Developments of fuzzy PID controllers

Abstract: This paper describes the development and tuning methods for a novel self-organizing fuzzy proportional integral derivative (PID) controller. Before applying fuzzy logic, the PID gains are tuned using a conventional tuning method. At supervisory level, fuzzy logic readjusts the PID gains online. In the first tuning method, fuzzy logic at the supervisory level readjusts the three PID gains during the system operation. In the second tuning method, fuzzy logic only readjusts the proportional PID gain, and the corresponding integral and derivative gains are readjusted using the Ziegler–Nichols tuning method while the system is in operation. For the compositional rule of inferences in the fuzzy PID and the self-organizing fuzzy PID schemes two new approaches are introduced: the min implication function with the mean of maxima defuzzification method, and the max-product implication function with the centre of gravity defuzzification method. The fuzzy PID controller, the self-organizing fuzzy PID controller and the PID controller are all applied to a non-linear revolute-joint robot arm for step input and path tracking experiments using computer simulation. For the step input and path tracking experiments, the novel self-organizing fuzzy PID controller produces a better output response than the fuzzy PID controller; and in turn both controllers exhibit better process output than the PID controller.     

自适应模糊-PID控制器在链梯速度控制系统中的应用

链梯速度具有随负载而变化的时变特性,当负载变化时,采用传统的PID控制很难满足要求,为此本文提出了一种基于模糊推理的PID控制器,它利用FUZZY推理,对系统所处的不同状态实现PID参数的在线自适应控制,使系统在任何状态下,都能得到比传统的PID更好的控制效果和更强的鲁棒性,仿真结果也证明了这一点。     

Analysis of direct action fuzzy PID controller structures

The majority of the research work on fuzzy PID controllers focuses on the conventional two-input PI or PD type controller proposed by Mamdani (1974). However, fuzzy PID controller design is still a complex task due to the involvement of a large number of parameters in defining the fuzzy rule base. This paper investigates different fuzzy PID controller structures, including the Mamdani-type controller. By expressing the fuzzy rules in different forms, each PLD structure is distinctly identified. For purpose of analysis, a linear-like fuzzy controller is defined. A simple analytical procedure is developed to deduce the closed form solution for a three-input fuzzy inference. This solution is used to identify the fuzzy PID action of each structure type in the dissociated form. The solution for single-input-single-output nonlinear fuzzy inferences illustrates the effect of nonlinearity tuning. The design of a fuzzy PID controller is then treated as a two-level tuning problem. The first level tunes the nonlinear PID gains and the second level tunes the linear gains, including scale factors of fuzzy variables. By assigning a minimum number of rules to each type, the linear and nonlinear gains are deduced and explicitly presented. The tuning characteristics of different fuzzy PID structures are evaluated with respect to their functional behaviors. The rule decoupled and one-input rule structures proposed in this paper provide greater flexibility and better functional properties than the conventional fuzzy PHD structures.     

Decomposed fuzzy proportional–integral–derivative controllers

In this paper, several types of decomposed proportional–integral–derivative fuzzy logic controllers (PID FLCs) are tested and compared. An important feature of decomposed PID FLCs are their simple structures. In its simplest version, the decomposed PID FLC uses three one-input one-output inferences with three separate rule bases. Behaviours of proportional, integral and derivative PID FLC parts are defined with simple rules in proportional rule base, integral rule base and derivative rule base. The proposed decomposed PID FLC has been compared with several PID FLCs structures. All PID FLCs have been realised by the same hardware and software tools and have been applied as a real-time controller to a simple magnetic suspension system.     

关于模糊PID控制器推理机维数的研究

对一维(1D)至三维(3D)模糊PID控制器进行了系统的分析研究,提出了四项系 统功能特性指标来评价不同结构的控制器;这包括控制分量合成,耦合影响,增益相关和规则 增长.通过对最常见的二维Mamdani模糊控制器进行分析研究,发现该控制器存在功能缺 陷.为此,提出了最优结构的一维模糊PID控制器.该控制器采用了"1D-3D"映射关系的模糊 推理机,从而实现了三个控制分量可以独立不相关的调整功能.通过与二维和三维控制器比 较结果表明,一维控制器具有最佳系统功能特性.     

新型模糊- PID复合控制器设计及应用

提出一种模糊-PID复合控制器的设计方法，通过PID线性控制器和模糊控制器并行结合，在偏离工作点较远的区域以模糊控制为主，工作点附近则主要使用PID线性控制。为保证两者的平稳过渡，采用模糊推理完成“切换”(模糊切换)，仿真结果表明该控制器实现了PID控制器和模糊PID控制器的优势互补和控制性能的明显改进。     

基于变论域模糊PID的分解炉温度控制研究

分解炉温度控制系统具有非线性、时变、纯滞后的特点,针对传统PID控制及模糊.PID控制难以很好地满足控制要求,提出了一种变论域模糊自适应PID控制方法。利用变论域思想,设计了一种基于函数模型的伸缩因子控制器,动态地调整模糊控制器的量化因子和比例因子,提高了控制精度。在Matlab环境下分别对PID控制、模糊PID控制和变论域模糊PID控制方法进行了仿真对比,结果表明变论域模糊PID控制方法具有更好的动静态性能和自适应能力。     

一种参数自调整PID模糊控制器

结合传统PID控制原理 ,提出一种新型模糊控制器结构 ,即PID型模糊控制器。为提高PID控制器性能 ,设计能在线调整PID参数的模糊控制方法。仿真结果表明 ,自调整参数PID型模糊控制器使系统在暂态响应及稳态性能方面性能优良。     

模糊PID控制器设计及MATLAB仿真

将模糊控制器和PID控制器结合在一起,构造了一个模糊PID控制器,利用模糊推理的方法调整PID控制器的参数,并利用MATLAB的SUMLINK工具箱,对系统进行仿真,仿真结果表明模糊PID控制器能使系统达到满意的控制效果.     

模糊自整定PID控制器在再热汽温控制中的应用研究

针对工业过程中再热汽温等一类大迟延惯性时变对象,采用模糊自整定PID控制器对PID参数调节和优化,该方法将模糊技术与PID控制综合起来,实现了PID控制的智能化。仿真结果表明:与常规PID控制器相比,模糊自整定PID控制器具有很强的适应性、鲁棒性和抗干扰性。