基于TS模糊PID控制模型的发电机控制器设计

研究了对风力双馈感应发电机进行控制的TS模糊PID控制器;针对常规PID控制器难以应对某些对象参数变化大,延时环节较大以及噪声干扰等问题,提出了一种能对发电机控制的TS模糊PID控制器;首先定义了双馈感应发电机的数学模型,在此基础上提出了基于TS模型的PID模糊控制器设计方法,并将其用于双馈感应发电机有功功率控制问题中,最后在加入15%测量噪声干扰的情况下对发电机的TS控制器和TS-PID控制器分别进行了仿真;实验结果表明:采用TS模糊PID控制方法比常规PID具有更强的适应性、鲁棒性和可移植性。     

基于模糊规则的多模型控制方法在AUV航向控制中的应用

本文研究了基于模糊切换规则的多模型控制方法,通过模糊切换实现了多控制器集的平滑切换,各局部控制器可以采用常规或智能控制规律设计。并在环境干扰条件下,以航向控制为例,对AUV进行航向跟踪,对比基于单一模型下设计的控制器：仿真结果验证了该控制方法具有很好的控制性能和鲁棒性。     

深度确定性策略梯度与模糊PID的协同温度控制

针对现有温度控制系统控温时间长、误差大的问题, 本文提出了一种基于深度确定性策略梯度(DDPG)和模糊自整定PID的协同温度控制. 首先, 模糊PID在控制大滞后系统时, 控制器不能立刻对产生的干扰起抑制作用, 且无法保证大滞后系统的稳定性等问题, 本文建立了模糊PID和DDPG算法相结合的温度控制模型, 该模型将模糊PID作为主控制器, DDPG算法作为辅助控制, 利用双控制器模型实现温度协同控制. 接着, 利用遗传算法对模糊PID的隶属函数和模糊规则进行寻优, 获得模型参数最优解. 最后, 在仿真实验中验证所提方法的有效性. 仿真实验结果表明, 本文提出的算法可有效减少噪声干扰, 减小控制系统的响应时间、误差和超调量.     

AUV深度的模糊神经网络滑模控制

本文设计了一个模糊神经网络滑模变结构控制器，通过模糊神经网络对滑模控制律 的控制增益进行在线调整，并在海浪干扰条件下，用此控制器对AUV进行深度控制．仿真结 果验证了该智能控制方法具有很好的控制性能和鲁棒牲．     

尾坐式飞行器过渡模式纵向姿态控制

针对尾坐式飞行器由垂直飞行模式向水平飞行模式转换过程中产生的模型参数变动干扰问题,设计了模糊滑模控制器进行姿态控制,利用模糊规则自适应调整趋近律以消除系统的抖振;通过仿真和飞行实验,验证了所设计的控制器具有良好的跟踪性能和鲁棒性,可以克服飞行器在过渡模式下系统参数的变动干扰,而且削弱了滑模控制器造成的输出抖振,减轻了副翼执行机构的负担。     

不确定Duffing混沌系统的同步控制

在模糊滑模变结构控制基础上,研究具有不确定性Duffing混沌系统的同步控制问题。选择合适的滑模面,基于Lyapunov稳定性理论设计模糊滑模变结构控制器及自适应更新规则,从理论上证明控制系统的稳定性。由于控制器的设计是基于自适应模糊滑模变结构控制的,与常规方法相比,控制器滑动模态不受干扰的影响,有较好的鲁棒性和快速跟踪能力。通过数值仿真实验验证了该系统的有效性。     

永磁球形电机的少保守性滑模控制

永磁球形电机轨迹跟踪控制方法常常利用高增益的控制输出来保证系统的鲁棒性及跟踪控制的快速性.但这种保守控制会带来较大的控制作用,甚至导致执行器饱和.为了减少控制的保守性,本文设计了一种带有非线性干扰观测器的模糊滑模控制器来解决球形电机的轨迹跟踪问题.利用干扰观测器对不确定性、摩擦、外界干扰、负载扰动等进行估计,并在控制输入端进行补偿实现对干扰的抑制.并利用滑模控制器抵消干扰观测器的干扰观测误差及不可观测部分的干扰,为了减少滑模的抖振,本文利用模糊逻辑对该部分进行逼近,并利用模糊的输出增益代替滑模的切换增益.此外通过Lyapunov方程证明了本文控制器的稳定性.仿真结果表明在存在模型不确定性及各种干扰的情况下,本文的轨迹跟踪控制具有良好的动静态性能和少保守性.     

卫星编队长期保持与控制比较研究

卫星编队飞行的难点之一是在复杂干扰力环境下控制队形.近圆轨道,编队控制充分考虑了J_2摄动等各种干扰对相对轨道构形的影响.以空间二体运动的Hill方程为基础建立相对运动动力学方程.分别采用基于线性二次调节器、李亚普诺夫理论以及零控脱靶量的方法设计了编队控制器,对干扰力作用下卫星编队长期保持的不同控制方法进行了比较研究,并对三种编队控制方法的控制精度、能量消耗进行了仿真分析.研究表明,对于编队的长期三种保持控制方法都是有效的,但基于零控脱靶量的编队控制方法更简单、性能好、实用性强,是一种较为理想的编队控制方法.     

