非正则线性系统的闭环P型迭代学习控制

迭代学习控制是改善具有重复运行性质过程的跟踪性能的有效方法。开环迭代学习控制学习周期长，在迭代学习的初期容易出现不稳定和高增益的现象。对非正则系统的迭代学习控制，需要采用高阶微分学习律。该文针对一类非正则线性定常连续系统，讨论了闭环P型迭代学习控制律，给出并证明了闭环P型迭代学习控制律的收敛性条件的两个定理，解决了非正则系统的P型迭代学习控制问题。仿真实例说明闭环迭代学习律的有效性和快速性。     

迭代学习控制与二维分析

本文探讨了在学习时间有限和元情形下用二维系统方法分析迭代学习控制的可能性，给出线性离散系统在开环与闭环学习中各种学习律下的稳定与收敛的充要条件，并比较了各种学习律的收敛速度，指出随着学习次数的增加，在一定条件下，Ｐ型学习与其它类型的学习算法具有相同的收敛速度。     

线性广义系统的迭代学习控制

针对线性时不变广义系统的迭代学习控制问题．利用时间加权范数性质．通过Frobenius范数给出广义系统在D型和PD型闭环学习律作用下系统的实际输出轨迹逐渐逼近理想输出轨迹的充分条件．并指出在D型闭环学习律的基础上加上P型闭环学习律不影响控制系统的收敛性．但可以改变系统的性能．仿真算例说明了该方法的有效性．     

基于稳定逆的非最小相位系统的迭代学习控制

针对迭代学习控制在非最小相位系统上应用效果差的缺点,根据最优化性能指标和非因果的稳定逆理论,提出了一种基于稳定逆的最优开闭环综合迭代学习控制,分析了学习律的收敛性并给出了此种非因果的学习律在实际应用中的运用方式.     

同步发电机励磁控制器设计

针对基于开环迭代学习控制的同步发电机励磁控制器存在稳定性差、易受干扰影响、不能完全跟踪期望轨迹的问题,设计了一种基于开闭环PID迭代学习控制与电力系统稳定器结合的同步发电机励磁控制器;在开环迭代学习控制的基础上,引入闭环反馈控制,将上一次的输入和输出误差及当前的输出误差作为当前的控制输入,从而构成开闭环PID型迭代学习控制;同时,利用电力系统稳定器提供的附加正阻尼信号来改善同步发电机励磁控制系统的电压稳定性和功角稳定性。仿真结果表明,该励磁控制器具有较强的鲁棒性,提高了系统稳定性。     

分层递阶迭代学习控制的研究与仿真

本文对分层递阶迭代学习控制算法进行了研究。研究表明,采用分层递阶迭代学习控制算法以后,控制器不仅可以用于开环稳定系统,也可以应用于开环不稳定系统。通过对人工髋关节模拟试验机位置系统的数学模型进行仿真应用后表明,无论是开环迭代学习控制,还是闭环迭代学习控制,都能使系统的输出渐近跟踪希望轨迹,但闭环学习控制效果要比开环学习控制要好。     

离散非线性系统开闭环P型迭代学习控制律及其收敛性

本文在讨论了一般开环与闭环迭代学习控制的不足后，针对一类离散非线性系统，提出了新的开闭环ＰＧ型迭代学习控制律，给出了它的收敛性证明，仿真结果表明：开闭环Ｐ型迭代律优于单纯的开环或产才环Ｐ型迭代 律。     

非线性系统开闭环PID型迭代学习控制算法的鲁棒性

针对非线性时变系统的迭代学习控制问题提出了一种开闭环PID型迭代学习控制律,并证明了系统满足收敛条件时,具有开闭环PID型迭代学习律的一类非线性时变系统在动态过程存在干扰的情况下控制算法的鲁棒性问题.分析表明,系统在状态干扰、输出干扰和初态干扰有界的情况下跟踪误差有界收敛,在所有干扰渐近重复的情况下可以完全地跟踪给定的期望轨迹.     

基于CMAC网络的迭代学习初始控制策略

针对传统迭代学习控制在面临新的环境或控制任务时学习时间长、收敛速度慢的问题,首先引入迭代学习初始控制算法,并给出了算法收敛的充分必要条件;然后,利用小脑模型连接控制网络(CMAC)与反馈PID网络进行综合,在系统的历史控制经验基础上,估计系统的期望控制输入,作为迭代学习控制器的初始控制输入,再由开闭环P型迭代学习律逐步改善控制效果,从而避免了对初始控制输入量的盲目选择,使得系统的实际输出只需较少的迭代次数就能达到跟踪的精度要求。机器人系统的仿真结果表明了该算法的可行性与有效性。     

初态学习下的迭代学习控制

提出一种新的初态学习律，以放宽常规迭代学习控制方法的初始定位条件．它允许一定的定位误差，在迭代中不需要定位在某一具体位置上，使得学习控制系统具有鲁棒收敛性．针对二阶LTI系统，给出了输入学习律及初态学习律的收敛性充分条件．依据收敛性条件，学习增益的选取需系统矩阵的估计值，但在一定建模误差下，仍能保证算法的收敛性．所提出的初态学习律本身及其收敛性条件均与输入矩阵无关．     

智能控制：从学习控制到平行控制

20世纪60年代,学习控制开启了人类探究复杂系统控制的新途径,基于人工智能技术的智能控制随之兴起.本文以智能控制为主线,阐述其由学习控制向平行控制发展的历程.本文首先介绍学习控制的基本思想,描述了智能机器的架构设计与运行机理.随着信息科技的进步,基于数据的计算智能方法随之出现.对此,本文进一步简述了基于计算智能的学习控制方法,并以自适应动态规划方法为切入点分析非线性动态系统自学习优化问题的求解过程.最后,针对工程复杂性与社会复杂性互相耦合的复杂系统控制问题,阐述了基于平行控制的学习与优化方法求解思路,分析其在求解复杂系统优化控制问题方面的优势.智能控制思想经历了学习控制、计算智能控制到平行控制的演化过程,可以看出平行控制是实现复杂系统知识自动化的有效方法.     

