模糊自整定PID控制器的设计与仿真

常规PID控制器因结构简单、鲁棒性好、参数调整方便等,常被用于工业过程控制.但其参数整定是在获取被控对象数学模型的基础上根据一定的规则来确定的,难以适应复杂多变的控制系统.针对其参数整定效果不良、调试时间长、对被控对象适应性差等缺点,将模糊控制与PID控制相结合,设计了模糊自整定PID控制器.在常规PID控制器基础上,根据相应的模糊规则进行模糊推理,实现PID参数的在线自整定.仿真结果表明,模糊自整定PID控制器,不仅具有模糊控制快速、适应性强等优点,又有PID控制精确度高的特点,使系统有较好的控制作用,因此具有较好的工业应用前景.     

模糊自整定PID的航空发动机转速控制研究

航空发动机具有强非线性和强时变性的特点,使用定参数PID方法的转速控制系统的性能在全飞行包线内难以保证.针对上述问题,提出设计模糊自整定PID控制器,利用输入误差及变化率建立一组PID参数在线调整规则,运用模糊推理方法实时进行参数自整定.结合某型航空发动机核心机非线性实时模型,进行转速串级控制硬件在回路仿真.结果表明,提出的模糊自整定PID控制方法实现了控制器参数在线调整,参数切换扰动小,满足全包线内转速控制的指标要求.     

不稳定时滞对象的最优鲁棒二自由度控制器解析设计

针对不稳定时滞对象提出一种二自由度控制器解析设计方法．通过内模控制规律进行控制器设计,改进内模控制的滤波器提高系统的鲁棒性及其性能．同时对系统的稳定性、鲁棒性及性能进行分析,提出了严格基于模型信息的鲁棒指标选择规则,保证系统获得良好的鲁棒/性能平衡．最后,将控制器的参数整定转化为一个约束优化问题,提出控制器解析整定方法,并通过仿真实例验证该方法的有效性．     

PID控制器改进方法研究

分析了常规PID(比例—积分—微分)控制特点,针对其在非线性控制中存在的问题,基于参数自整定和控制器误差组合方式,分别设计了参数自整定模糊PID控制器、参数自整定RBF神经网络PID控制器和非线性自抗扰PD控制器.并进行了实验验证,实验结果表明,从参数自整定和控制器误差组合角度出发,所采用的控制策略可使非线性系统具有较好的动态特性、鲁棒性和自适应能力.     

基于参数自整定模糊控制的永磁同步电机矢量控制调速系统

针对采用SVPWM调制的永磁同步电动机的矢量控制系统,介绍参数自整定模糊控制策略,该策略吸收了空间脉宽调制、模糊控制和PID控制的优点,能够按照被控制对象的性能要求,通过模糊控制规则自动整定控制器参数,速度调节采用模糊自整定控制器,改善了系统的性能.仿真结果表明,基于参数自整定模糊控制与常规PID控制的水磁同步电机调速系统相比,具有良好的动态,稳态性能以及较强的鲁棒性,从而证明了这种设计方法的合理性和优越性.     

小型电加热反应器温度的模糊自适应整定PID控制

小型电加热反应器系统具有较大的纯滞后、惯性滞后、非线性和时变特性,参数固定不变的普通PID控制器难以进行精确温控.通过把操作人员积累的PID参数整定经验知识总结成模糊规则,利用模糊逻辑推理进行在线实时整定,设计了电加热反应器温度模糊自适应整定PID控制算法.通过Matlab与组态软件"组态王"KINGVIEW的动态数据交换,在Matlalb上编程实现了模糊自适应整定PID控制算法.进行了一般情况下和具有较强非线性和时变特性情况下温度控制实验,实验结果表明,模糊自适应整定PID控制取得了比普通PID更好的控制结果,模糊自适应整定PID控制对过程非线性和时变特性具有更强的适应性.     

模糊PID控制器及模糊参数整定器的设计和应用

针对非线性和时变性系统的PID控制,应用模糊集合论,并充分考虑人工整定的经验,设计了由模糊参数整定器和变参数PID控制器构成的新的模糊PID控制器。运行结果表明,在控制非线性和时变对象时,其控制效果优于数字PID控制器。     

支持向量机在PID控制器参数整定中的仿真研究

PID控制是应用最为广泛的控制方法,由于系统中存在非线性和时变性,影响建立精确的模型,系统性能.为了解决控制参数整定,改善系统性能,提出一种基于支持向量机的PID控制器参数整定方法.通过将支持向量机和PID控制器相结合建立支持向量机的参数整定模型,在控制过程中将PID控制的参数作为支持向量机的输入,构造参数自适应学习的PID控制器,在控制过程中动态调整PID的三个控制参数,进行仿真的在线整定.仿真结果表明,支持向量机的PID控制方法在处理非线性和时变系统时,提高了实时性能,增强系统稳定性,并获得更好的控制效果,为通用非线性PID控制器设计提供了依据.     

