基于变论域模糊PID的分解炉温度控制研究

分解炉温度控制系统具有非线性、时变、纯滞后的特点,针对传统PID控制及模糊.PID控制难以很好地满足控制要求,提出了一种变论域模糊自适应PID控制方法。利用变论域思想,设计了一种基于函数模型的伸缩因子控制器,动态地调整模糊控制器的量化因子和比例因子,提高了控制精度。在Matlab环境下分别对PID控制、模糊PID控制和变论域模糊PID控制方法进行了仿真对比,结果表明变论域模糊PID控制方法具有更好的动静态性能和自适应能力。     

自适应人工神经网络电机控制器设计

提出了一种自适应人工神经网络无刷直流电动机(BLDCM)转速控制器设计方法;针对传统PID调节器难以应对系统超调和短时振荡等问题,提出了一种结合人工神经网络和传统PID控制的新方法;首先建立了(BLDCM)的本体数学模型,在此基础上描述了将人工神经网络和PID控制相结合的模型,并对具体的控制算法进行了定义;最后,使用Matlab仿真工具对BLDCM控制实例进行了仿真;实验结果表明,结合人工神经网络和PID控制器的新控制方法具有响应快、鲁棒性强以及控制精度高等优点,很好地抑制了超调和振荡。     

直流电机控制系统优化问题仿真分析

在直流电机控制优化的研究中,直流电机具有强非线性的特点,使用定参数PID方法的转速控制虽然可以提高直流电机的响应速度,提高其动态性能,但同时会产生超调现象,对转速控制系统的性能难以保证.应用专家系统和专家控制系统的基本知识设计了一个模糊自整定PID控制系统.该系统利用输入误差及变化率建立一组PID实时调整规则.结合所建立的直流电机模型,进行仿真.结果表明,所设计的模糊自整定PID控制器不但进一步提高直流电机的响应速度,同时很好解决了常规PID控制产生的超调问题.     

变风量空调系统末端的变论域模糊PID控制

针对目前传统PID控制对模型依赖性强,参数难以在线调整,对不确定性强的变风量(VAV)空调系统的控制快速性和准确性差的特点,提出一种变论域模糊PID控制,以提高系统的控制速度和精度,使系统具有良好的动、静态性能。在推导的变风量空调房间和末端装置数学模型的基础上进行了仿真研究。结果表明,控制系统具有很好的鲁棒性和自适应能力,可以明显提高系统的响应速率,控制器的动态结构更适用于变风量空调系统。     

基于倒立摆的一种控制器的设计与仿真

针对倒立摆多输入多输出系统，由于复杂非线性，强耦合性而难以建立数学模型和难以控制的问题．提出一种PID参数模糊自整定的控制方法。利用MATLAB设计PID参数模糊自整定控制器，并进行仿真和实际应用。通过与常规PID控制方法相比较，可以看出PID参数模糊自整定的控制方法不需要建立精确的数学模型，还可以及时在线调整PID参数，获得更好的控制效果。     

冻干试验机温度自适应模糊PID算法研究

将模糊控制理论应用于冻干试验机的温度控制系统中,有助于提高温度响应曲线的跟随性及加快反应时间,从而可以提高温度的控制精度。通过PID控制与参数自适应模糊PID控制的对比的MATLAB仿真实验,获得了调节时间有明显改善的参数自适应模糊PID控制器算法;对真空冷冻试验机系统的近似模型进行了定量分析,并结合实践经验,给出了模型的数学表达式。应用新研制的控制器算法,对具有非线性、大滞后特征的冻干试验机温度系统具有很好的控制作用,弥补了传统的PID控制的不足。     

无刷直流电机新型控制方案的仿真研究

为设计高性能低成本的轮式移动机器人,对永磁无刷直流电动机的控制器进行改进,利用带电流补偿的电压负反馈,实现无速度传感器控制,解决了难以安装和维护测速装置的弊病,降低成本;采用模糊自适应PID算法在线调整PID参数,提高系了系统的可靠性和鲁棒性。仿真结果证明该方案可以实现很好的调速性能。     

基于参数自整定模糊PID的SCR烟气脱硝控制

选择性催化还原(SCR)烟气脱硝系统对喷氨量的控制多为PID控制,在变工况下系统呈现出强非线性和滞后性的特点,难以保证喷氨量的精确控制.因此根据参数自整定模糊PID(Fuzzy-PID)控制原理,对PID参数进行实时校正,使其对参数的变化具有很强的适应能力,并且能达到系统所需的动态性能指标.仿真结果证明,参数自整定模糊PID控制相对于传统PID控制具有响应速度快、抗干扰能力强、振荡次数少等优点,且具有良好的鲁棒性和稳定性.     

