半导体泵浦固体激光器智能温控策略与算法研究

针对半导体泵浦固体激光器温度控制过程中存在的非线性、大滞后特性,提出一种基于仿人智能策略的前馈组合控制器。前馈控制部分通过在线优化滞后参数估计值以获得被控对象的静态逆模型,逆模型辨识与直接逆控制由两个神经网络分别实现,可以在线调整网络权值。基本模糊控制器可在模糊PID和模糊PD控制算法之间进行转换,适用于控制系统的各工作状态。仿人智能控制策略根据输出误差变化的不同阶段调整所使用的控制组合,并具有自寻优功能。仿真表明,该方法可取得较好的控制效果。     

纯滞后系统的逆动态模型复合控制

提出了一种基于小脑模型神经网络的纯滞后对象控制方案。该方案通过在线学习被控对象的逆动态模型,利用前馈控制原理实现快速跟踪控制,克服了传统的Smith控制方法的不足。该方法具有不需建立精确模型、响应快速和鲁棒性强等特点。仿真结果表明控制器具有优良的品质。     

原料预热温度的模糊PID-神经元控制

针对具有不确定性、大纯滞后的催化裂化反应再生装置原料预热温度控制,提出了一种模糊PID-神经元控制方法.从介绍催化裂化反应再生装置原料预热被控对象的建模、神经元非模型控制和公式化的模糊控制方法人手,建立了模糊PID-神经元控制系统,设计了模糊神经元混合控制器,并使用神经元来在线调整模糊PID控制器的模糊规则.仿真实验结果表明所提出的模糊PID-神经元控制方法具有强鲁棒性,能有效控制具有大纯滞后和不确定性的对象.     

基于模糊树模型的自适应直接逆控制

基于模糊树模型, 结合神经网络中的逆向学习和专门化学习, 提出了自适应直接逆控制方法. 首先离线辨识对象的逆模型作为初始的控制器, 然后与对象串联, 用最小均方差 (Least mean square, LMS) 算法在线调节控制器中的线性参数. 本方法辨识得到的逆模型控制器可以减少需要的模糊规则数目, 同时达到较好的跟踪控制效果. 仿真结果表明了方法的有效性.     

水平连铸计算机控制系统中的Smith-Fuzzy控制器

介绍了一种可补偿纯滞后的模型参考自学习模糊控制器 ,改善了普通Fuzzy控制器对大滞后工业过程的控制效果 ,通过模糊逆模型修改控制表 ,表现了较高的智能。以水平连铸控制系统为对象 ,进行了仿真研究 ,结果表明这种方法是可行的和有效的 ,具有很好的鲁棒性。     

基于神经网络的迟滞非线性补偿控制

提出了一种基于神经网络的迟滞非线性的补偿方法.首先构造一个Duhem逆算子来描述迟滞逆状态.然后利用神经网络来逼近此状态和输出之间的关系来得到神经网络迟滞逆模型,神经网络权值采用反馈误差学习方法来进行在线调整.系统的前馈控制器和反馈控制器分别为逆模型和PID控制器.该方法不需要建立迟滞的正模型,能够在线构造逆模型来实现迟滞补偿.最后通过仿真验证了该方法的有效性.     

水平连铸计算机控制系统中的Smith—Fuzzy控制器

介绍了一种可补偿纯滞后的模型参考自学习模糊控制器,改善了普通Fuzzy控制器对大滞后工业过程的控制效果,通过模糊逆模型修改控制表,表现了较高的智能。以水平连铸控制系统为对象,进行了仿真研究,结果表明这种方法是可行的和有效的,具有很好的鲁棒性。     

一种新型仿人智能模糊混合控制器的研究

本文将智能控制算法和PI控制算法相结合,提出了一种基于PI的模糊混合控制器.新控制器在控制过程中借助仿人智能算法实现控制参数的在线调整.仿真结果表明,该控制方法对非线性时变系统有较好的控制效果.     

风力发电机组的变桨距复合控制

针对变桨距风力发电机组的强非线性和时滞特性,受参数变化和外部干扰严重,提出了基于前馈补偿和模糊PID的复合控制策略来实现变桨距功率控制.将模糊控制与常规的PID控制相结合,设计了模糊自整定PID控制器对控制参数进行在线调整.考虑风速是一个可测的主要扰动,设计了前馈补偿器,采用分段比例控制补偿一个适当的前馈桨距角,在额定风速以上时作用于系统,实现按扰动的快速补偿.变桨距控制系统采用S7-1200PLC作控制器,编写程序实现了变桨距复合控制.试验表明,系统抗扰能力强,控制效果良好.     

