基于DRNN的多变量解耦控制系统

本文针对一类有强耦合带时延的多变量系统,采用对角递归神经网络(DRNN)与带动量项的PID梯度优化算法(PIDGDM),自适应调整PID控制器的二项参数,并行完成系统的解耦与控制工作。仿真结果表明,系统具有很好的静态和动态性能。     

可控受限多变量耦合系统的智能控制

针对可控受限多变量耦合系统,提出了一种基于对角递归神经网络(DRNN)整定的PID混合解耦控制。采用对角递归神经网络来辨识系统模型,进而对PID控制器参数进行整定,实现多变量解耦控制。通过对多变量耦合控制系统的设计和实时控制,实际控制结果达到了解耦控制的要求,并具有无超调、响应速度快、控制精度高等特点。     

预测控制 第六讲 多变量系统的解耦预测控制

预测控制──第六讲多变量系统的解耦预测控制成都电子科技大学（成都）周云钟上一节以多变量系统的动态矩阵控制讨论了多变量系统的预测控制问题。本讲介绍多变量系统的一种新的预测控制方法，即频域解耦、时域预测与优化的算法。１多变量系统的频域解耦设计设一个ｍ输入...     

改进DRNN用于纸张水分定量解耦控制

针对抄纸过程中纸机系统大滞后、非线性、时变且纸张的水分与定量之间存在严重耦合的问题,提出一种基于改进DRNN神经网络辨识的PID解耦控制器。该控制器利用改进DRNN对定量与水分参数的Jacobian信息辨识结果,自适应调整PID控制器的各项比例系数。仿真结果表明水分与定量之间相互影响很小,能较好实现对象的解耦控制,且适应能力强。实际运行结果表明,该算法的投入提高了控制精度,具有较强的鲁棒性。     

多变量动态矩阵控制算法研究

研究精馏塔的优化控制问题,由于PID控制精度差,多变量解耦控制理想完全解耦难以实现,预测控制存在模型失配,参数与工程指标联系不紧密的缺点,提出基于PID的多变量动态矩阵控制(DMC)策略.通过在DMC性能指标中引入预测输出误差项,利用PID指标改变传统DMC优化过程.上述算法兼具预测控制的解耦特性与PID控制参数工程意义明确的特点,加快了Shell原油精馏塔响应速度,消除了产品干点在设定值附近振荡,减少一个产品干点改变致使另一产品干点波动的现象.同时改进算法与传统预测控制器结构类似,便于广大工程人员设计实现.     

一类串联生产过程的分布式解耦预测控制

研究一类由多个子过程串联而成的生产过程的预测控制，提出了在分布式通信模式下的预测控制算法．该算法由两部分组成：一部分利用其他子过程在上一时刻的控制策略，对子过程间的弱耦合进行前馈补偿；另一部分通过顺序求解对强耦合进行解耦控制．在线性无约束情况下，得到了分布式解耦动态矩阵控制律．最后通过仿真验证了算法的有效性．     

基于神经网络的解耦控制方法

本文介绍了BP算法的基本原理及其实现步骤,并将BP算法应用于神经网络解耦器和PID神经网络的训练中,即本文中各个神经网络的训练算法均采用BP算法,提出了一种神经网络在线解耦控制算法,即将神经网络解耦和神经网络PID控制两者结合,对系统进行解耦控制。将解耦与控制结合,既避免了单独采用自适应PID控制时控制效果不佳的问题,又避免了单独采用解耦时原有控制器不能适应变化后的对象问题。最后对一组双输入双输出耦合系统进行了仿真研究。     

混合液温度流量解耦控制算法及系统虚拟

针对一种具体的混合液温度流量非线性控制问题，文中提出了一种解析方式的解耦控制算法。采用MatLab对算法的仿真表明，这种解耦控制算法具有良好的解耦和控制效果。为全面分析该算法，设计了交互式实时混合液温度流量解耦控制系统虚拟软件，该软件的使用结果说明，这种算法可以很好地解决这种解耦控制问题。     

近空间无尾飞行器解耦与控制分配方法研究

关于飞行器稳定性优化控制问题,由于近空间无尾飞行器具有典型的通道间强耦合且多个操纵面介入单个通道控制,执行机构的复杂化和非线性导致控制系统设计实时性和响应性差,精度达不到要求。为提高姿态控制器控制精度,减小力矩分配误差,提出通过解耦控制系统和控制分配逻辑来分别解决对象的耦合和多操纵面的力矩分配问题。通过将动态逆设计方法与变结构控制理论相结合,设计了一种强鲁棒性的解耦控制律,并在加权伪逆算法的基础上,提出采用舵面偏角修正操纵面的效率矩阵,从而实现了强耦合非线性对象的高精度控制。仿真结果表明,控制器解耦效果良好且具有较强鲁棒性,控制分配算法减小了80%力矩分配误差,满足了飞行器的控制需求。     

基于BP神经网络的多变量PID解耦控制

基于神经网络的智能PID控制策略，以经典的PID控制理论为基础，并通过具有多变量解耦控制自学习功能的神经网络参数整定来实现。本文给出了网络的结构和算法，示出了一组二元变量强耦合时变系统的实时仿真结果。通过计算机仿真证明，基于神经网络的PID控制具有良好的自学习和自适应解耦控制能力。该系统融解耦器和控制器于一体，易于实现，适用于非线性多变量系统的解耦控制。它使解耦后的系统具有较好的动态和静态性能，特别是当根据BP控制规律确定了网络连接权系数的初值时，还能使系统参数快速收敛。     

Minimality of dynamic input-output decoupling for nonlinear systems

In this note we study the strong dynamic input-output decoupling problem for nonlinear systems. Using an algebraic theory for nonlinear control systems, we obtain for a dynamic input-output decouplable nonlinear system a compensator of minimal dimension that solves the decoupling problem.     

