迭代学习模型预测控制研究现状与挑战

历经20多年的发展, 迭代学习模型预测控制在理论和应用方面都取得了长足的进步. 但由于批次工业过程复杂多样、结构各异、精细化程度较高, 现有的迭代学习模型预测控制理论仍面临着巨大挑战. 本文简要回顾了迭代学习模型预测控制理论的产生及发展, 阐述了二维预测模型、控制律迭代优化及二维稳定性等基本理论问题; 分析了现有方法在理论及应用方面的局限性, 说明了迭代学习模型预测控制在迭代建模、高效优化、变工况适应等方面面临的难点问题, 提出了可行的解决方案. 简要综述了近年来迭代学习模型预测控制理论和应用层面的发展动态, 指出了研究复杂非线性系统、快速系统、变工况系统对进一步完善其理论体系和拓宽其应用前景的意义, 展望了成品质量控制和动态经济控制等重要的未来研究方向.     

非线性模型预测控制的理论及应用综述

对非线性模型预测控制进行了综述.从理论研究和实际应用方面,总结和归纳了非线性预测控制的进展情况,根据不同的预测模型由最初的线性化模型、机理模型和典型的实验模型到目前成为研究热点的智能模型,讨论了几种典型的非线性模型预测控制方案,并就算法、稳定性、输出反馈和实际应用等方面指出了有待于解决的问题和将来非线性预测控制的发展方向,特别指出了智能非线性预测控制等融合技术是将来研究NMPC的一个重要的发展趋势.     

流程工业预测控制技术概述

对预测控的研究作了简要概述.对预测控制的发展历史作了简要回顾.阐述了预测控制与自适应和内模理论的结合方面的研究.对于非线性系统的预测控制,从预测模型的角度,描述了关于预测控制方法的研究情况.并且讨论了预测控制与其它智能控制策略结合的研究.最后指出了一些预测控制研究中的问题,以及预测控制研究的方向.     

分数阶控制研究综述

作为控制科学与工程中一个新的研究领域,分数阶控制的研究愈来愈被关注.简要介绍了分数阶控制的数学背景和基本知识,对分数阶控制理论及应用(分数阶系统模型、系统分析、分数阶控制器、非线性分数阶系统、系统辨识)的研究作了总结、评述和展望.     

非线性模型预测控制技术的研究现状与发展趋势探讨

分析了当前的非线性模型预测控制（Nonlinear Model Predictive Control,NMPC）技术和应用现状,并为今后的研究和发展提出了一些课题。给出了NMPC的主要原理,并概述了NMPC的关键优点/不足及其一些理论、计算和实施方面的问题。除了关于NMPC的数学构造及其闭环稳定性的基本问题的一般描述,还对如NMPC的鲁棒构造问题、输出反馈问题,并对闭环系统的性能预测进行了讨论。一个NMPC算法的成功取决于最初选择的非线性模型结构的合理性,所以给出了可为一个新的NMPC算法形成潜在数学构造的一些合适的非线性模型结构的简述。总之,3个对NMPC应用的最主要障碍是：非线性模型的开发;状态估计;快速、可靠的实时控制算法的求解方案。对于未来NMPC技术的需求包括非线性模型辨识的系统方法发展;非线性估计方法;可靠的数值求解技术;以及评价NMPC应用的更好方法。     

基于多模糊模型的非线性预测控制

研究了基于多模糊模型的非线性预测控制问题 ,提出了基于多模型融合的非线性预测控制方法 .首先根据实际对象在不同运行点附近的状态建立了非线性系统的线性多模糊模型表示 ,然后给出了基于多模糊模型的预测控制原理结构框图 .非线性多模糊模型被用来作为预测模型 ,CSTR过程的仿真研究表明是一种有前景的非线性预测控制方法 .     

切换非线性系统采样控制的研究现状与进展

切换非线性系统由于其广泛的工程应用背景以及重要的理论研究价值引起各行业学者的广泛关注.近年来,随着计算机技术的快速发展,切换非线性系统采样控制问题成为研究热点.对目前切换非线性系统采样控制领域的研究现状进行综述.首先介绍了切换非线性系统的基本问题,并梳理了切换非线性系统几个常用控制方法的基本思想.然后从时间触发采样控制和事件触发采样控制两个方面对切换非线性系统采样控制的国内外研究现状进行了论述.最后进行了总结并提出切换非线性系统采样控制领域未来值得关注的研究方向.     