基于支持向量机的船舶锅炉智能控制研究

在非线性系统逆控制的思想基础上,采用SVM辨识系统的逆模型,设计并实现了基于SVM的逆控制.为克服基于SVM输入输出数据辨识的逆模型不精确引起的逆控制不足,提出了一种SVM逆控制与模糊控制相结合的复合控制策略.在复合控制中,模糊控制器主要用来实现前期控制快速响应的要求.在此期间,逆辨识器在线调整其参数并完全传递给逆控制器.SVM逆控制器在后期起主要作用,以消除对被控系统的稳态误差.由于此复合控制系统中增加了反馈环节,因而比直接逆控制具有更优的性能和鲁棒性,有效解决了逆控制模型精确度要求的问题.将其应用于用锅炉蒸汽压力控制系统中,仿真证明了该复合智能控制策略是有效和实用的.     

Hybrid control of a pneumatic artificial muscle (PAM) robot arm using an inverse NARX fuzzy model

We investigated the possibility of applying a hybrid feed-forward inverse nonlinear autoregressive with exogenous input (NARX) fuzzy model-PID controller to a nonlinear pneumatic artificial muscle (PAM) robot arm to improve its joint angle position output performance. The proposed hybrid inverse NARX fuzzy-PID controller is implemented to control a PAM robot arm that is subjected to nonlinear systematic features and load variations in real time. First the inverse NARX fuzzy model is modeled and identified by a modified genetic algorithm (MGA) based on input/output training data gathered experimentally from the PAM system. Second the performance of the optimized inverse NARX fuzzy model is experimentally demonstrated in a novel hybrid inverse NARX fuzzy-PID position controller of the PAM robot arm. The results of these experiments demonstrate the feasibility and benefits of the proposed control approach compared to traditional PID control strategies. Consequently, the good performance of the MGA-based inverse NARX fuzzy model in the proposed hybrid inverse NARX fuzzy-PID position control of the PAM robot arm is demonstrated. These results are also applied to model and to control other highly nonlinear systems.     

基于CAFSA-FNN的扭矩加载系统控制研究*

针对磁粉制动器扭矩加载系统的非线性和滞后性,提出了一种基于混沌人工鱼群-模糊神经网络(CAFSA-FNN)PID控制器。该控制器采用基于Mamdani模型的模糊神经网络来整定PID控制器的控制参数,并结合混沌人工鱼群算法离线粗调和BP算法在线细调来学习和调整模糊神经网络的参数。利用Matlab进行离线仿真优化,在此基础上使用PID控制器、模糊神经网络控制器、人工鱼群-模糊神经网络控制器以及本文设计的控制器进行磁粉制动器扭矩加载实验,实验结果证明了该控制器的稳定性、快速性和有效性,能够解决滞后性问题。     

Self-adaptive interval type-2 neural fuzzy network control for PMLSM drives

This paper proposes a self-adaptive interval type-2 neural fuzzy network (SAIT2NFN) control system for the high-precision motion control of permanent magnet linear synchronous motor (PMLSM) drives. The antecedent parts in the SAIT2NFN use interval type-2 fuzzy sets to handle uncertainties in PMLSM drives, including payload variation, external disturbance, and sense noise. The SAIT2NFN is firstly trained to model the inverse dynamics of PMLSM through concurrent structure and parameter learning. The fuzzy rules in the SAIT2NFN can be generated automatically by using online clustering algorithm to obtain a suitable-sized network structure, and a back propagation is proposed to adjust all network parameters. Then, a robust SAIT2NFN inverse control system that consists of the SAIT2NFN and an error-feedback controller is proposed to control the PMLSM drive in a changing environment. Moreover, the Kalman filtering algorithm with a dead zone is derived using Lyapunov stability theorem for online fine-tuning all network parameters to guarantee the convergence of the SAIT2NFN. Experimental results show that the proposed SAIT2NFN control system achieves the best tracking performance in comparison with type-1 NFN control systems.     