Stochastic optimal control of networked control systems with control packet dropouts

This paper considers the stochastic optimal control problem for networked control systems(NCSs)with control packet dropouts.The proportional plus up to the third-order derivative(PD3)compensation strategy is adopted to compensate for control packet dropouts at the actuator by using the past control packets stored in the buffer.Based on the strategy,a new NCS structure model with packet dropouts is provided,where the packet dropout is assumed to obey the Bernoulli random binary distribution.In terms of the given model,the stochastic optimal control law is proposed. Numerical examples illustrate the effectiveness of the results.     

Supervisory control of switching control systems

In this paper, the problem of designing a switching policy for an adaptive switching control system is formulated as a problem of supervisory control of a discrete-event system (DES). Two important problems in switching control are then addressed using the DES formulation and the theory of supervisory control under partial observation. First, it is verified whether for a given set of controllers, a switching policy satisfying a given set of constraints on the transitions among controllers exists. If so, then a minimally restrictive switching policy is designed. Next, an iterative algorithm is introduced for finding a minimal set of controllers for which a switching policy satisfying the switching constraints exists. It is shown that in the supervisory control problem considered in this paper, limitations on event observation are the factors that essentially restrict supervisory control. In other words, once observation limitations are respected, limitations on control will be automatically satisfied. This result is used to simplify the proposed iterative algorithm for finding minimal controller sets.     

From robust control to adaptive control

The paper emphasizes the interaction between robust control, identification in closed loops and adaptive control. Robust control and recent algorithms developed for plant model identification in closed loops have led to new designs of adaptive control systems. Their performances are further enhanced by the use of multiple-model adaptive control, based on switching and tuning. These developments are illustrated by their application to the control of a flexible transmission system.     

Hyperbolic optimal control and fuzzy control

In this paper, we consider a new approach to fuzzy control which entails the formulation of a novel state-space representation and a new form of optimal control problem. Basically, in this new formulation, linear functions in the conventional state-space representation and cost functional are replaced by hyperbolic functions. We give a solution for this new, infinite-time, optimal control problem, which we call hyperbolic optimal control. Furthermore, we show that the resulting optimal controller is in fact a Mamdani-type fuzzy controller with Gaussian membership functions and center of gravity defuzzification. These results enable us to investigate analytically important issues, such as stability and robustness, pertaining to fuzzy controllers as well as add a powerful theoretical framework to the field of fuzzy control     

Survey on iterative learning control,repetitive control,and run-to-run control

In this paper, three control methods—iterative learning control (ILC), repetitive control (RC), and run-to-run control (R2R)—are studied and compared. Some mathematical transformations allow ILC, RC, and R2R to be described in a uniform framework that highlights their similarities. These methods, which play an important role in controlling repetitive processes and run-based processes, are collectively referred to as learning-type control in this paper. According to the classification adopted in this paper, learning-type control has two classes—direct form and indirect form. The main ideas and designing procedures for these two patterns are introduced, separately. Approximately 400 papers related to learning-type control are categorized. Statistical analysis of the resulting data reveals some promising fields for learning-type control. Finally, a flowchart based on the unique features of the different methods is presented as a guideline for choosing an appropriate learning-type control for different problems.     

Generalized control systems,boundary control systems,and delayed control systems

We investigate the relationships between the three classes of systems mentioned in the title: we show that systems with delays in control are a special instance of boundary control systems, and a boundary control system produces a generalized control system when projected onto its (unstable) eigenspaces. We use this observation to investigate the action of feedback on the dynamical behavior of systems with boundary controls. In particular, the well-known fact that spectral controllability is necessary and sufficient for a system with delays in control to be stabilizable is derived from a general rather than from anad hoc method. This paper was written according to the programs of the GNAFA-CNR group, with the financial support of the Italian “Ministero della Pubblica Istruzione.”     

Embedded model control: reconciling modern control theory and error-based control design

The paper addresses the problem of reconciling the modern control paradigm developed by R. Kalman in the sixties of the past century, and the centenary error based design of the proportional, integrative and derivative (PID) controllers. This is done with the help of the error loop whose stability is proved to be necessary and sufficient for the close loop plant stability. The error loop is built by cascading the uncertain plant to model discrepancies (causal, parametric, initial state, neglected dynamics), which are driven by the design model output and by arbitrary bounded signals, with the control unit transfer functions. The embedded model control takes advantage of the error loop and its equations to design appropriate algorithms of the modern control theory (state predictor, control law, reference generator), which guarantee the error loop stability and performance. A simulated multivariate case study shows modeling and control design steps and the coherence of the predicted and simulated performance.