基于模糊控制理论的一种PID参数自整定控制器的设计与仿真

０　概　述常规ＰＩＤ控制原理简单，使用方便，鲁棒性较强，即使被控对象的数学模型不精确也可使用。因此，长期以来被广泛应用于工业过程控制，并取得了良好的控制效果。就是目前最新的过程控制计算机其基本控制方式也仍然是ＰＩＤ控制。在工业控制过程中，有的对象参数未知或参数缓慢变化，有的对象带有延时或随机干扰，即所谓的不确定性。面对这种情况，常规ＰＩＤ控制不能够在线自整定参数ＫＰ、ＫＩ、ＫＤ。本文应用模糊推理的方法实现ＰＩＤ参数在线自整定，并设计出相应的模糊ＰＩＤ控制器。仿真结果表明了该设计方法完全有效。１　…     

一种改进的三维模糊控制设计方法

针对传统三维模糊控制器模糊规则多,在实际应用中不易实现,输入量之间相互耦合的问题,提出一种PID与模糊混合(M)型控制器.该控制策略是把三个输入量分别为偏差、偏差变化、偏差的偏差变化的一维模糊控制器加权融合实现混合(M)型控制器,解决了多变量模糊控制中的规则数量几何爆炸问题,具有更大的可控范围.针对传统模糊控制不能消除系统静差的缺陷,吸收增量式PID控制递推累加的形式,对偏差进行积分,混合(M)型控制器实现了对系统静差的消除.此外介绍了M型控制器的参数整定方法,并对其稳定性进行了分析.仿真表明,M型控制器控制效果比二维模糊控制和PID控制都好.     

模糊控制器的两种设计方法

介绍了两种模糊控制的设计方法，一种是模糊控制规则固定的用户图形界面方法，另一种是模糊控制规则可调的命令行方法。并基于Simulink实现了模糊控制系统的建立和动态仿真。     

一种自调整模糊控制器的设计与仿真研究

提出了一种基于规则，参数自整定的模糊控制器的设计方法，它通过引入误差加权因子和误差变化率加权因子，以系统阶跃响应的上升时间和超调量为性能评价指标，并根据评价结果建立自适应算法，对模糊控制器的模糊推理规则及参数进行自调整。仿真结果进行表明，误差变化率加权因子以明显改善模糊控制器的稳定性，性能评价指标函数，模糊推理规则和参数调整算法简单实用，便于实现，对被控对象的参数变化具有较强的适应能力，控制效果良好。     

基于设计的三维形态研究

本文将采用解释和比较的方式,结合社会发展与未来趋势,重点从三维形态的概念基础、三维空间与二维形体的差异、平面设计中的三维形态以及平面设计中三维形态的缺陷这几个方面着手,透过促进多媒体技术和视错觉传达形式对平面设计的影响这一研究内容的延伸,对三维形态设计对设计的影响进行深入的探讨。     

设计模糊控制器的一种方法

本文提出一种设计模糊控制器（ＦＣ）的方法,该方法将于Ｅｉ,ＥＣｊ,Ｕｋ分别定义为各自论域上的模糊划分,采用代数乘和代数加运算,量化等级可以根据需要而定,简化算法可以实现实时在线计算,内存只存储控制规则和隶属度函数,计算复杂度与量化等级无关;设计的模糊控制器具有良好的动态和稳态性能。     

三维模糊PID控制器的一种简化

本文提出了一种简化模糊控制方案将三维的控制规则简化为二维控制规则，大大减少了模糊控制规则的数目，简化了控制策略，控制器的设计及调整过程，并讨论了该控制器的调整方法，最后通过仿真验证了其优越性。     

基于模糊逻辑的滑模控制设计方法

针对一类具有不确定性的单输入非线性系统，根据滑模控制原理。采用趋近律方法优化的滑模控制设计方法，以满足控制系统的鲁棒性和趋近运动品质的要求；同时根据模糊理论将切换函数模糊化，并且通过模糊规则自适应调整切换函数的模糊量化因子，以消除变结构控制系统固有的抖振和进一步改善系统偏差。仿真结果表明．设计的模糊滑模系统，具有对模型不确定性和外来干扰较强的鲁棒性以及良好的跟踪性能。避免了系统固有的颤动现象，控制的效果良好，性能满意；可应用于此类不确定非线性系统的设计综合问题。     

