一种基于衰减集结的鲁棒预测控制器

高效鲁棒预测控制 (ERPC) 是一种在线计算量较小, 且控制性能较好的鲁棒预测控制算法. 但采用单一椭圆不变集的设计方法存在保守性. 本文采用衰减集结策略, 通过离线设计在系统状态空间中投影彼此正交的两个椭圆不变集, 在线进行凸组合的方法设计 ERPC 控制器, 使系统初始可行域进一步扩大, 并在一定程度上改善了控制性能.     

约束分段仿射系统的鲁棒最小时间控制

针对一类有界附加扰动的分段仿射系统,提出了一种离线低复杂性的鲁棒预测控制方法-鲁棒最小时间控制.首先计算系统的最大鲁棒正不变集和相关的局部稳定控制律,然后基于最大鲁棒正不变集通过多参数规划离线迭代计算系统的鲁棒一步集,得到的最小时间控制器覆盖最大鲁棒可稳定集.提出的鲁棒最小时间控制确保系统状态在最小时间内进入鲁棒正不变...     

基于鲁棒一步集的Tube不变集鲁棒模型预测控制

针对一类干扰有界的输入和状态受约束线性离散系统,提出了一种基于鲁棒一步集的Tube不 变集鲁棒模型预测控制方法.首先采用多面体不变集离线设计方法得到基于多面体不变集序列的扩 展终端约束集;然后为了扩大鲁棒模型预测控制的初始状态允许区域,并提高系统的鲁棒性,在扩展终端约束集的基础上,通过引入鲁棒一步集并借助Tube不变集控制策略,设计了基于鲁棒一步集的鲁棒模型预测控制方法,并给出了算法的存在性和稳定性证明. 该方法不仅极大地扩大了初始状态允许区域,而且对有界干扰具有有效的抑制作用,使得受扰系统收敛到以原点为中心的最小鲁棒正不变集内.最后仿真验证了算法的有效性.     

基于混合H2/H∞ 的鲁棒预测控制综合设计方法

针对具有有界扰动的多胞不确定约束系统,基于混合H2/H∞指标,提出一种鲁棒预测控制器设计方法.该控制器在保证系统对外界扰动的抑制指标前提下,具有较好的控制性能.同时,考虑了该控制器的可行性设计问题,通过附加约束保证了预测控制器的递归可行性.     

约束分段仿射系统的鲁棒一步预测控制

针对一类具有附加有界扰动的离散时间约束分段仿射系统,提出了一种鲁棒低复杂性的模型预测控制方法,即鲁棒一步控制.首先,基于最大鲁棒正不变集,计算系统的最大鲁棒可稳定集并作为第一步预测状态的约束集,使得产生的滚动时域控制器可以在较小的预测时域内控制最大鲁棒可稳定集.然后,在最大鲁棒正不变集外,通过构建线性矩阵不等式来分析其鲁棒稳定性.两个步骤分别确保控制器的可行性和闭环系统的鲁棒稳定性.大量的数值例子表明,和已有的控制方法相比,所得的鲁棒一步控制器具有更低的复杂性.     

约束分段仿射系统的鲁棒一步预测控制

针对一类具有附加有界扰动的离散时间约束分段仿射系统,提出了一种鲁棒低复杂性的模型预测控制方法,即鲁棒一步控制。首先,基于最大鲁棒正不变集,计算系统的最大鲁棒可稳定集并作为第一步预测状态的约束集,使得产生的滚动时域控制器可以在较小的预测时域内控制最大鲁棒可稳定集。然后,在最大鲁棒正不变集外,通过构建线性矩阵不等式来寻找而二次李雅普诺夫函数以证明其鲁棒稳定性。两个步骤分别确保控制器的可行性和闭环系统的鲁棒稳定性。大量的仿真例子表明,和已有的控制方法相比,所得的鲁棒一步控制器具有更低的复杂性。     

采用鲁棒容许集对有约束的不确定系统作鲁棒模型预测控制

针对输入和状态受约束的干扰有界多胞不确定线性系统,提出了基于鲁棒容许集的扩大吸引域鲁棒模型预测控制(RMPC)方法.首先给出了多面体不变集的鲁棒容许集计算方法,并推导了鲁棒容许集存在的充分必要条件.其次,为了拓展Tube不变集鲁棒模型预测控制算法的适用范围,讨论了干扰有界多胞不确定线性系统的Tube不变集控制策略.之后为了扩大约束系统吸引域,提出了干扰有界多胞不确定系统的鲁棒容许集模型预测控制策略.通过采用鲁棒容许集和Tube不变集RMPC,该方法不仅扩大了吸引域,而且降低了在线计算量;同时,采用基于最小鲁棒正不变集的Tube不变集策略保证了算法的鲁棒性.最后仿真结果验证了算法的有效性.     

