共查询到19条相似文献,搜索用时 578 毫秒
1.
2.
3.
工业过程中普遍存在大时滞对象,为了解决大时滞系统控制中超调量大和调节时间长等问题,将史密斯(Smith)预估控制原理和模糊PI控制器参数的自适应调整方法结合起来,在Smith预估控制系统中,用模糊PI控制器代替传统的PID控制器,根据模糊控制原理对PI的2个参数进行在线整定,提出一种大时滞系统的模糊自适应PI-Smith控制方法.通过对电加热炉的仿真研究,结果表明该方法具有调节时间短、无超调量、控制精度高、无稳态偏差的优点,同时对系统模型变化具有良好的适应性. 相似文献
4.
针对电加热炉温度控制系统,研究了PID-模糊Smith复合控制方法。该控制方法利用Smith预估算法克服纯滞后,利用模糊控制来提高系统的鲁棒性,利用PID控制来提高稳态精度。在模型匹配和失配情况下进行了仿真研究,结果表明复合式控制器具有良好的稳定性和鲁棒性,对于大时间滞后的电加热炉温控系统是一种实用而简便的控制方法。 相似文献
5.
在无刷直流电机(BLDCM)的控制上,传统PID等控制方法存在或多或少的不足.在模糊PID控制的基础上提出了一种模糊神经网络PI控制器的设计方法.该方法结合了模糊逻辑与神经网络,使得模糊控制器模拟了人的控制功能,不仅对环境变化有较强的适应能力,还拥有自学习能力.相比模糊PID控制,其具有计算量小、稳定性强等特点.对BLDCM进行建模与分析;在BLDCM数学模型的基础上,分别设计模糊PID控制器和模糊神经网络PI控制器;对设计的控制器进行仿真验证并分析.实验结果表明,模糊神经网络PI控制具有跟踪性能好、超调小、响应快、脉动小等优点,其动静态特性均优于模糊PID控制. 相似文献
6.
基于神经网络的模糊自适应PID控制及其实现 总被引:4,自引:0,他引:4
提出一种基于BP神经网络的模糊自适应PID控制器,将模糊控制具有的较强逻辑推理功能、神经网络的自适应、自学习能力以及传统PID控制的优点融于一体,形成了对非精确、非线性对象的良好控制策略。针对模糊神经网络控制器运算量大、收敛慢的特点,硬件上采用数字信号处理器(DS)作为控制,运算单元,以提高系统实用性。对交流伺服系统的实验仿真结果表明,该控制器对模型、环境具有较好的适应能力与较强的鲁棒性。 相似文献
7.
针对工业中常见的时滞现象,提出把内模控制方法和神经控制原理有效结合起来,利用一种改进RBF神经网络对被控对象的模型和控制器进行自适应辨识,通过对实验室电加热炉这种典型一阶滞后对象实验,仿真表明,所提出的方法具有良好的控制特性,在系统受到干扰或对象参数发生变化的情况下,仍然具有良好的自适应性和鲁棒稳定性。 相似文献
8.
在退火炉控制系统中,常规的PID控制器及传统的模糊控制器难以达到理想的温度控制效果,针对常规PID控制器或者模糊控制器在控制具有非线性、纯滞后、时变特点的退火炉温度时存在的缺点,文中提出一种结合模糊控制器和PI控制器优点的模糊-Pl退火炉温度控制策略。文中对该模糊-PI控制器进行详细的设计,并用Matlab/Simulink软件对算法进行建模仿真,结果表明该控制器动态响应快、超调小、稳态精度高、鲁棒性好。模糊-PI控制器具有良好的控制性能,非常适合参数时变的大滞后退火炉控制系统。 相似文献
9.
10.
11.
基于模糊神经网络的变换器自适应控制方法 总被引:1,自引:1,他引:0
提出了一种新型的基于模糊神经网络自适应PI调节电流控制电压型PWM变换器方法.结合了模糊神经网络控制与PI控制器,根据三相电流比较产生的三相电流误差和电流误差变化率,自动调整P、I参数,提高了电流的控制精度和变换器的动态性能.采用MATLAB/Simulink对常规PI控制器和模糊神经网络自适应PI控制器进行了仿真对比.仿真结果表明了采用模糊神经网络自适应PI控制器,其系统输出的误差及误差变化要小,系统的跟踪精度得以提高,动态性能得到改善. 相似文献
12.
Multiple-page mapping artificial neural network algorithm used for constant tension control 总被引:1,自引:0,他引:1
Constant tension control is widely required in industrial applications such as paper machines, coating machines, rewinding and unwinding machines. In a metal film coating machine, which is a multi-input multi-output system, speed and tension have cross coupling and thus desired speed and tension responses are difficult to achieve by applying conventional analogue proportional-plus-integral (PI) control. This paper introduces a multiple-page mapping artificial neural network with back-propagation training algorithm. This method can successfully decouple the speed and tension control loops and both loops can operate quasi-independently. It overcomes the disadvantages of traditional PI control systems. To handle the variation of the rewinding roll diameter, multiple pages of neural networks are applied. Some simulation results show the effectiveness of this control algorithm. 相似文献
13.