电动加载系统的模糊自适应PID控制研究

电动加载系统的突出优点是加载跟踪速度快,但加载过程中系统多余力能否快速消除是制约加载精度、影响加载系统动态品质的最重要因素.常规PID控制难以满足电动加载系统对高精度和快速性的要求,而模糊自适应PID控制能够解决这个难题,实验证实了系统采用模糊自适应PID算法后明显提高了加载精度和响应速度.     

模糊PID控制的柴油机调速系统仿真

PID控制是生产过程中应用最广泛的控制方式,但它对含变化参数的模型控制效果不理想。为了使舰船柴油机适应不断变化的工作环境,在传统PID控制的柴油机调速系统的基础上加入模糊控制环节,对PID参数进行在线整定。详细介绍了模糊PID算法在柴油机调速系统中的应用以及模糊控制器的设计,并通过MATLAB对模糊PID柴油机调速系统进行仿真,仿真结果表明,模糊自整定PID控制系统可以改善系统的动态特性,减小系统的振荡,提高系统的响应速度。     

1种多面体描述的非线性预测控制器综合

针对工业过程中的非线性计算量大,实时性低等问题,提出了1种计算非线性预测控制的新方法。该方法将神经网络与线性微分包含(LDI)相结合对非线性系统建模,从而将非线性系统转换成多面体描述的线性时变系统。对于多面体描述系统的各个顶点构成的多个线性模型,在线求得不同状态下的控制器。最后通过证明多面体描述的线性系统的稳定性来保证原非线性的稳定性。通过仿真看出此算法在处理复杂系统的控制问题具有良好的控制效果。     

Improved training of neural networks for the nonlinear activecontrol of sound and vibration

Active control of sound and vibration has been the subject of a lot of research, and examples of applications are now numerous. However, few practical implementations of nonlinear active controllers have been realized. Nonlinear active controllers may be required in cases where the actuators used in active control systems exhibit nonlinear characteristics, or in cases when the structure to be controlled exhibits a nonlinear behavior. A multilayer perceptron neural-network based control structure was previously introduced as a nonlinear active controller, with a training algorithm based on an extended backpropagation scheme. This paper introduces new heuristical training algorithms for the same neural-network control structure. The objective is to develop new algorithms with faster convergence speed and/or lower computational loads. Experimental results of active sound control using a nonlinear actuator with linear and nonlinear controllers are presented. The results show that some of the new algorithms can greatly improve the learning rate of the neural-network control structure, and that for the considered experimental setup a neural-network controller can outperform linear controllers.     

高斯基神经网络的非线性PID控制方法

针对二阶非线性系统,提出了一种用高斯基函数作为神经元激励函数的PID（Proportion-Integral-Derivative）控制方法。该方法用高斯基函数模拟PID参数随误差变化的曲线,用神经网络算法在线调整各模拟曲线的系数,从而构造出具有非线性特征的PID控制策略,实现了基于高斯基神经网络的非线性PID智能控制方法。计算机仿真结果表明,该方法具有良好的非线性控制效果,因此在工业领域具有广泛的应用前景。     

随机激励下非线性水轮机系统的控制设计

针对一类随机非线性哈密顿系统提出了一种全新的反馈跟踪控制方法.该控制策略可以准确地控制系统输出的概率密度分布特性.闭环系统的稳定性也通过李雅普诺夫函数法得到严格的数学证明.最后,以随机非线性水轮机系统为例,详细演示了控制设计过程及其有效性.仿真结果表明,新的反馈控制策略可以使水轮机系统的输出满足预先指定的平稳概率密度函数.     

