仿真结果表明了该方法的有效性.     

Adaptive fuzzy control with compressors and limiters for a class of uncertain nonlinear systems

This paper proposes a novel adaptive fuzzy control design for a class of nonlinear uncertain systems. The definition of compressor and limiter with adjustable parameters is introduced at beginning, and then updated laws of parameters of the compressor and estimate values of fuzzy approximation accuracies are utilized to synthesize stable adaptive controllers. The most advantage of designing adaptive fuzzy controller is neglectful of the logic structure of fuzzy logic systems, which make designer focus on parameters of the compressor, limiter and fuzzy approximation accuracies. This adaptive fuzzy control method can not only reduce the number of on-line updated parameters but also guarantee states of the closed-loop system to be uniformly ultimately bounded (UUB). Simulation results demonstrate the effectiveness of the control scheme in this paper.     

The adaptive control of nonlinear systems using the Sugeno-type offuzzy logic

An adaptive fuzzy controller is synthesized from a collection of fuzzy IF-THEN rules. The parameters of the membership functions characterizing the linguistic terms in the fuzzy IF-THEN rules are changed according to some adaptive laws for the purpose of controlling a plant to track a reference trajectory. In the paper, a direct adaptive fuzzy control design method is developed for the general higher order nonlinear continuous systems. We use the Sugeno-type of the fuzzy logic system to approximate the controller. It is proved that the closed-loop system using this adaptive fuzzy controller is globally stable in the sense that all signals involved are bounded. Finally, we apply the method of direct adaptive fuzzy controllers to control an unstable system     

The simplest fuzzy controllers using different inference methods are different nonlinear proportional-integral controllers with variable gains

The author analytically proves that the simplest fuzzy controllers using different inference methods are different nonlinear proportional-integral (PI) controllers with proportional-gains and integral-gains changing with inputs of the controllers. The inference methods involved are Mamdani's minimum inference method, Larsen's product inference method, the drastic product inference method and the bounded product inference method. Configuration of the fuzzy controllers is minimal, which includes two input fuzzy sets, three output fuzzy sets, four control rules, Zadeh fuzzy logic AND, Lukasiewicz fuzzy logic OR and a center of gravity defuzzification algorithm. After analytically investigating properties of the nonlinear PI controllers, the author reveals that the bounded product inference method is inappropriate for the control purpose while the other three inference methods are appropriate. Dynamic and static control behaviors of the fuzzy controllers with the appropriate inference methods are analytically compared with each other, and are also compared with those of the linear PI controller. Finally, it is analytically proven that the fuzzy control systems have the same local stability at the equilibrium point as the corresponding linear PI control system does.     

Adaptive Fuzzy Output Tracking Control of MIMO Nonlinear Uncertain Systems

In this paper, the adaptive fuzzy tracking control problem is discussed for a class of uncertain multiple-input-multiple-output (MIMO) nonlinear systems with the block-triangular structure. The fuzzy logic systems are used to approximate the unknown nonlinear functions. By using the backstepping technique, the adaptive fuzzy tracking control design scheme is developed, which has minimal learning parameterizations. The adaptive fuzzy tracking controllers guarantee that the outputs of systems converge to a small neighborhood of the reference signals and all the signals in the closed-loop system are semiglobally uniformly ultimately bounded. Two examples are used to show the effectiveness of the approach     

分数阶永磁同步电机混沌系统模糊跟踪控制

针对不确定分数阶永磁同步电机混沌系统信号跟踪控制问题,提出自适应模糊控制策略．首先,采用模糊逻辑系统来逼近系统中复杂分数阶函数．然后,基于分数阶李亚普诺夫稳定性理论构造模糊控制器以及分数阶参数自适应律,并在保证所有变量有界的情况下实现系统对已知信号的有效跟踪．最后,通过数值仿真结果验证该方法的有效性．     

广义不确定系统的模糊滑模控制*

研究了广义不确定系统的模糊滑模控制问题。控制结构中采用模糊系统来补偿动态不确定性,利用李亚普诺夫理论,证明了闭环系统的所有状态是全局有界的。     

Output feedback tracking control of robot manipulators with model uncertainty via adaptive fuzzy logic

Many robot controllers require not only joint position measurements but also joint velocity measurements; however, most robotic systems are only equipped with joint position measurement devices. In this paper, a new output feedback tracking control approach is developed for the robot manipulators with model uncertainty. The approach suggested herein does not require velocity measurements and employs the adaptive fuzzy logic. The adaptive fuzzy logic allows us to approximate uncertain and nonlinear robot dynamics. Only one fuzzy system is used to implement the observer-controller structure of the output feedback robot system. It is shown in a rigorous manner that all the signals in a closed loop composed of a robot, an observer, and a controller are uniformly ultimately bounded. Finally, computer simulation results on three-link robot manipulators are presented to show the results which indicate good position tracking performance and robustness against payload uncertainty and external disturbances.     

一类非线性互联系统的间接自适应模糊滑模跟踪控制

基于模糊逻辑逼近原理,针对非线性动态未知互联系统,设计一种新的模糊分散控制 器.讨论了互联项满足或不满足一般性约束条件的两种情形,用不同方法来补偿其对大系统的影 响.同时利用模式转换函数来实现间接自适应控制与模糊滑模控制之间的转换,使系统的状态在 有界闭集内变化,并依据Lyapunov方法证明了闭环系统稳定,跟踪误差收敛到零的一个小邻域 内.仿真结果证明所设计的模糊控制器的有效性.