UC轧机二次型板形缺陷模糊神经网络控制

针对UC轧机中间辊弯辊回路的非线性过程精确控制问题,将模糊控制与神经网络结合,设计模糊神经网络控制器并将其应用于对中间辊弯辊回路的控制中。介绍了由神经网络结构组成的模糊控制器,网络完成模糊化、模糊推理、清晰化操作,模糊规则和系统的输入输出由梯度下降法进行调整。仿真结果表明,这种模糊神经网络控制器能很好地跟踪二次板形的目标设定值,系统的响应快,超调小,鲁棒性强。     

参数自调整模糊控制器在空调器中的应用

针对空调系统的复杂情况,介绍了一种在线调整常规模糊控制器的量化因子和比例因子的设计方法,实时对常规模糊控制器进行优化。仿真表明,其动态性能和稳态性能都优于常规模糊控制器,具有良好的控制性能。     

参数自调整模糊控制器在微气压传感器测控系统中的应用研究

为了对真空测控系统进行动态气压控制,提出了一种参数自调整模糊控制器的设计方法。该方法利用参数调整控制器在线自动修正基本模糊控制器的量化因子和比例因子,从而改善基本模糊控制器对任意设定气压的控制性能。此外,在控制算法基础上引入计数器Counter来消除系统的频繁振荡。实验结果表明,该方案具有较好的控制效果,基本上解决了传感器所需真空测试环境1kPa～100kPa的动态气压控制问题。     

基于改进双种群遗传算法的模糊控制器的优化

遗传算法是一种自适应、启发式、群体型、概率性、迭代式全局收敛算法,利用遗传算法的良好的搜索特性来优化模糊控制器,可以取得很好的控制效果.本文对传统的双种群遗传算法进行了归纳和分析,在此基础上提出了一种改进的双种群遗传算法(CGDPGA).将此改进算法用于优化模糊控制器的隶属度函数、量化因子和比例因子来实现模糊控制器的全...     

一种专家智能融合控制策略及其应用

介绍一种将智能控制理论及专家系统知识进行融合,用遗传算法离线优化了带多个调整因子的模糊控制器的调整因子,然后用专家知识对模糊控制器的量化因子和比例因子进行在线调节。该控制策略提高了模糊控制器的响应特性,应用于典型电液伺服系统中,改善了系统性能,有效地抑制了超调和干扰。     

在线自校正模糊PID控制器的研究

该文先设计一个基于T—S模型的模糊PID控制器,为提高模糊控制的适应性,在分析量化因子和比例因子对系统性能影响的基础上,又制定了基于T—S模型的在线调整量化因子和比例因子的模糊调整规则,实现了模糊PID控制器在线自校正。其算法简单,系统实时性、鲁棒性好。对大纯滞后对象的仿真表明,该控制器明显改善系统的动态性能。     

基于免疫遗传算法优化的模糊控制方法及其应用

在模糊控制器的设计过程中，为了使模糊控制器的性能达到全局优化，应用免疫遗传算法对模糊控制器参数进行优化设计；在综合考虑各种参数对控制器性能影响的基础上，给出了一种全面优化隶属度函数、比例因子和量化因子的优化方法；利用了免疫算法能保持个体的多样性和能对学习过程进行引导的特点，对模糊控制器的多个参数同时进行优化，从而显著提高了系统的收敛性、稳定性。应用该方法对数控铣削加工过程的模糊控制器的设计进行了仿真，并与其他方法进行比较和控制实例的验证，表明了该基于免疫遗传算法优化的模糊器能获得更优良的控制性能。     

基于遗传算法的模糊支持向量网络控制

将模糊控制与支持向量网络相结合，设计了一种模糊支持向量网络控制器．该控制器融合了模糊控制与支持向量网络的优点，具有不依赖被控对象模型、泛化能力强等特点．利用遗传算法来优化支持向量机参数和控制器比例因子参数，以期实现最优的控制性能. 仿真结果表明了控制系统具有优良的控制性能．     

FNN模型参考自适应控制在轧机液压弯辊系统中的应用

UC轧机液压弯辊系统的数学模型具有很强的时变性和不确定性,是典型的非线性过程;针对UC轧机液压弯辊系统的特性,提出模糊神经网络模型参考自适应控制策略,并将其应用到液压巧辊控制系统中;仿真结果表明,模糊神经网络模型参考自适应控制能够很好的跟踪参考模型的设定,系统的响应快。     

基于GA的模糊控制器在PWM直流位置控制系统中的应用

设计了一种模糊控制器。其中，模糊控制器输入量和输出量的隶属函数以及量化因子用遗传算法进行优化，并把该控制器用于一个基于PWM技术的直流位置控制系统。与原来采用的PI控制器相比，模糊控制器对系统参数的变化适应性更强，控制效果更好。     

Finishing mill strip gage setup and control by interval type-1 non-singleton type-2 fuzzy logic systems

The setup and control of the finishing mill roll gap positions required to achieve the desired strip head thickness as measured by the finish mill exit X-ray gauge sensor is made by an intelligent controller based on an interval type-2 fuzzy logic system. The controller calculates the finishing mill stand screw positions required to achieve the strip finishing mill exit target thickness. The interval type-2 fuzzy head gage controller uses as inputs the transfer bar thickness, the width and the temperature at finishing mill entry, the strip target thickness, the width and the temperature at finishing mill exit, the stand work roll diameter, the stand work roll speed, the stand entry thickness, the stand exit thickness, the stand rolling force, and the %C of the strip. Taking into account that the measurements and inputs to the proposed system are modeled as type-1 non-singleton fuzzy numbers, we present the so called interval type-1 non-singleton type-2 fuzzy logic roll gap controller. As reported in the literature, interval type-2 fuzzy logic systems have greater non-linear approximation capacity than that of its type-1 counterpart and it has the advantage to develop more robust and reliable solutions than the latter. The experiments of these applications were carried out for three different types of coils, from a real hot strip mill. The results proved the feasibility of the developed system for roll gap control. Comparison against the mathematical based model shows that the proposed interval type-2 fuzzy logic system equalizes the performance in finishing mill stand screw positions setup and enhances the achieved strip thickness under the tested conditions characterized by high uncertainty levels.     