Ridong Zhang Anke Xue Jianzhong Wang Shuqing Wang Zhengyun Ren 《Journal of Process Control》2009,19(1):68-74
The paper presents a new nonlinear predictive control design for a kind of nonlinear mechatronic drive systems, which leads to the improvement of regulatory capacity for both reference input tracking and load disturbance rejection. The nonlinear system is first treated into an equal linear time-variant system plus a nonlinear part using a neural network, then an iterative learning linear predictive controller is developed with a similar structure of PI optimal regulator and with setpoint feed forward control. Because the overall control law is a linear one, this design gives a direct and also effective multi-step prediction method and avoids the complicated nonlinear optimization. The control law is also an accurate one compared with traditional linearized method. Besides, changes of the system state variables are considered in the objective function with control performance superior to conventional state space predictive control designs which only consider the predicted output errors. The proposed method is compared with conventional state space predictive control method and classical PI optimal control method. Tracking performance, robustness and disturbance rejection are enlightened. 相似文献
14.
15.
针对永磁同步电机矢量控制系统中,传统PID控制器对永磁同步电机控制性能效率不高以及对工作环境变化的适应性能不良等问题,首先,在两相旋转坐标系下采取双轴理论建立永磁同步电机数学模型;接着,采用模糊控制理论对永磁同步电机矢量控制系统中转速环传统的普通PID控制器进行改进研究,深入地从理论层次研究分析建立得到模糊PI控制器。在Matlab/Simulink环境下建立基于模糊PI控制器的永磁同步电机矢量控制系统仿真模型,并且将建立得到的模糊PI控制器引入永磁同步电机矢量控制系统进行仿真实验。实验结果表明,模糊PI控制器相对于传统的PID控制器具有更加优越的动态性能和稳态性能。 相似文献
16.
Mojtaba Alizadeh Soheil Ganjefar Morteza Alizadeh 《Engineering Applications of Artificial Intelligence》2013,26(9):2227-2242
Although the PI or PID (PI/PID) controllers have many advantages, their control performance may be degraded when the controlled object is highly nonlinear and uncertain; the main problem is related to static nature of fixed-gain PI/PID controllers. This work aims to propose a wavelet neural adaptive proportional plus conventional integral-derivative (WNAP+ID) controller to solve the PI/PID controller problems. To create an adaptive nature for PI/PID controller and for online processing of the error signal, this work subtly employs a one to one offline trained self-recurrent wavelet neural network as a processing unit (SRWNN-PU) in series connection with the fixed-proportional gain of conventional PI/PID controller. Offline training of the SRWNN-PU can be performed with any virtual training samples, independent of plant data, and it is thus possible to use a generalized SRWNN-PU for any systems. Employing a SRWNN-identifier (SRWNNI), the SRWNN-PU parameters are then updated online to process the error signal and minimize a control cost function in real-time operation. Although the proposed WNAP+ID is not limited to power system applications, it is used as supplementary damping controller of static synchronous series compensator (SSSC) of two SSSC-aided power systems to enhance the transient stability. The nonlinear time-domain simulation and system performance characteristics in terms of ITAE revealed that the WNAP+ID has more control proficiency in comparison to PID controller. As additional simulations, the features of the proposed controller are compared to those of the literature while some of its promising features like its fast noise-rejection ability and its high online adapting ability are also highlighted. 相似文献
17.
18.
A novel approach to stabilization and trajectory tracking for nonlinear systems with unknown parameters and uncertain disturbances is developed. We take a drastic departure from the classical adaptive control approach consisting of a parameterized feedback law and an identifier, which tries to minimize a tracking (or prediction) error. Instead, we propose a simple nonlinear PI structure that generates a stable error equation with a perturbation function that exhibits at least one root. Trajectories are forced to converge to this root by suitably adjusting the nonlinear PI gains. We consider the two basic problems of: (i) matched uncertainties, when the uncertain terms are in the image of the input matrix, and (ii) unknown control directions, when the control signal is multiplied by a gain of unknown sign. We show that, without knowing the system parameters, and with only basic information on the uncertainties we can achieve global asymptotic stability and global tracking, without injecting high gains into the loop. Interestingly, we prove that we can take as our nonlinear PI structure an activation function reminiscent of that used in neural networks. Although most of the results are derived assuming full state measurement, we also present an observer-based solution for a chain of integrators with unknown control direction. The procedure is shown to provide simple solutions to the classical problems of neural network function approximation, as well as eccentricity control and friction compensation of mechanical systems. 相似文献
19.
针对传统PI控制无法使微型涡喷发动机的扰动抑制性能与设定值跟踪性能同时最佳的问题,开展了微型涡喷发动机二自由度(Two-Degree-of-Freedom,2-DOF)PI控制研究。首先基于Speedgoat实时目标机搭建了快速原型试验系统。根据发动机开环试验数据辨识得到不同稳态点下的传递函数模型,在此基础上设计了2-DOF PI控制器,并进行仿真验证。最后将控制算法部署至Speedgoat中开展实物试验。结果表明,设计的2-DOF PI控制器能够使微型涡喷发动机的扰动抑制性能与设定值跟踪性能同时最佳,并在发动机较大的工作范围内有良好的控制性能。 相似文献