基于模糊自适应PID的无人机纵向姿态控制研究

针对无人机的典型的非线性、控制参数时变以及建模复杂的特性,设计了一种基于模糊自适应PID的控制器来实现对无人机纵向姿态的控制;该控制器以误差e和误差变化率ec作为输入,可以满足不同时刻e和ec对PID参数自整定的要求;利用模糊控制规则在线对PID参数进行修正,从而使被控对象具有更好的动、静态性能;仿真结果表明,设计的模糊自适应PID控制器具有响应快及超调小的特性,而且自适应能力也较强.     

SR电机模糊自整定PID控制器设计与仿真

针对开关磁阻电机的非线性、时变和强耦合性,提出了模糊自整定PID的调速控制策略.在分析SR电机非线性数学模型的基础上,将速度偏差和偏差变化率作为控制器输入,PID的三个控制参数为输出.利用模糊规则对PID参数进行在线修改,实现PID参数的自动最佳调整.通过将MATLAB中的Fuzzy Toolbox和SIMULINK有机结合,实现了基于模糊自整定PID控制器的开关磁阻电机调速系统仿真,并进行了传统PID控制与模糊自整定PID控制的仿真比较.结果表明:模糊自整定PID控制器能够明显改善系统的调速性能,具有较强的鲁棒性,并且计算量少,易于实现,便于工程应用.     

PID auto-tuning using new model reduction method and explicit PID tuning rule for a fractional order plus time delay model

In this paper, a new model reduction method and an explicit PID tuning rule for the purpose of PID auto-tuning on the basis of a fractional order plus time delay model are proposed. The model reduction method directly fits the fractional order plus time delay model to frequency response data by solving a simple single-variable optimization problem. In addition, the optimal tuning parameters of the PID controller are obtained by solving the Integral of the Time weighted Absolute Error (ITAE) minimization problem and then, the proposed PID tuning rule in the form of an explicit formula is developed by fitting the parameters of the formula to the obtained optimal tuning parameters. The proposed tuning method provides almost the same performance as the optimal tuning parameters. Simulation study confirms that the auto-tuning strategy based on the proposed model reduction method and the PID tuning rule can successfully incorporate various types of process dynamics.     

Time-varying controller for known nonlinear dynamic systems with guaranteed stability

This paper presents a solution for the design of a time-varying parametric controller for general nonlinear dynamic systems. The controller can be of any prespecified smooth nonlinear state feedback type so long as it includes a set of time-varying parameters. A Lyapunov function is constructed and used to formulate an effective tuning rule for the involved time-varying parameters. With this selection of the tuning rule, it has been shown that the closed loop system is stable. Two examples are included to illustrate the use of the proposed methods and encouraging results have been obtained.     

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.     

Genetic tuning of fuzzy rule deep structures preserving interpretability and its interaction with fuzzy rule set reduction

Tuning fuzzy rule-based systems for linguistic fuzzy modeling is an interesting and widely developed task. It involves adjusting some of the components of the knowledge base without completely redefining it. This contribution introduces a genetic tuning process for jointly fitting the fuzzy rule symbolic representations and the meaning of the involved membership functions. To adjust the former component, we propose the use of linguistic hedges to perform slight modifications keeping a good interpretability. To alter the latter component, two different approaches changing their basic parameters and using nonlinear scaling factors are proposed. As the accomplished experimental study shows, the good performance of our proposal mainly lies in the consideration of this tuning approach performed at two different levels of significance. The paper also analyzes the interaction of the proposed tuning method with a fuzzy rule set reduction process. A good interpretability-accuracy tradeoff is obtained combining both processes with a sequential scheme: first reducing the rule set and subsequently tuning the model.     

