水力压裂混合液砂密度模糊控制方法研究

水力压裂过程中,混砂车的混砂密度值直接影响水力压裂施工的质量,本文采用模糊控制方法控制混砂液密度,运用Matlab进行了模糊控制和常规PID控制仿真.仿真结果表明:模糊控制方法的控制响应速度快,阻尼小,稳态误差小,控制效果好.     

基于PLC的模糊PID控制器在发动机试验台测控系统中的应用

将模糊控制和PID控制结合起来运用于发动机试验台测控系统中,构造了一个参数自整定模糊PID控制器,通过模糊控制规则在线调整PID控制器的参数,用MATLAB实现该控制器的仿真。仿真结果表明该控制器的动态响应曲线好、超调量小、稳态精度高。并给出基于PLC直接查表方式实现该模糊PID控制器,为模糊控制技术在工业控制中应用提供了更加广阔的空间。     

胎面生产线辅线速度链模糊自适应PID控制

针对常规PID控制在胎面挤出联动生产线辅线速度链整定中,参数整定不尽人意的问题,设计出一种基于模糊控制理论的自适应PID控制器。通过建立模糊控制规则和进行模糊推理来确定PID的参数,实现对辅线速度的调节。利用Matlab的模糊逻辑控制工具箱对算法进行仿真,仿真结果表明,该控制器与常规PID控制器相比,具有调节时间短、超调量小、跟踪调节性能好、鲁棒性等优点。实践证明,这种模糊自适应PID控制器比常规的PID控制器具有更好的控制特性。     

模糊PID控制在污水处理SBR工艺中的应用

污水处理SBR工艺生物反应池中溶解氧的控制存在非线性、时变、大滞后等问题,而传统PID控制参数整定不易,为此构造了一个模糊自适应PID控制器,将模糊控制和PID控制结合起来,通过模糊控制规则在线调整PlD控制器的三个参数。经仿真验证,模糊自适应PID控制器比传统PID控制器的动态响应曲线好,响应时间短,超调量小,动静态性能好。     

基于变论域模糊PID算法的张力控制

张力控制在工业中应用十分广泛,其核心是保持张力的恒定。目前恒张力控制主要采用的算法有分段PID和模糊PID,但是这两种方法控制稳定性差。在常规模糊控制的基础上,引入了变论域的思想并设计了伸缩因子基于函数类型的变论域模糊控制器,以此对执行机构为磁粉制动器的张力系统进行控制。通过建立系统模型对常规模糊控制PID算法和变论域模糊控制PID算法进行了仿真对比,仿真结果表明采用变论域模糊PID算法进行系统控制时具有超调小,响应速度快的优点。     

基于Matlab的模糊自整定PID控制器仿真研究

将模糊控制与PlD控制结合,利用模糊推理方法实现对PID参数的在线自整定.利用MATLAB的模糊控制工具箱以及Simulink对其进行了仿真,结果表明设计的自整定PID模糊控制器具有控制精度高,超调小,动态性能好的特性.     

一种智能PID复合控制器的设计

提出一种新型PID复合控制算法，将模糊控制和混沌PID控制结合起来，既具有模糊控制快速、鲁棒性强的优点，又具有PID稳态精度高的优点。仿真表明该算法使用可靠、精度高，而且具有较强的抗干扰能力和较好的鲁棒性，优于常规的PID控制与模糊控制。     

一种新型电阻炉温度控制系统的设计

采用参数自整定FUZZY—PID控制算法控制电阻炉，将模糊控制和PID控制结合起来．既具有模糊控制快速、鲁棒性强的优点，又具有PID稳态精度高的优点。仿真结果表明，该算法使用可靠、精度高，而且具有较强的抗干扰能力和较好的鲁棒性，优于常规的PID控制与模糊控制。     

PID与模糊控制算法的比较及其改进

分析比较了PID控制和模糊控制算法，针对一般PID控制容易产生超调，模糊控制的稳态精度不高的缺陷，提出了相应的改进算法－复合PID算法和带动态补偿的模糊控制算法，并通过水温控制仿真实验，比较了这几种控制算法的控制效果，实验结果证明，这两种改进算法不仅系统超调小，而且具有较高的稳态控制精度，可广泛应用于精确控制领域。     