一种基于集结的鲁棒MPC控制器

针对存在有界外界扰动的有约束线性系统,本文提出了一种新的MPC控制器ABRMPC. 一方面,通过引入输入幅值衰减集结策略,使得各时刻优化的变量数大大减少,简化了在线计 算;另一方面,在对各步的扰动进行了考虑之后,得到新的状态约束,可保证系统实际状态始终处 于原约束域内.本文重点对如何得到下一时刻的可行解进行了研究,指出了衰减系数的上界应满 足的条件;而后证明了系统输入将最终趋于零,同时该控制器可使处于不变集之外的系统状态趋 向于不变集,从而使系统具有鲁棒稳定性.最后通过仿真实例对文中结论进行了验证.     

一类带时滞状态扰动系统的自适应鲁棒镇定

针对一类带时滞状态扰动的系统,讨论了系统的鲁棒自适应镇定问题.当扰动有界且界未知时,运用自适应控制方法,设计出一类自适应控制器.采用Lyapunov＿Karasovskii函数方法,证明了文中所提出的控制器可鲁棒镇定该系统.     

不确定Markov 跳跃线性系统的鲁棒跟踪与模型跟随

讨论一类带wiener过程的不确定Markov跳跃线性系统的鲁棒跟踪和模型跟随问题．通过构造一组非线性鲁棒状态反馈跟踪控制器来跟踪给定的动态信号，该控制器确保系统当时间趋于无穷时是以扰动衰减系数鲁棒随机稳定的，且跟踪误差有界．最后给出了算例，说明所设计的鲁棒跟踪控制器具有较好的跟踪性能．     

模型跟踪广义预测鲁棒自适应控制器

本文采用滤波CARMA模型,基于内模原理,提出了一种新的广义预测鲁棒自适应控制器,并分析了闭环系统性能,在新的控制器中,引入适当的前馈作用,使得跟踪和调节问题解耦,利用部分状态跟踪、模型参考以及极点配置方法解决跟踪问题,利用多步预测滚动优化方法解决调节问题;适当选择滤波器可以保证对平稳随机扰动有满意的响应,减少可调参数对闭环系统响应的影响,增强系统对未建模动态的鲁棒性,仿真结果表明:该控制器对确定性和非平稳随机扰动具有不变性,对系统时延和阶次变化具有鲁棒性,适用于非最小相位和开环不稳定系统。     

Nonlinear model predictive control from data: a set membership approach

A new approach to design a Nonlinear Model Predictive Control law that employs an approximate model, derived directly from data, is introduced. The main advantage of using such models lies in the possibility to obtain a finite computable bound on the worst‐case model error. Such a bound can be exploited to analyze the robust convergence of the system trajectories to a neighborhood of the origin. The effectiveness of the proposed approach, named Set Membership Predictive Control, is shown in a vehicle lateral stability control problem, through numerical simulations of harsh maneuvers. Copyright © 2012 John Wiley & Sons, Ltd.     

汽轮机系统的鲁棒预测控制仿真研究

为了进一步提高汽轮机的动态控制性能，本文在阐述了汽轮机的数学模型的基础上。提出将鲁棒广义预测自校正控制算法应用于汽轮机的转速控制中，包括控制结构和控制器设计。仿真结果显示当核动力装置模型失配时，鲁棒广义预测控制能够提高汽轮机系统的鲁棒稳定性。表明所采用的鲁棒广义预测控制算法能够较好的控制汽轮机主要参数的输出，对蒸汽轮机模型的不确定性具有良好的适应性和鲁棒性。     

Robustness and coordination in voltage control of large-scale power systems

In this paper it is shown how the robustness and the coordination of the voltage regulation actions for the transmission grid can be improved. Simpler approaches which ensure higher robustness and performances can be used if the control objectives are pursued at two hierarchical levels of different nature. Also, this is a way to coordinate means of control of different nature with a sufficient time and methodological separation in order to avoid negative mutual influence. At the first level, called the static level, optimal reachable set-points are computed for the second control level, called the dynamic level. The static level can be combined with the shunt reactive power compensation. The system non-linearities are taken into account at the static level while the dynamic level is a linear robust predictive control which takes into account the presence of asynchronous transmission delays. The predictive control strategy is based on the separation property; the output delays are handled using an original steady-state Kalman predictor of order equal to the length of the state of the system without delays. The robustness is improved at the dynamic level against uncertain delays, parametric uncertainties (like, e.g., moderate topological errors and load variations not taken into account in the control model) and unmodelled dynamics. The two-level organisation of the control allows, on one hand, to take into account the important evolutions of the system (like, e.g., large and known topological and load changes) and, on the other hand, a coherent hybrid reactive power control: the switched control of the grid shunt compensation for the reactive power is done at the static level while the reactive power injection provided by the generators is continuously handled at the dynamic level. This is a theoretical analysis of how concepts of automatic control and voltage regulation of power systems can be combined. To be applied as a control scheme, the results presented here should be adapted to a specific context (particularities of the power system and of the organisation of the power industry). They can be used, eventually in conjunction with other improvements, to existing horizontally-organised interconnections (in which all generators of a controlled region can be easily managed since owned by the same utility) or to face specific requirements of moving to the open access in the electric power industry like, e.g., tolerating simplified models in order to cover larger regions, taking into account the interaction between regions, recalibrating set-points, assisting human operator when necessary or facilitating implementation of mechanisms for the management of the reactive power based on price signals.     