T-S模糊系统H2/H∞混合控制器设计的LMI方法

应用线性矩阵不等式（LMI）方法研究T-S模糊系统的H2／H∞混合控制器的设计问题．首先针对T-S模糊系统分别设计H2和H∞控制器；然后以线性矩阵不等式的形式给出T-S模糊系统H2／H∞混合控制器存在的充分条件及相应的控制器设计方法．在给定的H∞干扰约束下，通过优化H2控制性能指标实现了模糊状态反馈次优控制．最后通过例子验证了所给出的H2／H∞混合控制器设计方法的可行性和有效性．     

基于模糊前馈的ASV再入姿态的模糊鲁棒跟踪控制方法

提出一种模糊鲁棒跟踪控制方法,并应用于研究空天飞行器(ASV)再人段姿态角的跟踪问题.基于ASV再入段存在外界干扰的不确定姿态动态系统的T-S模糊模型,考查姿态角跟踪参考信号的跟踪误差,引入模糊前馈,得出跟踪误差指数稳定的约束条件.并在镇定控制是前馈控制的先决条件的前提下,研究模糊前馈跟踪控制器和具有极点约束的H∞模糊镇定控制器的设计问题,基于Matlab的线性矩阵不等式(LMI)和模糊逻辑控制(FLC)工具可实现此问题的求解.仿真结果验证本文算法的有效性.     

Fuzzy logic compliance control of the peg in hole insertion

Compliance control of the peg-in-hole insertion while both peg and hole are rigidly supported, is studied. Initially, the peg-in-hole operation is mathematically modelled to develop a better understanding of the existing constraints. Imitating a human operator, a compliant motion for the assembly of the peg in the hole using the heuristic approach is developed. Two basic fuzzy controllers are studied. One in which inference engine operates purely based on force/torque information received from the sensor. In the other the approximate position of the peg is also taken into account to estimate the corrective action required. The rule-bases of both controllers are developed based on the qualitative knowledge of the behaviour of the controlled process. The performance of the fuzzy controllers are compared with the performance of a non-fuzzy IF–THEN logic branching control algorithm. The results obtained are encouraging.     

The stability of an oceanic structure with T–S fuzzy models

In this study we construct and derive analytical solutions for a mathematical model of an oceanic environment in which wave-induced flow fields cause structural surge motion after which a fuzzy control technique is developed to alleviate structural vibration. Specifically the Takagi–Sugeno (T–S) fuzzy model is employed to approximate the oceanic structure and a parallel-distributed-compensation (PDC) scheme is utilized in a control procedure designed to reduce the structural response. All local state feedback controllers are integrated to construct a global fuzzy logic controller. The Lyapunov method is used to achieve structural stability. The interaction between the wave motion and the structural response is explained using the separation of variables method. The surge motion is related to the characteristics of the wave and the structure. A parametric approach is utilized to show these effects. Other parameters remain constant. In an oceanic structural system, platform migration is often caused by the wave force. The stability of an oceanic structure can be proven theoretically based on stability analysis. The decay of the displacement and velocity due to the use of the proposed fuzzy controllers is demonstrated by a numerical simulation.     

机械臂自调整因子模糊PD迭代学习控制研究

大部分模糊控制器不具有适应控制对象变化的能力,基于此设计一种自调整因子模糊控制器,并针对机械臂长时间重复操作导致运动精确度下降这一类问题,结合迭代学习控制方法,提出一种自调整因子模糊PD迭代学习控制方法;以双关节机械臂为研究对象,利用Fuzzy工具箱编写模糊控制规则,通过系统产生的误差以及误差的变化率作为模糊控制器的输入量调整模糊系统中的量化因子和比例因子,实现模糊规则的更新和对迭代学习控制中的PD参数的实时调整,系统的自适应性得到提高,并在Simulink中进行机械臂的运动控制实验,仿真结果表明,所提控制方法最终产生的误差可以精确到0.0001 rad,同时在进行第2次迭代时关节角度和角速度误差收敛基本趋于零,整体的控制效果较好。     

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.     

Four wheel steering control by fuzzy approach

This study introduces a fuzzy four-wheel steering control design method for automotive vehicles. After the analysis of some stability aspects of the vehicle lateral motion, including front steering angle variations, the representation of vehicle nonlinear model by Takagi-Sugeno (T-S) fuzzy model is presented. Next, based on the fuzzy model, a fuzzy controller is developed to improve the stability of the vehicle. Sufficient conditions for stability and stabilization of the T-S fuzzy model using fuzzy feedback controllers is given. To demonstrate the effectiveness of the proposed fuzzy controller, simulation results are given showing the performance improvements of the vehicle in terms of the stability and the maneuverability in critical situations.     