模糊PID控制在光伏发电MPPT中的应用

光伏电池的输出功率随外部环境和负载的变化而变化,为有效利用太阳能,需要对其进行最大功率点跟踪(MPPT).模糊控制属于有差控制,在最大功率点附近仍有振荡存在,为进一步提高光伏发电MPPT控制品质,提出一种基于占空比扰动的模糊PID双模控制策略.其基本方法是直接将占空比作为控制变量,在模糊控制基础上加入了传统的PID控制,在大偏差范围内采用模糊控制进行快速响应调整,在小偏差范围内的精度调整采用常规PID控制,实现了MPPT精确性和快速性的兼备.仿真结果表明,改进方法能够快速、准确地跟踪光伏电池的最大功率点,避免在最大功率点的振荡,提高了能量转换效率.     

Adaptive fuzzy backstepping output feedback control of nonlinear uncertain systems with unknown virtual control coefficients using MT-filters

In this paper, an adaptive fuzzy output feedback control approach is developed for a class of SISO nonlinear uncertain systems with unmeasured states and unknown virtual control coefficients. The fuzzy logic systems are used to model the uncertain nonlinear systems. The MT-filters and the state observer are designed to estimate the unmeasured states. Using backstepping design principle and combining the Nussbaum gain functions, an adaptive fuzzy output feedback control scheme is developed. It is proved that the proposed adaptive fuzzy control approach can guarantee all the signals in the closed-loop system are semi-globally uniformly ultimately bounded and the tracking error converges to a small neighborhood of origin. A simulation is included to illustrate the effectiveness of the proposed approach.     

Adaptive fuzzy decentralized output feedback control for stochastic nonlinear large‐scale systems using DSC technique

In this paper, an adaptive fuzzy decentralized backstepping output feedback control approach is proposed for a class of uncertain large‐scale stochastic nonlinear systems without the measurements of the states. The fuzzy logic systems are used to approximate the unknown nonlinear functions, and a fuzzy state observer is designed for estimating the unmeasured states. Using the designed fuzzy state observer, and by combining the adaptive backstepping technique with dynamic surface control technique, an adaptive fuzzy decentralized output feedback control approach is developed. It is shown that the proposed control approach can guarantee that all the signals of the resulting closed‐loop system are semi‐globally uniformly ultimately bounded in probability, and the observer errors and the output of the system converge to a small neighborhood of the origin by choosing appropriate design parameters. A simulation example is provided to show the effectiveness of the proposed approaches. Copyright © 2011 John Wiley & Sons, Ltd.     

立体仓库堆垛机的模糊控制定位技术研究

分析了自动化立体仓库堆垛机控制系统位置控制策略和常规定位过程,给出了堆垛机定位控制的数学模型,通过采用具有调整因子的规则自生成的方法进行模糊推理并实现位置模糊控制。仿真结果证明,系统对输入信号的响应快,跟踪误差小,过度时间短,且无超调或超调量小,震荡次数少,稳态精度高,系统输出与给定值稳态偏差小,定位精度高。     

Comparison and application of three integral-improved methods on conventional fuzzy control strategy

This paper presents three integral methods of a fuzzy knowledge-based control system, to diminish the steady-state output errors. The three controllers, viz the three-input fuzzy controller, fuzzy plus integral controller and are fuzzy-PI controller, have been described and are applied to the two systems. The first is a zero-type-number first-order system whose input is step, and the other is a linearized one-type-number fifth-order electrohydraulic servo system whose input is ramp. Responses of both systems have steady-state errors without integral control. Simulation results have shown that all these three integral methods can effectively eliminate steady-state errors. The three-input fuzzy controller that produces the least overshoot and shortest rise time is the first choice for producing rapid dynamic response and minimum output errors. Fuzzy-PI controller is the second best for producing small output errors.     