未知参数非线性系统的自适应广义预测控制

研究非线性系统的稳定性和跟踪优化问题,针对未知参数非线性系统的参数辨识和输出跟踪问题,给出参数自适应广义预测控制方法,为使辨识模型能实时反映被控对象特性以及输出对设定值的跟踪有较高精度.提出将非线性系统转化为受控自回归滑动平均模型,根据输入输出数据辨识模型参数.采用广义预测控制滚动优化的策略得出最优控制律,将最优控制律作用于对象实现非线性系统的优化控制以及系统输出对设定值的跟踪控制.明显克服了自适应控制对模型精度要求高的缺陷且具有在线辨识,滚动优化的特点.最后,通过仿真实例验证了方法的有效性.     

一类非线性系统非切换解析模型预测控制方法研究

针对关系度不确定非线性系统, 基于模型预测控制理论和切换解析非线性模型预测控制(Nonlinear model predictive control, NMPC) 提出了一种非切换的解析NMPC新方法. 论证了在非切换解析NMPC控制律下, 通过坐标变换可以将闭环系统分别在关系度确定和不确定的两个子空间近似为线性系统, 得出非切换解析NMPC使闭环系统稳定的必要条件. 通过仿真实验验证了非切换解析NMPC可以达到很好的响应特性, 无需切换的特征也扩大了其应用范围.     

微分改进RBF网络线性预测控制算法的研究

针对现有非线性系统辨识超调较大和预测控制计算量繁琐等问题,提出了改进的RBF神经网络线性预测控制算法.该方法通过在传统性能指标函数中增加误差微分项,以优化跟踪效果;利用辨识模型作为预测模型,对输出设定值进行线性逼近的反向优化,并实时给出优化控制量.该方法简化了传统预测控制算法,在加快寻优速度的同时,有效地抑制了超调.通过非线性系统仿真实例,验证了该方法的可行性和有效性.     

双层结构预测控制研究进展

现代工业大系统的优化控制采用递阶结构,其中以预测控制为代表的先进过程控制已经成为重要的一级.目前,主流的工业预测控制技术均采用双层结构,即包含稳态优化层和动态控制层.双层结构预测控制技术可以有效解决复杂工业过程常见的多目标优化、多变量控制的难点问题.本文简要总结了双层结构预测控制的算法,并从控制输入与被控输出稳态关系入手分析了多变量预测控制稳态解的相容性和唯一性,说明了稳态优化的重要性.针对双层结构预测控制与区间预测控制的性能比较、稳态模型的奇异性以及闭环系统动态特性等提出了一些见解,并指出了需要重点研究的主题.     

基于时延的网络控制系统稳定性分析

在20世纪90年代网络控制系统问世了,它综合了计算机技术、通信技术和控制系统理论,是一个新生的控制发展理论课题,是现代工业生产发展的风向标,基于时延的网络控制系统稳定性的分析就显得至关重要。本文简单介绍了网络控制系统的定义和网络控制系统发展现状、存在的问题,研究了基于实验补偿和预测控制的网络控制系统,针对不同控制系统的时延进行了分析。     

Operations Research Techniques in Manufacturing Planning and Control Systems

This paper investigates the possible uses for operations research (OR) models and techniques in manufacturing planning and control (MPC) systems. We discuss various MPC problems where OR can be applied and the potential inclusion of such models in computer-aided MPC systems. It is found that even though the use of OR techniques in current standard MPC packages is rather restricted, there is a potential for utilizing more OR techniques in computer-aided MPC systems, at least in theory. In practice, OR-related techniques are more often developed as stand-alone systems for decision support as a complement to the MPC system. In this paper, specific OR techniques are discussed for specific MPC problems in terms of applicability and the appropriate type of software solution; none at all, stand-alone system or integrated part of the MPC system.     

MPC: Current practice and challenges

Linear Model Predictive Control (MPC) continues to be the technology of choice for constrained, multivariable control applications in the process industry. Successful deployment of MPC requires “getting right” multiple aspects of the control problem. This includes the design of the underlying regulatory controls, design of the MPC(s), test design for model identification, model development, and dealing with nonlinearities. Approaches and techniques that are successfully applied in practice are described, including the challenges involved in ensuring a successful MPC application. Academic contributions are highlighted and suggestions provided for improving MPC.     

