具有解耦性能的离散时间线性多变量系统最优跟踪控制

在传统线性二次跟踪控制方法的基础上, 针对一类具有强耦合特性的离散时间线性多变量系统, 提出了一种具有解耦性能的最优跟踪控制方法. 首先为实现解耦, 将耦合项作为可测干扰, 基于零和博弈思想提出了一种新的性能指标; 然后针对该性能指标, 利用极小值原理设计最优跟踪控制器, 通过适当加权矩阵的选择, 同步实现解耦和跟踪; 最后进行仿真实验, 仿真结果表明了该方法的有效性以及在最优性能等方面的优越性.     

热风炉燃烧系统最优控制

热风炉燃烧系统是复杂多变量系统,基于最优控制策略,对具有耦合作用的多变量热风炉燃烧系统进行解耦.通过引入神经网络环节,将强耦合多变量系统转化成多个独立的单变量系统,对每个单变量系统进行预测函数控制,实现热风炉燃料流量的最优控制和拱顶温度及废气温度的平稳控制.解耦控制采用前馈补偿器解耦,解耦补偿器采用BP神经网络结构.现场实际应用结果表明,该控制策略具有较好的动态跟踪特性,能满足复杂多变量控制系统的实时控制要求.     

基于观测器的受扰非线性系统近似最优跟踪控制

研究一类受扰非线性系统的最优输出跟踪控制问题.给出了有限时域最优输出跟踪控制律的近似设计算法.首先将求解受扰非线性系统最优跟踪控制问题转换为求解状态向量与伴随向量耦合的非线性两点边值问题,然后利用逐次逼近方法构造序列将其转化为求解两个解耦的线性微分方程序列问题.通过迭代求解伴随向量的序列,可得到由解析的线性前馈-反馈控制部分和伴随向量的极限形式的非线性补偿部分组成的最优输出跟踪控制律.利用参考输入降维观测器和扰动降维观测器,解决了前馈控制的物理可实现问题.最后仿真结果表明了该方法的有效性.     

变风量空调系统恒温解耦控制优化

在室内恒温控制问题的研究中,变风量空调系统的房间送风量、冷冻水流量和风机转速三个输入变量与房间温度、送风温度和静压三个输出变量之间存在着不同程度的耦合关系,每个房间的温度控制会受到不同程度的干扰,严重时会影响到整个系统的稳定性.为解决上述问题,根据变风量空调系统的房间、表冷器、风机等各个子系统模型,通过寻找一个合适的开环传递函数矩阵,实现对系统的解耦控制,通过使解耦矩阵的对角元素为1,得到简化的解耦矩阵.比通常利用对角化方法和状态反馈矩阵方法直接求得的解耦矩阵要简单.运行空调实验结果表明控制回路之间干扰不明显,解耦控制效果良好.     

基于解耦的球磨机系统动态矩阵控制

针对制粉系统球磨机的多变量强耦合特性,提出基于逆向解耦的动态矩阵控制设计方法,先将球磨机系统分解为三个单输入单输出系统,然后利用基于对象阶跃响应的动态矩阵控制算法,对解耦后的广义系统进行了仿真研究.结果表明,逆向解耦能有效地解除变量间的强耦合和大时延问题,并且动态矩阵预测控制算法控制效果明显,优于传统的PID控制,具有实时性强、计算速度快和抗干扰能力好等优点.     

基于期望控制函数频率特征的解耦多变量控制

提出一种简单的解耦多变量系统比例积分微分(PID)控制器设计方法;首先,将多变量过程前馈解耦为多个独立单回路系统,利用解耦器对角环节保证解耦器的稳定性和可物理实现性;其次,针对每个已解耦单回路系统,提出同时考虑设定值跟踪、扰动抑制和鲁棒稳定性要求的期望闭环传递函数,由此得到期望控制函数,并基于期望控制函数的频率特征快速获取PID控制参数;最后,示例了解耦多变量控制器的设计过程;仿真结果表明该解耦PID设计法对一般多变量时滞过程的控制是有效的。     

多变量发酵过程的神经网络在线解耦控制

为提高多变量系统解耦控制的性能,提出了一种基于参考模型的神经网络在线解耦控制方法.构造神经网络实现前馈解耦,通过参考模型的输出与被控系统输出估计耦合作用对被控量的影响,由此设计神经网络权值参数学习算法,在线调整网络参数使多变量耦合系统实现解耦;对解耦后的子系统分别设计闭环控制器,以达到优良的控制性能.仿真实验结果表明,提出的解耦控制方法是简单有效的.     