一种建立模糊控制查询表的简单方法和程序

针对建立模糊控制查询表计算工作量大且又费时的问题，介绍了一种建立查询表的简单方法，并用MATLAB语言进行了程序设计。     

线性模糊控制器设计的一种新方法

基于模糊语言控制规则描述的模糊控制,已成功地用于工业过程控制并具有广泛的应用前景。但常规的模糊控制器的设计其计算和编程非常麻烦,该文介绍线性模糊PID控制器设计的一种新方法。首先推导模糊线性PID控制器的数学等效式,用Mamdani方法进行模糊控制算法,在MATLAB Simulink和Floulib工具帮助下用Manxtani方法取一阶控制对象进行二维PI模糊控制仿真实验,并分别画出模糊PI阶跃响应及其与传统PI控制器阶跃响应之差的曲线和三维区间图,理论分析与实验结果表明：采用Mamdani方法进行线性模糊PID设计与传统的方法相比,在应用上简便、清晰、具有一定的实用价值。     

机器人操作器的自适应模糊滑模控制器设计

针对机器人动力学系统提出了一种基于模糊逻辑的自适应模糊滑模控制方案.根据滑模控制原理并利用模糊系统的逼近能力设计控制器,基于李雅谱诺夫方法设计自适应律,证明了闭环模糊控制系统的稳定性和跟踪误差的收敛性.控制结构简单,不需要复杂的运算.该设计方案柔化了控制信号,减轻了一般滑模控制的抖振现象.仿真结果表明了所提控制策略的有效性.     

Sum-of-squares-based fuzzy controller design using quantum-inspired evolutionary algorithm

In the field of fuzzy control, control gains are obtained by solving stabilisation conditions in linear-matrix-inequality-based Takagi–Sugeno fuzzy control method and sum-of-squares-based polynomial fuzzy control method. However, the optimal performance requirements are not considered under those stabilisation conditions. In order to handle specific performance problems, this paper proposes a novel design procedure with regard to polynomial fuzzy controllers using quantum-inspired evolutionary algorithms. The first contribution of this paper is a combination of polynomial fuzzy control and quantum-inspired evolutionary algorithms to undertake an optimal performance controller design. The second contribution is the proposed stability condition derived from the polynomial Lyapunov function. The proposed design approach is dissimilar to the traditional approach, in which control gains are obtained by solving the stabilisation conditions. The first step of the controller design uses the quantum-inspired evolutionary algorithms to determine the control gains with the best performance. Then, the stability of the closed-loop system is analysed under the proposed stability conditions. To illustrate effectiveness and validity, the problem of balancing and the up-swing of an inverted pendulum on a cart is used.     

Stable nonlinear controller design for a Takagi-Sugeno fuzzy model

This paper proposes another adaptive control scheme for nonlinear systems using a Takagi-Sugeno fuzzy model. Takagi-Sugeno fuzzy models have been widely used to identify the structures and parameters of unknown or partially known plants, and to control nonlinear systems. This scheme shows a good approximation capability by the fuzzy blending of local dynamics. Since a Takagi-Sugeno fuzzy model is a nonlinear system in nature, and its parameters are not linearly parameterized, it is difficult to design an adaptive controller using conventional design methods for adaptive controllers which are derived from linearly parameterized systems. In this paper, the functional form of the local dynamics are assumed to be known, but the corresponding parameters are unknown. This additional information about system nonlinearity makes it possible to design an adaptive controller for a nonlinearly parameterized system. The control law is similar to that of a conventional adaptive control technique, while its parameter-update rule is based on the local search method. A parameter-update law is derived so that the time-derivative of the Lyapunov function is negative in the region of interest. Simulation results have shown that this adaptive controller is capable of a good performance. This work was presented in part at the Fifth International Symposium on Artificial Life and Robotics, Oita, Japan, January 26–28, 2000     

Systematic design of a stable type-2 fuzzy logic controller

Stability is one of the more important aspects in the traditional knowledge of automatic control. Type-2 fuzzy logic is an emerging and promising area for achieving intelligent control (in this case, fuzzy control). In this work we use the fuzzy Lyapunov synthesis as proposed by Margaliot and Langholz [M. Margaliot, G. Langholz, New Approaches to Fuzzy Modeling and Control: Design and Analysis, World Scientific, Singapore, 2000] to build a Lyapunov stable type-1 fuzzy logic control system, and then we make an extension from a type-1 to a type-2 fuzzy logic control system, ensuring the stability on the control system and proving the robustness of the corresponding fuzzy controller.     