Event-triggered adaptive fuzzy control for nonstrict-feedback nonlinear systems with output hysteresis

This paper concentrates upon the issue of adaptive fuzzy tracing control for a class of nonstrict-feedback nonlinear systems output with hysteresis via an event-triggered strategy. To handle the difficulty caused by the nonstrict nonlinear systems, the variable separation technique is introduced. The design difficulty of output hysteresis is addressed by employing a hysteresis inverse function and Nussbaum function to compensate unmeasurable state signal. Meanwhile, the fuzzy logic system (FLS) is used to estimate the unknown function at each step of recursion. Moreover, by devising the relative threshold event-triggered mechanism (ETM), the frequency of actuators and controllers can be largely decreased. Thus, the adaptive fuzzy event-triggered tracing control strategy is proposed by combining the barrier Lyapunov function and backstepping technique. With the proposed scheme, it is theoretically demonstrated that all signals in the closed-loop system are bounded, and the tracing errors are driven to a small neighborhood of the origin under the output constraint. Eventually, two examples demonstrate the efficacy of the proposed control strategy.     

Robust adaptive fuzzy control scheme for nonlinear system with uncertainty

In this paper, a robust adaptive fuzzy control scheme for a class of nonlinear system with uncertainty is proposed. First, using prior knowledge about the plant we obtain a fuzzy model, which is called the generalized fuzzy hyperbolic model (GFHM). Secondly, for the case that the states of the system are not available an observer is designed and a robust adaptive fuzzy output feedback control scheme is developed. The overall control system guarantees that the tracking error converges to a small neighborhood of origin and that all signals involved are uniformly bounded. The main advantages of the proposed control scheme are that the human knowledge about the plant under control can be used to design the controller and only one parameter in the adaptive mechanism needs to be on-line adjusted.     

Robust adaptive fuzzy control scheme for nonlinear system with uncertainty

In this paper, a robust adaptive fuzzy control scheme for a class of nonlinear system with uncertainty is proposed. First, using prior knowledge about the plant we obtain a fuzzy model, which is called the generalized fuzzy hyperbolic model （GFHM）. Secondly, for the case that the states of the system are not available an observer is designed and a robust adaptive fuzzy output feedback control scheme is developed. The overall control system guarantees that the tracking error converges to a small neighborhood of origin and that all signals involved are uniformly bounded. The main advantages of the proposed control scheme are that the human knowledge about the plant under control can be used to design the controller and only one parameter in the adaptive mechanism needs to be on-line adjusted.     

利用模糊系统的直升机的自适应控制

针对直升机动力学为非线性,且存在不确定因素和状态变化,设计利用模糊系统的自适应控制器.设计的控制器是系统的输出跟踪参考模型输出的直接调整模糊控制器参数的自适应控制器.又利用Lyapunov函数保证了闭环控制系统的稳定性并推导最优的自适应规律.实验结果表明,有外部扰动的情况下所设计的自适应控制器比模糊控制器对直升机控制具有良好的动态响应和稳定性,是一种非常有效的控制方法.     

基于小型PLC的温度模糊控制系统

由开关量输入输出模块、A/D和D/A等基本单元组成的小型PLC具有体积小、成本低和可靠性高的优势.如果能通过软件编程使小型PLC具有模糊控制功能,并应用于不易建模或模型参数不确定的过程控制系统,将使系统具有较高的可靠性和性能价格比.本文提出了一种在小型PLC上构造模糊控制器的方法,并以温度控制为背景,介绍了模糊控制器的设计以及系统实现的原理.     

Adaptive fuzzy control for pure-feedback stochastic nonlinear systems with unknown dead-zone input

This paper is concerned with the problem of adaptive fuzzy output tracking control for a class of nonlinear pure-feedback stochastic systems with unknown dead-zone. Fuzzy logic systems in Mamdani type are used to approximate the unknown nonlinearities, then a novel adaptive fuzzy tracking controller is designed by using backstepping technique. The control scheme is systematically derived without requiring any information on the boundedness of dead-zone parameters (slopes and break-points) and the repeated differentiation of the virtual control signals. The proposed adaptive fuzzy controller guarantees that all the signals in the closed-loop system are bounded in probability and the system output eventually converges to a small neighbourhood of the desired reference signal in the sense of mean quartic value. Simulation results further illustrate the effectiveness of the proposed control scheme.