多变量系统的广义预测控制解耦设计

针对在系统关联严重的情况下,通过选择合适的权矩阵,预测控制可以实现间接解耦控制,但是系统的跟踪性能将大大降低的问题,提出了一种带设定值观测器的多变量广叉预测解耦控制算法,结合前馈解耦控制策略与广义预测控制律,将其他通道对当前通道的影响作为扰动,从而实现近似解耦.在目标函数中用控制量的增量代替控制量,使控制增量变化不至于...     

解析解耦设计在变风量空调系统中的应用

研究空调优化控制问题,变风量空调(VAV)具有多变量、强耦合和非线性系统.变量之间的耦合会直接影响空调系统的性能指标和稳定性.针对变风量空调系统进行建模,模型为三输入三输出的传递函数矩阵.在内模控制系统结构基础上,运用解耦控制的解析设计方法来设计解耦控制器.解耦设计方法可以达到标称系统变量间完全解耦,并且参数可以在线调整,使输出能够紧密跟随系统的动态变化.仿真结果表明,解耦控制器的设计提高了性能,应用效果较好.     

多变量解耦控制系统设计与仿真

工业生产过程中,被控系统越来越复杂,需要控制的变量通常不止一对,而且相互耦合,较成熟的线性多变量控制理论与设计方法已难以满足实际的多变量生产过程控制。本文针对多变量强耦合带有延迟环节的被控系统,研究了一种简化的串联前馈补偿解耦控制器设计方法。根据被控对象的辨识模型,设计串联前馈补偿解耦器,使系统解耦成单输入单输出模型,进而分别对解耦后输入输出模型设计PID控制器,整定PID控制器参数。仿真结果表明该方法具有较好的解耦能力和鲁棒性,算法简单,易于实现。     

Analytical decoupling control strategy using a unity feedback control structure for MIMO processes with time delays

An analytical decoupling control method is proposed for multiple-input–multiple-output (MIMO) processes with multiple time delays. The desired diagonal system transfer matrix is proposed first in terms of the H₂ optimal performance specification, resulting in the ideal desired decoupling controller matrix derived within the framework of a unity feedback control structure. It is demonstrated that dead-time compensators must be enclosed in the decoupling controller matrix to realize absolute decoupling for MIMO processes with multiple time delays. To alleviate the difficulties associated with the implementation, the ideal desired decoupling controller matrix is transformed into a practical form using an analytical approximation approach. Correspondingly, the stability of the resultant control system is assessed, together with its robust stability in the presence of process uncertainties. An on-line tuning rule for the single adjustable parameter of each column controllers in the decoupling controller matrix is given to cope with the process unmodeled dynamics. Finally, illustrative examples are given to show the superiority of the proposed method over the recently improved decoupling control methods.     

Optimal Decoupling Control Method and Its Application to a Ball Mill Coal-pulverizing System

The conventional optimal tracking control method cannot realize decoupling control of linear systems with a strong coupling property. To solve this problem, in this paper, an optimal decoupling control method is proposed, which can simultaneously provide optimal performance. The optimal decoupling controller is composed of an inner-loop decoupling controller and an outer-loop optimal tracking controller. First, by introducing one virtual control variable, the original differential equation on state is converted to a generalized system on output. Then, by introducing the other virtual control variable, and viewing the coupling terms as the measurable disturbances, the generalized system is open-loop decoupled. Finally, for the decoupled system, the optimal tracking control method is used. It is proved that the decoupling control is optimal for a certain performance index. Simulations on a ball mill coal-pulverizing system are conducted. The results show the effectiveness and superiority of the proposed method as compared with the conventional optimal quadratic tracking (LQT) control method.      

