基于可行模型集合方法的多模型切换控制

对含参数跳变的一类具有有界干扰的多输入多输出系统，为改善系统的瞬态响应性能，采用由多个固定模型和两个自适应模型组成的多模型，并引入切换指标函数构成多模型切换控制器。为克服多模型方法计算量大的缺点，采用可行模型集合方法，给出了可行模型应满足的必要条件。应用这一必要条件，对系统的多个模型进行检验，可快速缩小模型可行集合的范围，再通过切换指标函数，在可行模型集合中选择正确的控制器，从而在不降低系统响应性能的前提下，提高计算速度。     

多模型切换系统H_∞鲁棒控制器的设计与应用

基于H∞控制理论以及切换系统稳定性理论,对于多输入多输出(MIMO)多模型切换控制系统,提出了一种可以有效抑制抖动和改善瞬态响应性能的鲁棒镇定控制器设计方法.通过引入PI控制思想,根据模型跟踪方法设计了增广状态反馈控制器,并将控制器设计问题转化为方便求解的线性矩阵不等式(LMI).该方法的最大优点是可以很方便的保证多模型切换系统的全局稳定性,同时使得设计的控制器具有较强的鲁棒性.将本文提出的方法应用到某型BTT导弹自动驾驶仪设计中,仿真结果证明了此方法的有效性和优越性.     

基于局部化技术的多模型自适应控制

对一类含跳变参数的单输入单输出离散时间被控对象,建立由多固定模型和自适应 模型组成的多模型,并通过引入指标切换函数构成多模型自适应极点配置控制器,在保证闭 环系统稳定的前提下,改善系统瞬态响应.同时采用局部化(Localization)技术优化多模型模 型集,在不损失计算精度的前提下,大大减少了计算量,提高了计算速度.     

基于极点配置的多模型自适应控制

基于极点配置技术对单输入单输出离散时间被控对象提出多模型自适应控制算法，在保证稳定性的前提下，算法对被控对象参数跳变时能进行有效的控制，并提高了系统的瞬态响应。     

多模型预测函数控制及其应用研究

提出了多模型预测函数控制策略，并给出了模型平滑切换方法。格多模型预测函数控制策略应用于主汽温控制系统，仿真结果表明，采用多模型预测函数控制策略的主汽温控制系统的动态品质明显优于采用单一模型的系统。     

多层次多模型预测控制算法的模型切换方法研究

针对多层次多模型(Multi-hierarchical multi-model, MHM)预测控制系统的模型切换问题, 在分析各通道非线性程度对模型层次切换以及层次 间模型切换过程对系统动态特性的影响的基础上, 提出了一种新的模型切换方法. 并将此方法应用到多输入多输出pH 中和过程, 仿真结果表明, 该方法有效地改善了系统工况大范围跳变时的动态性能.     

基于多模型切换的多变量直接自适应极点配置控制器

提出一种基于多模型切换的多变量直接自适应控制器,控制器参数模型集可由系统参数模型集直接得到,不但仍为一紧集并且被其模型子集完全覆盖.该多模型控制器不仅改善了控制系统的暂态响应,而且可以实现极点配置,最后给出了全局收敛性分析.     

一类非最小相位系统的多变量多模型解耦控制器

为解决系统暂态响应变差问题，提出一种基于多模型切换的多变量直接自适应控制器，通过加权多项式矩阵的选择，可消除稳态误差，实现静态解耦控制，该控制器由多个参数已知固定模型和两个自适应模型构成，多个固定参数控制器模型可由系统参数模型通过映射直接得到，并与邻城一起完全覆盖控制器参数模型集，仿真结果表明，对于非最小相位系统，暂态响应可得到明显改善。     

基于控制量补偿的多模型主汽温控制系统

本文针对过热汽温对象的特性参数随机组运行工况的变化而有较大变化的特点,设计了一种基于控制量补偿和多模型切换的串级控制系统,基于不同工况点设计的控制器可根据运行工况进行切换。仿真结果表明这种控制方案实现了系统全工况运行的满意控制。     

基于多模型混合最小方差控制的时变扰动控制系统性能评估

在实际工业过程中,控制系统经常会受到时变扰动的影响,致使针对单一扰动模型设计的最小方差控制准则不再适用于评估时变扰动控制系统的性能. 当多个扰动信号同时出现时,采用常规多模型切换方法会发生间歇切换进而产生较大的暂态误差,不能准确评估系统当前性能. 针对上述问题,本文提出了一种基于多模型混合最小方差控制准则的性能评估方法. 首先根据每个扰动模型分别制定最小方差控制器,组成多模型最小方差控制器,然后在每个时间点混合多模型最小方差控制器,并将在其作用下的输出方差作为最终的性能评估基准,该方法既 充分考虑到每个扰动的特性,又避免了常规多模型切换方法因间歇切换而产生的暂态误差对评估结果准确性带来的影响,实现了准确、可靠地评估时变扰动控制系统的性能. 通过仿真,验证了基于多模型混合最小方差控制准则的性能评估方法的有效性.     