广义预测控制的鲁棒化改进

利用内模控制结构分析了广义预测控制在未建模动态鲁棒性方面的缺陷,提出采用失配滤波器以增强系统的鲁棒性。针对广义预测控制的特点,提出了次优失配滤波器的简单设计方法。     

A robust economic MPC for changing economic criterion

This note presents a robust economic model predictive control controller suitable for changing economic criterion. The proposal ensures feasibility under any change of the economic criterion, thanks to the use of artificial variables and a relaxed terminal constraint, and robustness in presence of additive bounded disturbances. The resulting robust formulation considers a nominal prediction model and restricted constraints (in order to account for the effect of additive disturbances). The controlled system under the proposed controller is shown to be input‐to‐state stable in the sense that it is asymptotically steered to an invariant region around the best admissible steady state. An illustrative example shows the benefits and the properties of the proposed controller.     

A computer-aided design tool for robustness analysis and control-relevant identification of Horizon Predictive Control with application to a binary distillation column

This paper describes a MATLAB-based computer-aided design tool, IRA-HPC, which accomplishes integrated system identification and robustness analysis for Horizon Predictive Control (HPC), a model predictive control algorithm implemented on the Application Module of the Honeywell TDC 3000 distributed control system. The tool addresses lifecycle as well as functional aspects of the technology, with the goal of making advanced control principles more accessible to the practising control engineer. IRA-HPC systematically performs the various stages of system identification in a control-relevant framework (addressing input design, parameter estimation, and model validation from the standpoint of the final purpose of the model, which is control system design), followed by robust HPC controller tuning using the Structured Singular Value (μ) paradigm as a basis. The benefits of the tool are shown experimentally in the modelling and control of a methanol/isopropanol pilot-scale distillation column, interfaced to an industrial-scale real-time computing testbed. The example demonstrates the practical feasibility of this tool and its benefits in terms of simplifying the choices of design variables in integrated identification and control design.     

A note on pole placement using dynamic compensators

The classical Routh's algorithm has the drawback that it involves divisions. Hence if one starts for example with integer or polynomial entries one ends up with rational numbers or rational functions respectively. Using Hurwitz determinants instead one can avoid this drawback in principle. However an efficient way to compute these determinants without introducing fractions has been missing. An algorithm to compute these determinants in a fraction free manner is presented. It is shown that this algorithm is optimal with respect to the growth of the entries.     

Robust model predictive control for constrained continuous-time nonlinear systems

In this paper, a robust model predictive control (MPC) is designed for a class of constrained continuous-time nonlinear systems with bounded additive disturbances. The robust MPC consists of a nonlinear feedback control and a continuous-time model-based dual-mode MPC. The nonlinear feedback control guarantees the actual trajectory being contained in a tube centred at the nominal trajectory. The dual-mode MPC is designed to ensure asymptotic convergence of the nominal trajectory to zero. This paper extends current results on discrete-time model-based tube MPC and linear system model-based tube MPC to continuous-time nonlinear model-based tube MPC. The feasibility and robustness of the proposed robust MPC have been demonstrated by theoretical analysis and applications to a cart-damper springer system and a one-link robot manipulator.     

Feedback min–max model predictive control using robust one-step sets

A solution to the infinite-horizon min–max model predictive control (MPC) problem of constrained polytopic systems has recently been defined in terms of a sequence of free control moves over a fixed horizon and a state feedback law in the terminal region using a time-varying terminal cost. The advantage of this formulation is the enlargement of the admissible set of initial states without sacrificing local optimality, but this comes at the expense of higher computational complexity. This article, by means of a counterexample, shows that the robust feasibility and stability properties of such algorithms are not, in general, guaranteed when more than one control move is adopted. For this reason, this work presents a novel formulation of min–max MPC based on the concept of within-horizon feedback and robust contractive set theory that ensures robust stability for any choice of the control horizon. A parameter-dependent feedback extension is also proposed and analysed. The effectiveness of the algorithms is demonstrated with two numerical examples.