Direct adaptive decoupling control for general stochastic multivariable systems

This paper presents a novel adaptive decoupling controller for general stochastic multivariable systems with arbitrary time delay structure. The decoupling controller is constructed using a diagonal dynamic precompensator to diagonalize the system interactor matrix and then combining the feedforward control strategy and the generalized minimum variance approach. It cannot only control unstable and'or non-minimum phase processes but also decouple the closed-loop systems both dynamically and in the static state. It is adaptively implemented in direct form. The global convergence properties and parameter estimate consistency for this adaptive decoupling algorithm are also discussed. It is shown that this adaptive controller not only has globally convergent properties but also generates strongly consistent parameter estimates. The results of simulations and an application are proposed to demonstrate the effectiveness of the algorithm.     

基于鲁棒开环解耦的系统逆二自由度控制

针对MIMO系统的轨迹跟踪,提出一种基于鲁棒开环解耦的系统逆二自由度（2DOF）控制方法．该方法通过鲁棒开环解耦解决MIMO系统的耦合问题,利用系统逆2DOF控制保证轨迹跟踪性能．首先提出一种保证预补偿器阶数最小和解耦系统鲁棒性的鲁棒开环解耦方法;然后结合鲁棒开环解耦和系统逆2DOF控制,给出系统逆前馈控制和H∞混合灵敏度鲁棒反馈控制器的设计方法;最后通过设计实例及仿真结果验证了该控制方法的有效性．     

Tracking control of a two-wheeled mobile robot using input–output linearization

This paper discusses the tracking control of two-wheeled mobile robots that have more outputs than inputs. If the number of inputs and the number of outputs are not the same, the inverse of the decoupling matrix in the input–output linearization does not exist. Therefore, a modified input–output linearization method is proposed, to solve the problem by means of a generalized inverse that provides a least-squares solution. The experimental results show that the tracking of posture, position and orientation (the 3-output case) has advantages over position tracking (the 2-output case) from the viewpoint of input power efficiency, because smoother responses can be obtained by considering the orientation.     

Reduced-order solution to the finite-time optimal-control problemsof linear weakly coupled systems

The optimal solution to the finite-time optimal-control problem of weakly coupled linear systems is found in terms of completely decoupled reduced-order differential equations for both closed-loop and open-loop control. This is achieved through the use of the decoupling transformation that block diagonalizes the Hamiltonian matrix of the weakly coupled linear-quadratic control problem. The convergence to the optimal solution is pretty rapid. The proposed technique is very well suited for parallel computations     

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。     

Nonlinear Decoupling PID Control Using Neural Networks and Multiple Models

For a class of complex industrial processes with strong nonlinearity, serious coupling and uncertainty, a nonlinear decoupling proportional-integral-differential （PID） controller is proposed, which consists of a traditional PID controller, a decoupling compensator and a feedforward compensator for the unmodeled dynamics. The parameters of such controller is selected based on the generalized minimum variance control law. The unmodeled dynamics is estimated and compensated by neural networks, a switching mechanism is introduced to improve tracking performance, then a nonlinear decoupling PID control algorithm is proposed. All signals in such switching system are globally bounded and the tracking error is convergent. Simulations show effectiveness of the algorithm.     

考虑输入饱和的农用拖拉机路径跟踪有限时间控制

针对农用拖拉机的路径跟踪控制问题,提出基于有限时间和饱和技术的路径跟踪控制策略.首先,建立农机路径跟踪运动学模型,并通过有限时间控制技术,构造有限时间路径跟踪控制方法;其次,考虑到农用拖拉机的转向系统物理限制,将饱和技术与有限时间控制结合,给出复合的路径跟踪控制方法,通过严格的理论分析验证闭环系统在该控制器下的有限时间稳定性;最后,通过仿真结果表明,所设计的制导方法可以保证农用拖拉机快速、稳定地完成路径跟踪目标.     

Nonlinear Decoupling PID Control Using Neural Networks and Multiple Models

For a class of complex industrial processes with strong nonlinearity, serious coupling and uncertainty, a nonlinear decoupling proportional-integral-differential (PID) controller is proposed, which consists of a traditional PID controller, a decoupling compensator and a feedforward compensator for the unmodeled dynamics. The parameters of such controller is selected based on the generalized minimum variance control law. The unmodeled dynamics is estimated and compensated by neural networks, a switching mechanism is introduced to improve tracking performance, then a nonlinear decoupling PID control algorithm is proposed. All signals in such switching system are globally bounded and the tracking error is convergent. Simulations show effectiveness of the algorithm.