基于计算型模糊推理算法的模糊控制器设计

针对传统模糊推理算法在推理过程中容易忽略部分推理信息,模糊规则一旦确定就难以调整的缺点,提出一种基于数值计算的模糊推理算法。算法采用数值计算的方法对推理过程进行了改进,这种改进能够充分考虑所有输入的影响,又能根据输入的变化,对模糊规则进行适当的调整。基于该算法的模糊控制器能够大大提高控制性能和精度,减小稳态误差。通过对直流电动机的仿真控制效果表明,该控制器比传统模糊控制器的控制性能好,精度高,抗干扰能力强。     

Fuzzy modelling control for aircraft automatic landing system

Aircraft landing control based on fuzzy modelling networks is presented. The proposed scheme uses a fuzzy controller combined with a linearized inverse aircraft model. A multi-layered fuzzy neural network is used as the controller, providing the control signals at each stage of the aircraft-landing phase. The algorithm used to train the network is the Backpropagation Through Time. The linearized inverse aircraft model provides the error signals that will be used to back-propagate through the controller at each stage. The objective of this study is to improve the performance of conventional automatic landing systems. The simulation results are described for the automatic landing system of a commercial aeroplane. Tracking performance and robustness are demonstrated through software simulations. Simulation results show that the fuzzy controller can successfully expand the safety envelope to include more hostile environments such as severe turbulence.     

基于模糊模型的非线性内模控制策略研究

针对一类非线性动态过程提出了基于模糊模型的非线性内模控制算法(NFIMC)。NFIMC控制器包括逆模糊模型控制器和滤波器。过程的模糊模型和逆模糊模型均可由模糊辨识获得。CSTR的仿真结果表明:该算法可以对强非线性过程实现有效控制,并且具有结构简单、计算效率高等优点,有利于在线应用。     

基于模糊专家模型的神经控制器及应用研究

针对具有严重非线性特性的pH中和过程,提出了一种基于模糊专家模型的神经控制策略,这种方法将神经网络逆控制器与神经元PID控制器相结合,并利用模糊专家模型所得到的预报结果来调整神经元PID的权值。仿真试验表明该方法能有效改善控制性能,所提出的方法实现了对pH过程的有效控制,并且有很强的适应性。     

Adaptive fuzzy model based inverse controller design using BB-BC optimization algorithm

The use of inverse system model as a controller might be an efficient way in controlling non-linear systems. It is also a known fact that fuzzy logic modeling is a powerful tool in representing nonlinear systems. Therefore, inverse fuzzy model can be used as a controller for controlling nonlinear plants. In this context, firstly, a new fuzzy model based inverse controller design methodology is presented in this study. The design methodology introduced here is based on a recursive optimization procedure that searches for an optimal inverse model control signal at every sampling time. Since the task of optimization should be accomplished in between two sampling periods the use of a fast optimization algorithm becomes essential. For this reason, Big Bang-Big Crunch (BB-BC) optimization algorithm is used due to its low computational time and high global convergence properties. Even though, inverse model controllers may produce perfect control while operating in an open loop fashion, this open loop control would not be sufficient in the case of modeling mismatches or disturbances that might occur over the system. In order to overcome this problem, secondly, an on-line adaptation mechanism via BB-BC optimization algorithm is introduced in addition to BB-BC optimization based fuzzy model inverse controller. The adaptation mechanism is used to update the related parameters of the model while minimizing the absolute value of the instantaneous error between the system and model outputs. In this manner, the system output is somehow fed back, the overall control form can be considered as a closed-loop system. The new fuzzy model based inverse control scheme with the new online adaptation mechanism has been implemented and tested on the two real time processes; namely, heat transfer and pH processes and very satisfactory results has been reported.     

基于模糊RBF神经网络的PID及其应用

针对传统的PID控制器参数固定而导致在控制中效果差的问题,提出一种基于模糊RBF神经网络智能PID控制器的设计方法。该方法结合了模糊控制的推理能力强与神经网络学习能力强的特点,将模糊控制与RBF神经网络相结合以在线调整PID控制器参数,整定出一组适合于控制对象的kp, ki, kd参数。将算法运用到电机控制系统的PID参数寻优中,仿真结果表明基于此算法设计的PID控制器改善了电机控制系统的动态性能和稳定性。