A predictive functional control algorithm for multivariable systems with time delay

To improve the dynamic characteristics and the coupling capability, a new predictive functional control algorithm is proposed for strong coupling multivariable systems with time delay, which combines predictive functional control and decoupliug control. First, a decoupling control algorithm is proposed, in which first-order models with time delay are established by analyzing the amplitude-frequency and phase-frequency characteristics of the decoupled subject. Then, a controller is designed for the single-variable subjects after decoupling based on the principles of predictive functional control. The simulation results show that this proposed algorithm has less online computation time and faster tracking. It can provide a more effective control for complex multivariable systems.     

非线性系统解耦原则及实施

本文给定输出的一个分划,寻找反馈规律,使得控制系统分解成若干个平行的、独立作用 的子系统,这就是控制系统的反馈(块)解耦问题.对于由微分代数语言描述的非线性输入输 出控制系统,证明了它具有其本身所固有的解耦结构,当且仅当给定的分划与这个解耦结构相 "匹配"时.系统可达到解耦.对于由状态空间方程所描述的非线性系统,本文用动态扩张算法 给出了其解耦结构的构造.     

Input-output decoupling of non-linear systems via a general class of dynamic compensation

An algorithm is presented for input-output decoupling of non-linear control systems with the use of a dynamic precompensator. The precompensator used is of non-linear form. Moreover, this algorithm solves the above problem in a local fashion, i.e. we find a neighbourhood of the initial state of the overall system (the original system together with the dynamic compensator) where we can achieve input-output decoupling using the appropriate feedback law.     

基于参数辨识的自适应解耦控制算法的研究

多变量模型的复杂结构、强耦合性、被控对象参数的未知、慢时变等问题要求控制器必须具有良好的自适应性,针对以上问题提出了一种基于改进的广义最小方差闭环自适应解耦控制器实现更好的自适应,其由参数可调的控制器和自适应控制律组成,此控制器通过将闭环系统方程的传递函数矩阵等于期望的对角矩阵来实现解耦,同时改进的辨识算法可进行在线辨识控制器的参数实现同步自适应解耦。通过以CARMA为多变量控制模型,采用该方法进行仿真有效的解决了多变量之间的耦合性。结果表明该方法能够适应相应的变化,跟踪性能较好,且具备良好的解耦能力,进而保证了闭环系统的稳定性,从而验证了此方法能够效提高控制系统的稳定性和鲁棒性。     

Diagonal recurrent neural networks for dynamic systems control

A new neural paradigm called diagonal recurrent neural network (DRNN) is presented. The architecture of DRNN is a modified model of the fully connected recurrent neural network with one hidden layer, and the hidden layer comprises self-recurrent neurons. Two DRNN's are utilized in a control system, one as an identifier called diagonal recurrent neuroidentifier (DRNI) and the other as a controller called diagonal recurrent neurocontroller (DRNC). A controlled plant is identified by the DRNI, which then provides the sensitivity information of the plant to the DRNC. A generalized dynamic backpropagation algorithm (DBP) is developed and used to train both DRNC and DRNI. Due to the recurrence, the DRNN can capture the dynamic behavior of a system. To guarantee convergence and for faster learning, an approach that uses adaptive learning rates is developed by introducing a Lyapunov function. Convergence theorems for the adaptive backpropagation algorithms are developed for both DRNI and DRNC. The proposed DRNN paradigm is applied to numerical problems and the simulation results are included.     

基于相干控制的二能级量子系统退相干抑制

对于二能级开放量子系统,研究了利用相干控制抑制退相干效应的问题.首先讨论了二能级开放量子系统在相干控制下的建模问题,将退相干抑制归结为与环境噪声解耦的控制问题.然后,引入开环控制抑制退相干,并证明该控制可使系统状态中的部分分量与环境噪声渐近解耦.最后引入反馈控制,使得系统状态的相应分量可以与环境精确解耦,同时能够避免测量引入的量子噪声的影响.     

Multivariable Intelligent Decoupling Control System and its Application

Many industrial processes have compositive complexities including multivariable, strong coupling, nonlinearity, time-variant and operating condition variations. Combining multivariable adaptive decoupling control with neural networks, this paper presents a multivariable neural network-based decoupling control algorithm. This control algorithm is integrated with distributed control technique and intelligent control technique, and a three-leveled intelligent decoupling control system consisting of basic control level, coordinating control level, and management and decision level is developed. The configuration and function of the control system are discussed in detail. This system has been successfully applied in ball mill pulverizing systems of 200MW power units, and remarkable benefits have been obtained.     

Multivariable Intelligent Decoupling Control System and its Application

Many industrial processes have compositive complexities including multivariable, strong coupling, nonlinearity, time-variant and operating condition variations. Combining multivariable adaptive decoupling control with neural networks, this paper presents a multivariable neural network-based decoupling control algorithm. This control algorithm is integrated with distributed control technique and intelligent control technique, and a three-leveled intelligent decoupling control system consisting of basic control level, coordinating control level, and management and decision level is developed. The configuration and function of the control system are discussed in detail. This system has been successfully applied in ball mill pulverizing systems of 200MW power units, and remarkable benefits have been obtained.