Takagi-Sugeno fuzzy model based indirect adaptive fuzzy observer and controller design

This paper proposes the design scheme of the alternative adaptive observer and controller based on the Takagi-Sugeno (T-S) fuzzy model. The T-S fuzzy modeling and the state feedback control technique are adopted for the simple structure. The proposed method maintains consistent performance in the presence of parameter uncertainties and incorporates linguistic fuzzy information from human operators. In addition, with the simple adaptive state feedback controller, it solves the singularity problem, which occurs in the inverse dynamics based on the feedback linearization method. Using Lyapunov theory and Lipschitz condition, the stability analysis is conducted, and the adaptive law is derived. The proposed method is applied to the stabilization problem of a flexible joint manipulator in order to guarantee its performance.     

基于波波夫稳定判据的模糊控制器系统化设计方法

利用模糊控制器具有的输入输出比例特性及非线性系统的波波夫稳定判据, 提出了在保证被控系统稳定的前提下模糊控制器的系统化设计方法. 仿真结果表明, 应用此种方法设计的模糊控制器可以确保系统的稳定性.     

一种改进的模糊PD 控制器

充分利用非线性跟踪微分器获得高质量微分信号的特性，将跟踪微分器与传统的简单模糊PD控制器相结合，提出一种简单的高性能的改进的模糊PD控制器．该改进模糊控制器的最显著特点是对测量噪声的强鲁棒性和工程易实现性．数值仿真证明了其有效性和高效性．     

A novel design of high-sensitive fuzzy PID controller

A hybrid model is designed by combining the genetic algorithm (GA), radial basis function neural network (RBF-NN) and Sugeno fuzzy logic to determine the optimal parameters of a proportional-integral-derivative (PID) controller. Our approach used the rule base of the Sugeno fuzzy system and fuzzy PID controller of the automatic voltage regulator (AVR) to improve the system sensitive response. The rule base is developed by proposing a feature extraction for genetic neural fuzzy PID controller through integrating the GA with radial basis function neural network. The GNFPID controller is found to possess excellent features of easy implementation, stable convergence characteristic, good computational efficiency and high-quality solution. Our simulation provides high sensitive response (∼0.005 s) of an AVR system compared to the real-code genetic algorithm (RGA), a linear-quadratic regulator (LQR) method and GA. We assert that GNFPID is highly efficient and robust in improving the sensitive response of an AVR system.     

Adaptive sliding mode controller design based on T-S fuzzy system models

An adaptive sliding mode control (ASMC) technique based on T-S fuzzy system models is proposed in this paper for a class of perturbed nonlinear MIMO dynamic systems in order to solve tracking problems. A T-S fuzzy model is firstly formed by utilizing fuzzy theorem to amalgamate a set of linearized dynamic equations. The adaptive sliding mode controller is then designed based on this fuzzy model with perturbations. The proposed control scheme can drive the dynamics of controlled system into a designated sliding surface in finite time, and guarantee the property of asymptotical stability. It is also shown that the information of upper bound of modeling errors as well as perturbations, except the information of upper bound of input uncertainty, is not required when using the proposed controller.     

基于文化遗传算法的水浴温度模糊控制器设计

水浴温度控制普遍存在滞后、超调、波动性大等特点,且难以建立精确的数学模型,一般采用模糊控制器实现对水浴温度的控制,但模糊控制规则具有较强的主观性,其控制性能往往达不到客观要求。研究遗传算法在模糊控制规则寻优上的应用,将其和文化算法相结合,构成一种具有双层协同进化机制的文化遗传算法,采用专家经验的知识信息构成信仰空间作为指导方向,然后在群体空间中进行遗传算法操作,选取群体中适应度值较高的个体更新信仰空间,再通过迭代优化,得到适应度值高的个体,将其作为优化的模糊控制规则。仿真实验表明,优化后的规则在控制过程中的动态温度跟踪和稳态误差方面均具有较好的效果。