Multi‐primary‐color LCD: Its characteristics and extended applications

Abstract— The development of multi‐primary‐color (MPC) display systems is one of the big paradigm shifts in recent display technologies and induces new potentials of display devices. The development of MPC display systems for different goals is briefly reviewed. Especially, by employing MPC systems, it is possible to reproduce the real material colors faithfully and efficiently. For signal processing, MPC systems have a big advantage in the so‐called color‐reproduction redundancy. A number of applications can be derived from this characteristic, such as improving the viewing‐angle dependency issue and power savings. On the other hand, MPC systems have a typical trade‐off versus RGB‐standardized input signals, especially for reproducing bright green. New algorithms to moderate this trade‐off on MPC systems by employing color‐reproduction redundancy are proposed. The goal of our algorithms is to maintain the compatibility with RGB‐based input signals though the initial display design so that the characteristics of MPC systems are not changed or lost. These algorithms indicate that MPC display systems are applicable not only for a specifically limited objective but also for other applications, e.g., TV broadcasting.     

A framework for hybrid model predictive control in mineral processing

Model Predictive Control (MPC) is an advanced technique for process control that has seen a significant and widespread increase in its use in the process industry since its introduction. In mineral processing, in particular, several applications of conventional MPC can be found for the individual processes of crushing, grinding, flotation, thickening, agglomeration, and smelting with varying degrees of success depending on the variables involved and the control objectives. Given the complexity of the processes normally found in mineral processing, there is also great interest in the design and development of advanced control techniques which aim to deal with situations that conventional controllers are unable to do. In this aspect, Hybrid MPC enables the representation of systems, incorporating logical variables, rules, and continuous dynamics. This paper firstly presents a framework for modeling and representation of hybrid systems, and the design and development of hybrid predictive controllers. Additionally, two application examples in mineral processing are presented. Results through simulation show that the control schemes developed under this framework exhibit a better performance when compared with conventional expert or MPC controllers, while providing a highly systematized methodology for the analysis, design, and development of hybrid MPC controllers.     

Advanced model predictive control framework for autonomous intelligent mechatronic systems: A tutorial overview and perspectives

This paper presents a review on the development and application of model predictive control (MPC) for autonomous intelligent mechatronic systems (AIMS). Starting from the conceptual analysis of “mechatronics”, we analyze the characteristics and control system design requirements of AIMS. In order to fulfill the design requirements, we propose to develop a unified MPC framework for AIMS. The main MPC schemes, covering MPC basics, robust MPC, distributed MPC, Lyapunov-based MPC, event-based MPC, network-based MPC, switched MPC, fast MPC, are reviewed with an attempt to document some of the key achievements in the past decades. Furthermore, we provide the review and analysis of MPC applications to three types of mechatronic systems, including unmanned aerial vehicles (UAVs), autonomous marine vehicles (AMVs), and autonomous ground robots (AGRs). Some promising research directions and concluding remarks are presented.     

不确定系统的鲁棒与随机模型预测控制算法比较研究

近几十年来,不确定系统模型预测控制的理论和应用得到了飞速发展.本文简要地回顾了不确定系统中鲁棒模型预测控制和随机模型预测控制的发展历史,总结了它们的相关应用,并较为细致地分析了线性不确定系统模型预测控制的各种主要算法.通过总结各种算法的通用模型、运作方式、问题规模,以及它们保证递归可行性、稳定性的方法,分析了部分算法可行域间的关系,揭示了各种算法的主要特点、适用场合和未来可发展方向,并通过仿真实例直观地分析了各种算法的性能和可靠性.     

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.     

Model Predictive Control of water resources systems: A review and research agenda

Model Predictive Control (MPC) has recently gained increasing interest in the adaptive management of water resources systems due to its capability of incorporating disturbance forecasts into real-time optimal control problems. Yet, related literature is scattered with heterogeneous applications, case-specific problem settings, and results that are hardly generalized and transferable across systems. Here, we systematically review 149 peer-reviewed journal articles published over the last 25 years on MPC applied to water reservoirs, open channels, and urban water networks to identify common trends and open challenges in research and practice. The three water systems we consider are inter-connected, multi-purpose and multi-scale dynamical systems affected by multiple hydro-climatic uncertainties and evolving socioeconomic factors. Our review first identifies four main challenges currently limiting most MPC applications in the water domain: (i) lack of systematic benchmarking of MPC with respect to other control methods; (ii) lack of assessment of the impact of uncertainties on the model-based control; (iii) limited analysis of the impact of diverse forecast types, resolutions, and prediction horizons; (iv) under-consideration of the multi-objective nature of most water resources systems. We then argue that future MPC applications in water resources systems should focus on addressing these four challenges as key priorities for future developments.