Robust adaptive neural control for a class of perturbed strict feedback nonlinear systems

This paper presents a robust adaptive neural control design for a class of perturbed strict feedback nonlinear system with both completely unknown virtual control coefficients and unknown nonlinearities. The unknown nonlinearities comprise two types of nonlinear functions: one naturally satisfies the "triangularity condition" and can be approximated by linearly parameterized neural networks, while the other is assumed to be partially known and consists of parametric uncertainties and known "bounding functions." With the utilization of iterative Lyapunov design and neural networks, the proposed design procedure expands the class of nonlinear systems for which robust adaptive control approaches have been studied. The design method does not require a priori knowledge of the signs of the unknown virtual control coefficients. Leakage terms are incorporated into the adaptive laws to prevent parameter drifts due to the inherent neural-network approximation errors. It is proved that the proposed robust adaptive scheme can guarantee the uniform ultimate boundedness of the closed-loop system signals.. The control performance can be guaranteed by an appropriate choice of the design parameters. Simulation studies are included to illustrate the effectiveness of the proposed approach.     

Robustness design of fuzzy control for nonlinear multiple time-delay large-scale systems via neural-network-based approach

The stabilization problem is considered in this correspondence for a nonlinear multiple time-delay large-scale system. First, the neural-network (NN) model is employed to approximate each subsystem. Then, a linear differential inclusion (LDI) state-space representation is established for the dynamics of each NN model. According to the LDI state-space representation, a robustness design of fuzzy control is proposed to overcome the effect of modeling errors between subsystems and NN models. Next, in terms of Lyapunov's direct method, a delay-dependent stability criterion is derived to guarantee the asymptotic stability of nonlinear multiple time-delay large-scale systems. Finally, based on this criterion and the decentralized control scheme, a set of fuzzy controllers is synthesized to stabilize the nonlinear multiple time-delay large-scale system.     

Hybrid intelligent control scheme of a polymerization kettle for ACR production

Polymerization kettle is the key controlled plant in ACR (Acrylate Copolymer Resin) production, which is a nonlinear time-delay system with parametric variance. However, modeling difficulties make the plant dynamic model poorly defined. A hybrid intelligent control scheme including an intelligent predictor is designed for this complex plant based on time-delay compensation theory. It consists of a Smith neural-network predictor and a self-adjusting-scaling-factor fuzzy logic controller. The simulation experiments verified the performance of our proposed system in two scenarios: one with invariant parameters and the other with time-varying parameters. Moreover, the comparison to other three typical control methods including Smith PID, Smith neural-network PID and Smith fuzzy logic control is also presented, which demonstrates that the proposed control scheme has satisfactory effect. Even when the system parameters vary with time, the proposed system still gives superior performance and improved robustness.     

开关变换器周期窗内吸引子共存现象仿真研究

开关DC-DC变换器是一种强非线性系统,存在各种非线性现象,切分叉是其中的一种特殊分叉.开关变换器因切分叉而引发了阵发混沌,阵发混沌使得系统的非线性动力学特性变得更加复杂.对电流控制型Boost变换器中产生的复杂动力学现象进行了仿真研究,揭示了参数变化分叉图中存在着周期窗、周期窗内共存吸引子和不完全倍周期费根鲍姆树等现象,通过构造相应的切分叉离散迭代映射曲线,说明了这些现象都足由于系统发生切分叉后形成的.研究结果对开关变换器的稳定设计具有重要的指导意义.     

A direct adaptive neural-network control for unknown nonlinearsystems and its application

In this paper a direct adaptive neural-network control strategy for unknown nonlinear systems is presented. The system considered is described by an unknown NARMA model, and a feedforward neural network is used to learn the system. Taking the neural network as a neural model of the system, control signals are directly obtained by minimizing either the instant difference or the cumulative differences between a set point and the output of the neural model. Since the training algorithm guarantees that the output of the neural model approaches that of the actual system, it is shown that the control signals obtained can also make the real system output close to the set point. An application to a flow-rate control system is included to demonstrate the applicability of the proposed method and desired results are obtained.     

Suppression of Chaotic Behaviors in a Complex Biological System by Disturbance Observer-based Derivative-Integral Terminal Sliding Mode

Coronary artery systems are a kind of complex biological systems. Their chaotic phenomena can lead to serious health problems and illness development. From the perspective of engineering, this paper investigates the chaos suppression problem. At first, nonlinear dynamics of coronary artery systems are presented. To suppress the chaotic phenomena, the method of derivative-integral terminal sliding mode control is adopted. Since coronary artery systems suffer from uncertainties, the technique of disturbance observer is taken into consideration. The stability of such a control system that integrates the derivative-integral terminal sliding mode controller and the disturbance observer is proven in the sense of Lyapunov. To verify the feasibility and effectiveness of the proposed strategy, simulation results are illustrated in comparison with a benchmark.