Structural analysis of fuzzy controllers with nonlinear input fuzzy sets in relation to nonlinear PID control with variable gains

The popular linear PID controller is mostly effective for linear or nearly linear control problems. Nonlinear PID controllers, however, are needed in order to satisfactorily control (highly) nonlinear plants, time-varying plants, or plants with significant time delay. This paper extends our previous papers in which we show rigorously that some fuzzy controllers are actually nonlinear PI, PD, and PID controllers with variable gains that can outperform their linear counterparts. In the present paper, we study the analytical structure of an important class of two- and three-dimensional fuzzy controllers. We link the entire class, as opposed to one controller at a time, to nonlinear PI, PD, and PID controllers with variable gains by establishing the conditions for the former to structurally become the latter. Unlike the results in the literature, which are exclusively for the fuzzy controllers using linear fuzzy sets for the input variables, this class of fuzzy controllers employs nonlinear input fuzzy sets of arbitrary types. Our structural results are thus more general and contain the existing ones as special cases. Two concrete examples are provided to illustrate the usefulness of the new results.     

Experimental Comparison Research on Active Vibration Control for Flexible Piezoelectric Manipulator Using Fuzzy Controller

Space manipulators are flexible structures. Vibration problem will be unavoidable due to motion or external disturbance excitation. Model based control methods will not maintain the required accuracy because of the existence of nonlinear factors and parameter uncertainties. To solve these problems, fuzzy logic control laws with different membership function groups are adopted to suppress vibrations of a flexible smart manipulator using collocated piezoelectric sensor/actuator pair. Also, dual-mode controllers combining fuzzy logic and proportional integral control are designed, for suppressing the lower amplitude vibration near the equilibrium point significantly. Experimental comparison research is conducted, using fuzzy control algorithms and the dual-mode controllers with different membership functions. The experimental results show that the adopted fuzzy control algorithms can substantially suppress the larger amplitude vibration; and the dual-mode controllers can also damp out the lower amplitude vibration significantly. The experimental results demonstrate that the proposed fuzzy controllers and dual-mode controllers can suppress vibration effectively, and the optimal placement is feasible.     

Genetic algorithm design of neural network and fuzzy logic controllers

This paper discusses the design of neural network and fuzzy logic controllers using genetic algorithms, for real-time control of flows in sewerage networks. The soft controllers operate in a critical control range, with a simple set-point strategy governing “easy” cases. The genetic algorithm designs controllers and set-points by repeated application of a simulator. A comparison between neural network, fuzzy logic and benchmark controller performance is presented. Global and local control strategies are compared. Methods to reduce execution time of the genetic algorithm, including the use of a Tabu algorithm for training data selection, are also discussed. The results indicate that local control is superior to global control, and that the genetic algorithm design of soft controllers is feasible even for complex flow systems of a realistic scale. Neural network and fuzzy logic controllers have comparable performance, although neural networks can be successfully optimised more consistently.     

A general technique for deriving analytical structure of fuzzy controllers using arbitrary trapezoidal input fuzzy sets and Zadeh AND operator

Deriving the analytical structure of fuzzy controllers is very important as it creates a solid foundation for better understanding, insightful analysis, and more effective design of fuzzy control systems. We previously developed a technique for deriving the analytical structure of the fuzzy controllers that use Zadeh fuzzy AND operator and the symmetric, identical trapezoidal or triangular input fuzzy sets. Many fuzzy controllers use arbitrary trapezoidal/triangular input fuzzy sets that are asymmetric. At present, there exists no technique capable of deriving the analytical structure of these fuzzy controllers. Extending our original technique, we now present a novel method that can accomplish rigorously the structure derivation for any fuzzy controller, Mamdani type or TS type, that employs the arbitrary trapezoidal input fuzzy sets and Zadeh fuzzy AND operator. The new technique contains our original technique as a special case. Given the importance of PID control, we focus on Mamdani fuzzy PI and PD controllers in this paper and show in detail how to use the new technique for different configurations of the fuzzy PI/PD controllers. The controllers use two arbitrary trapezoidal fuzzy sets for each input variable, four arbitrary singleton output fuzzy sets, four fuzzy rules, Zadeh fuzzy AND operator, and the centroid defuzzifier. This configuration is more general and complicated than the Mamdani fuzzy PI/PD controllers in the current literature. It actually contains them as special cases. We call this configuration the generalized fuzzy PI/PD controller.     

Analytical structures and analysis of the simplest fuzzy PI controllers

This paper deals with simplest fuzzy PI controllers which employ two fuzzy numbers on the universe of discourse (UOD) of each input variable, and three fuzzy numbers on the UOD of output variable. Analytical structures of such controllers are derived using triangular membership functions for fuzzification, different combinations of T-norms and T-conorms, different inference methods, and center of area (COA) method for defuzzification. Properties of these controllers are investigated. A comparative study is made on (i) the fuzzy PI controllers derived, and (ii) on the fuzzy PI controllers and their counterpart—conventional PI controller. Moreover, sufficient conditions for bounded-input bounded-output (BIBO) stability of fuzzy PI control systems are established using the well-known small gain theorem.