Observer-based multivariable flatness control of a cold rolling mill

The synthesis methodology developed by Kimura (1985) based on the design theory of output regulators essentially due to Wonham (1974) has been applied successfully to the flatness control system for a 6-high cold rolling mill. The system has the following remarkable features.

1. (1) The structure of the controller is simple. This makes it easy to tune the control system.

2. (2) The controller copes well with the detection time delay, and thus high performance is obtained even at a low rolling speed.

3. (3) The flatness error caused by the rolling force variation in mill acceleration and deceleration time would be kept to a minimum by the function to adjust roll bending force using the signal of rolling force.

Path‐Tracking Control of a Riderless Bicycle Via Road Preview and Speed Adaptation

In this study, a genetic‐fuzzy control system is used to control a riderless bicycle where control parameters can adapt to the speed change of the bicycle. The equations of motion are developed for a bicycle with constraints of rolling‐without‐slipping contact condition between the wheels and ground. This controller consists of two loops: the inner is a roll‐angle‐tracking controller which generates steering torque to control the roll angle while guaranteeing the stability, and the outer is a path‐tracking controller which generates the reference roll angle for the inner loop. The inner loop is a sliding‐mode controller (SMC) designed on the basis of a linear model obtained from a system identification process. By defining a stable sliding surface of error dynamics and an appropriate Lyapunov function, the bicycle can reach the roll‐angle reference in a finite time and follow that reference without chattering. The outer loop determines the proper reference roll‐angle by using a fuzzy‐logic controller (FLC) in which previewing and tracking errors are taken into consideration. The robustness of the proposed controller against speed change and external disturbances is verified by simulations.     

A novel fractional order fuzzy PID controller and its optimal time domain tuning based on integral performance indices

A novel fractional order (FO) fuzzy Proportional-Integral-Derivative (PID) controller has been proposed in this paper which works on the closed loop error and its fractional derivative as the input and has a fractional integrator in its output. The fractional order differ-integrations in the proposed fuzzy logic controller (FLC) are kept as design variables along with the input–output scaling factors (SF) and are optimized with Genetic Algorithm (GA) while minimizing several integral error indices along with the control signal as the objective function. Simulations studies are carried out to control a delayed nonlinear process and an open loop unstable process with time delay. The closed loop performances and controller efforts in each case are compared with conventional PID, fuzzy PID and PI^λD^μ controller subjected to different integral performance indices. Simulation results show that the proposed fractional order fuzzy PID controller outperforms the others in most cases.     

一种基于PFLC的可演化模糊逻辑控制器设计与实现

常规的模糊控制器主要通过计算机软件或单片机实现，但模糊控制器是一个高度并行的系统，实时性、自适应性要求较高，这种实现方式不能满足现代模糊控制器的设计要求。要解决这个问题必须从算法和器件结构入手。本文提出以可编程模糊逻辑控制器芯片（PFLC）作为可演化的部件，利用遗传算法优化生成模糊规则的演化硬件结构。模糊规则的自适应性是通过引入可调整因子，根据环境的变化自寻优获得。以典型二阶系统模糊控制为例进行仿真实验，其结果表明了这个可演化的模糊逻辑控制器结构的可行性。     

基于遗传算法和模糊逻辑的智能飞控技术研究

设计了基于遗传算法和模糊逻辑控制的智能飞行控制系统及采用论域自调整的模糊控制器，控制器以角度跟踪误差及其微分信号为输入来控制相应的气动舵面偏转，实现对该姿态的跟踪控制。文中给出了控制器输入输出的隶属函数，设计了相应的规则库。并在此基础上进一步利用遗传算法对模糊控制器进行优化设计，给出了遗传算法各个参数的选择原则。仿真结果表明，基于遗传算法和模糊逻辑的智能飞控系统具有良好的控制效果。     

Fuzzy PI control design for an industrial weigh belt feeder

An industrial weigh belt feeder is used to transport solid materials into a manufacturing process at a constant feedrate. It exhibits nonlinear behavior because of motor friction, saturation, and quantization noise in the sensors, which makes standard autotuning methods difficult to implement. The paper proposes and experimentally demonstrates two types of fuzzy logic controllers for an industrial weigh belt feeder. The first type is a PI-like fuzzy logic controller (FLC). A gain scheduled PI-like FLC and a self-tuning PI-like FLC are presented. For the gain scheduled PI-like FLC the output scaling factor of the controller is gain scheduled with the change of setpoint. For the self-tuning PI-like FLC, the output scaling factor of the controller is modified online by an updating factor whose value is determined by a rule base with the error and change of error of the controlled variable as the inputs. A fuzzy PI controller is also presented, where the proportional and integral gains are tuned online based on fuzzy inference rules. Experimental results show the effectiveness of the proposed fuzzy logic controllers. A performance comparison of the three controllers is also given.     

混沌系统的一种自学习模糊控制

提出一种基于遗传算法的自学习模糊控制方法控制混沌系统。用一种改进的遗传算法学习模糊控制器的隶属度函数，以改善模糊控制器的性能，使其达到良好的控制效果。用此方法控制Ｈｅｎｏｎ系统的混沌行为，效果良好。