Hybrid identification of fuzzy rule‐based models

In this study, we propose a hybrid identification algorithm for a class of fuzzy rule‐based systems. The rule‐based fuzzy modeling concerns structure optimization and parameter identification using the fuzzy inference methods and hybrid structure combined with two methods of optimization theories for nonlinear systems. Two types of inference methods of a fuzzy model concern a simplified and linear type of inference. The proposed hybrid optimal identification algorithm is carried out using a combination of genetic algorithms and an improved complex method. The genetic algorithms determine initial parameters of the membership function of the premise part of the fuzzy rules. In the sequel, the improved complex method (being in essence a powerful auto‐tuning algorithm) leads to fine‐tuning of the parameters of the respective membership functions. An aggregate performance index with a weighting factor is proposed in order to achieve a balance between performance of the fuzzy model obtained for the training and testing data. Numerical examples are included to evaluate the performance of the proposed model. They are also contrasted with the performance of the fuzzy models existing in the literature. © 2002 John Wiley & Sons, Inc.     

非线性系统的主动容错控制

对一般的非线性系统提出了一种新的主动容错控制方法.系统正常工作时,采用基于迭代学习观测器的输出反馈控制策略,控制器为迭代学习观测器的状态和调节参数的函数,此输出反馈控制器能良好地镇定该非线性系统.当系统发生故障后,进行控制器重组,在调节参数的自适应调节律中引入了故障估计的信息,使得系统发生故障后包含故障估计信息的重组控制器仍然能使系统稳定,实现了非线性系统的主动容错控制.计算机模拟显示所提出算法的有效性.     

Two-level tuning of fuzzy PID controllers

Fuzzy PID tuning requires two stages of tuning; low level tuning followed by high level tuning. At the higher level, a nonlinear tuning is performed to determine the nonlinear characteristics of the fuzzy output. At the lower level, a linear tuning is performed to determine the linear characteristics of the fuzzy output for achieving overall performance of fuzzy control. First, different fuzzy systems are defined and then simplified for two-point control. Non-linearity tuning diagrams are constructed for fuzzy systems in order to perform high level tuning. The linear tuning parameters are deduced from the conventional PID tuning knowledge. Using the tuning diagrams, high level tuning heuristics are developed. Finally, different applications are demonstrated to show the validity of the proposed tuning method.     

非线性伺服电动机的神经网络逆控制

伺服电动机由于存在接触过程的非线性、温漂等非线性因素的影响,很难建立其精确的数学模型,使得基于数学模型的控制困难.针对伺服电动机存在的非线性问题,提出了一种新颖的基于BP神经网络直接逆控制方法.首先,利用BP神经网络建立系统的正向模型(NNI),然后,设计基于神经网络的直接逆控制器(NNC),实现了对伺服电动机的自适应控制.在Lyapunov稳定性分析的基础上,给出了BP算法学习算子的选择方案,保证神经网络权值训练的快速收敛,同时,对训练BP神经网络控制器的专用算法(specialized learning)进行改进,利用NNI的输出求取权值调整的灵敏度函数.数字仿真结果表明提出的控制算法是简单有效的.     

IMC based Robust PID design: Tuning guidelines and automatic tuning

This communication addresses the problem of tuning a PID controller for step response. The tuning is based upon a First Order Plus Time Delay (FOPTD) model and aims to achieve a step response specification while taking into account robustness considerations. The industrial ISA-PID formulation is chosen. A tuning rule is derived first where the four parameters of the ISA-PID are determined by means of two new parameters: one parameter is related to the desired closed-loop time constant and the other one to the robustness level. On a second step, these two parameters are set to a fixed value in order to get a simple and automatic rule that directly gives the controller parameters in terms of the process model parameters. The proposed automatic tuning rule is compared with other known tunings.     

Control‐oriented modeling and robust nonlinear triple‐step controller design for an air‐feed system for polymer electrolyte membrane fuel cells

The efficient operation of polymer electrolyte membrane fuel cells (PEMFCs) significantly relies on the reliable control of air‐feed system. The core control objective in air‐feed system is to track a pre‐defined reference of the oxygen excess ratio to avoid oxygen starvation and stack damage. In this paper, we focus on the modeling of the air‐feed system in a PEMFC and the robust nonlinear controller design for the oxygen excess ratio tracking control. To facilitate the subsequent nonlinear controller design, a specific affine‐like, second‐order, control‐oriented model of oxygen excess ratio dynamic behavior is developed, and the modeling uncertainty is estimated and compensated by using an extended state observer (ESO). The control‐oriented model is verified via a high‐fidelity plant model. A nonlinear controller for oxygen excess ratio tracking control is proposed based on the triple‐step technique which is robust against the system disturbances. The tuning rule of the controller parameters is discussed in the scheme of the linear system. Finally, simulations are conducted to demonstrate the effectiveness and advantages of the proposed controller under variant operating conditions compared with baseline controllers.