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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. 相似文献
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为解决迭代学习过程中的任意迭代初值和迭代收敛理论证明难的问题,本文构造了一种轨迹跟踪误差初值恒位于滑模面内的时变终端滑模面,将轨迹跟踪误差初值不为零的轨迹跟踪控制问题转换为滑模面初值恒为零的滑模面跟踪控制问题,建立了任意迭代初值与相同迭代初值的迭代学习控制理论连接桥梁.本文提出一种基于时变滑模面的比例–积分–微分(PID)型闭环迭代学习控制策略,基于压缩映射原理证明了迭代学习的收敛性,给出了迭代收敛条件.时变终端滑模面经有限次迭代学习收敛到零,达到轨迹跟踪误差最终稳定在时变滑模面内的目的;Lyapunov稳定理论证明了位于滑模面内的轨迹跟踪误差在有限时间内收敛到原点,达到轨迹局部精确跟踪目的.随机初态下的工业机器人轨迹跟踪控制数值仿真验证了本文方法的有效性和系统对外部强干扰的鲁棒性. 相似文献
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间歇过程最优迭代学习控制的发展:从基于模型到数据驱动 总被引:1,自引:0,他引:1
本文综述了间歇过程的基于模型的和数据驱动的最优迭代学习控制方法.基于模型的最优迭代学习控制方法需要已知被控对象精确的线性模型,其研究较为成熟和完善,有着系统的设计方法和分析工具.数据驱动的最优迭代学习控制系统设计和分析的关键是非线性重复系统的迭代动态线性化.本文简要综述了基于模型的最优迭代学习控制的研究进展,详细回顾了数据驱动的迭代动态线性化方法,包括其详细的推导过程和突出的特点.回顾和讨论了广义的数据驱动最优迭代学习控制方法,包括完整轨迹跟踪的数据驱动最优迭代学习控制方法,提出和讨论了多中间点跟踪的数据驱动最优点到点迭代学习控制方法,和终端输出跟踪的数据驱动最优终端迭代学习控制方法.进一步,迭代学习控制研究中的关键问题,如随机迭代变化初始条件、迭代变化参考轨迹、输入输出约束、高阶学习控制律、计算复杂性等.本文突出强调了基于模型的和数据驱动的最优迭代学习控制方法各自的特点与区别联系,以方便读者理解.最后,本文提出数据驱动的迭代学习控制方法已成为越来越复杂间歇过程控制发展的未来方向,一些开放的具有挑战性的问题还有待于进一步研究. 相似文献
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迭代学习控制理论的发展动态 总被引:4,自引:0,他引:4
迭代学习控制(ILC)适合于具有某种重复运动(运行)性质的被控对象,可
实现有限时间区间上的完全跟踪任务.本文综述了迭代学习控制的基本内容和最新发展动态
,对迭代学习控制的基本理论进行了分类研究,并讨论其存在的问题和发展趋势. 相似文献
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In this paper, we formulate and explore the characteristics of iterative learning in ballistic control problems. The iterative learning control (ILC) theory provides a suitable framework for derivations and analysis of ballistic control under learning process. To overcome the obstacles caused by uncertain gradient and redundant control input, we incorporate extra trials into iterative learning. With the help of trial results, proper control and updating direction can be determined. Then, iterative learning can be applied to ballistic control problem. Several initial state learning algorithms are studied for initial speed control, force control, as well as combined speed and angle control. In the end, shooting angle learning in the basketball shot process is simulated to verify the effectiveness of iterative learning methods in ballistic control problems. 相似文献
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Jose Manuel Lopez‐Guede Borja Fernandez‐Gauna Manuel Graña Ekaitz Zulueta 《Computational Intelligence》2015,31(3):498-512
Multiagent systems are increasingly present in computational environments. However, the problem of agent design or control is an open research field. Reinforcement learning approaches offer solutions that allow autonomous learning with minimal supervision. The Q‐learning algorithm is a model‐free reinforcement learning solution that has proven its usefulness in single‐agent domains; however, it suffers from dimensionality curse when applied to multiagent systems. In this article, we discuss two approaches, namely TRQ‐learning and distributed Q‐learning, that overcome the limitations of Q‐learning offering feasible solutions. We test these approaches in two separate domains. The first is the control of a hose by a team of robots. The second is the trash disposal problem. Computational results show the effectiveness of Q‐learning solutions to multiagent systems’ control. 相似文献
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论述了迭代学习控制的基本理论问题,着重讨论了迭代学习理论研究的现状及其存在的问题,提出了一些有待进一步研究的方 面。 相似文献
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非线性非仿射离散时间系统的两阶段最优迭代学习控制 总被引:3,自引:0,他引:3
On the basis of a new dynamic linearization technology along the iteration axis,a dual-stage optimal iterative learning control is presented for nonlinear and non-affine discrete-time systems.Dual-stage indicates that two optimal learning stages are designed respectively to improve control input sequence and the learning gain iteratively.The main feature is that the controller design and convergence analysis only depend on the I/O data of the dynamical system.In other words,we can easily select the control parameters without knowing any other knowledge of the system.Simulation study illustrates the geometrical convergence of the presented method along the iteration axis,in which an example of freeway traffic iterative learning control is noteworthy for its intrinsic engineering importance. 相似文献
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Jian-Xin Xu 《Automatica》1997,33(12):2191-2195
In this paper, we propose a new concept—direct learning which is defined as the generation of the desired control input profile directly from existing control input profiles without any repeated learning. The motivation of developing direct learning control schemes is to overcome the limitation of conventional learning control methods which require that the desired tracking patterns (trajectories) be strictly repeatable throughout the learning process. The main advantages of the direct learning are (1) the capability of fully utilizing the preobtained control input signals which may correspond to tracking patterns with different magnitude scales and be achieved through various control approaches; (2) direct generation of the desired control input profile without repeating the operation cycles. The focus of this paper is on direct learning for a class of trajectories which have identical operation periods but are different in magnitude scales. 相似文献
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Hideharu Sawada Ji-Sun Shin Fumihiro Shoji Hee-Hyol Lee 《Artificial Life and Robotics》2008,13(1):139-143
PID control has widely used in the field of process control and a lot of methods have been used to design PID parameters.
When the characteristic values of a controlled object are changed due to a change over the years or disturbance, the skilled
operators observe the feature of the controlled responses and adjust the PID parameters using their knowledge and know-how,
and a lot of labors are required to do it. In this research, we design a learning type PID control system using the stochastic
automaton with learning function, namely learning automaton, which can autonomously adjust the control parameters updating
the state transition probability using relative amount of controlled error. We show the effectiveness of the proposed learning
type PID control system by simulations.
This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January
31–February 2, 2008 相似文献