基于EL模型的MMC无源控制器设计

建立了基于开关状态函数的MMC电磁暂态数学模型,设计了改进的基于EL模型的MMC无源控制器。该控制器采用误差控制方式,即通过控制误差为零使得MCC控制系统实际输出的物理量跟踪参考给定值;为使误差能量快速衰减为零,加快耗散特性,引入了阻尼矩阵;为消除稳态残差电压,将MMC直流电压引入控制系统,与参考电压相比较,经PI调节后与参考有功分量相加得到最终的有功参考分量。仿真结果表明,该控制器能够完全跟踪参考给定值,同时能降低电压波动范围,具有较好的动态响应特性。     

Multiple model adaptive control for switched linear systems: A two‐layer switching strategy

In this paper, the multiple model adaptive control scheme is first introduced into a class of switched systems. A switched multiple model adaptive control scheme is proposed to improve the transient behavior by resetting the controller parameters. Firstly, a finite‐time parameter identification model is presented, which greatly reduces the number of identification models. Secondly, a two‐layer switching strategy is constructed. The outer layer switching mechanism is to ensure the stability of the switched systems. The inner layer switching mechanism is to improve the transient behavior. Then, by using the constructed jumping multiple Lyapunov functions, the proposed adaptive control scheme guarantees that all the closed‐loop system signals remain bounded and the state tracking error converges to a small ball whose radius can be made arbitrarily small by appropriately choosing the design parameter. Finally, a practical example about model reference adaptive control of an electrohydraulic system using multiple models is given to demonstrate the validity of the main results.     

A moving switching sequence approach for nonlinear model predictive control of switched systems with state‐dependent switches and state jumps

In this paper, we propose an approach for real‐time implementation of nonlinear model predictive control (NMPC) for switched systems with state‐dependent switches called the moving switching sequence approach. In this approach, the switching sequence on the horizon moves to the present time at each time as well as the optimal state trajectory and the optimal control input on the horizon. We assume that the switching sequence is basically invariant until the first predicted switching time reaches the current time or a new switch enters the horizon. This assumption is reasonable in NMPC for systems with state‐dependent switches and reduces computational cost significantly compared with the direct optimization of the switching sequence all over the horizon. We update the switching sequence by checking whether an additional switch occurs or not at the last interval of the present switching sequence and whether the actual switch occurs or not between the current time and the next sampling time. We propose an algorithm consisting of two parts: (1) the local optimization of the control input and switching instants by solving the two‐point boundary‐value problem for the whole horizon under a given switching sequence and (2) the detection of an additional switch and the reconstruction of the solution taking into account the additional switch. We demonstrate the effectiveness of the proposed method through numerical simulations of a compass‐like biped walking robot, which contains state‐dependent switches and state jumps.     

Gaussian process internal model control

Nonlinear modelling approaches such as neural networks, fuzzy models and multiple model networks have been proposed for model based control, to improve the poor transient response of adaptive control techniques. The quality of control is known to be strongly related to the accuracy of the model which represents the process. A Bayesian Gaussian process (GP) approach provides an analytic prediction of the model uncertainty, which makes the GP model an ideal candidate for model based control strategies. This article extends the use of the GP model for nonlinear internal model control. The invertibility of the GP model is discussed and the use of predicted variance is illustrated on a simulated example.     

An Improved Result of Multiple Model Iterative Learning Control

For system operating repetitively, iterative learning control (ILC) has been tested as an effective method even with estimated models. However, the control performance may deteriorate due to sudden system failure or the adoption of imprecise model. The multiple model iterative learning control (MMILC) method shows great potential to improve the transient response and control performance. However, in existed MMILC, the stability can be guaranteed only by finite switching or very strict conditions about coefficient matrix, which make the application of MMILC a little difficult. In this paper, an improved MMILC method is presented. Control procedure is simplified and the ceasing condition is relaxed. Even with infinite times of model switching, system output is proved convergent to the desired trajectory. Simulation studies are carried out to show the effectiveness of the proposed method.      

锅炉–汽轮机系统的模糊经济模型预测控制

现代电厂的优化控制通常采用双层控制结构,上层通过优化经济性能指标获得稳态设定值,传递到下层实现设定值跟踪.然而,传统的控制结构往往会忽略动态跟踪过程中的经济性能.本文针对锅炉–汽轮机系统设计了基于模糊模型的经济模型预测控制策略.通过离线设计稳定的线性反馈控制律和可行域,来保证优化问题的递推可行性和稳定性.通过在线求解双模态经济模型预测控制优化问题,实现锅炉汽轮机系统动态过程中经济性能的提高.大范围和小范围负荷变化情况下的仿真结果表明了本文提出的模糊经济模型预测控制的有效性.     

Hierarchically supervised control of linear plants with unpredictable mode-switch dynamics

A mode-switch process is defined allowing a linear, piecewise time-invariant plant to switch, from time to time, from the actual configuration to another one of a given family of linear, time-invariant plants. When and where switching occurs is not a priori known. It is required to find a stabilizing controller yielding an output response with some desired transient specifications. The solution proposed is given by the connection of multiple families of linear controllers with a hierarchically supervised switching scheme.     

质子交换膜燃料电池动态建模及其双模控制

由于已提出的质子交换膜燃料电池(PEMFC)模型难于控制, 提出利用MATLAB/SIMULINK仿真工具进行PEMFC系统动态建模, 同时为实现对PEMFC系统输出电压的控制, 采用了基于模糊规则切换的模糊逻辑控制器(FLC)和比例积分微分控制器(PID)相结合的双模控制方式. 仿真结果证明该动态模型易于控制, 能够反映出PEMFC系统的动态输出特性, 而且验证了基于模糊规则切换的双模控制能够有效抑制扰动, 改善PEMFC系统的动态输出特性, 保证系统的稳定运行, 有助于对PEMFC系统的输出性能分析以及实时控制系统的设计.