转台伺服控制系统的PID—模糊复合控制算法的研究

本文对转台伺服控制系统的控制算法进行了研究,对PID控制算法和模糊控制算法的原理进行了简单的叙述和对比,提出了基于这两种控制算法的复合控制算法,此算法是基于前两种控制算法的各自优点,依据转台在工作状态的各个阶段速度和位置偏差的不同,分别采用不同的控制算法,使转台具有更好的动、静态性能,更好的模拟飞行器的飞行状态。     

基于BP神经网络的三轴转台偏航控制

三轴飞行仿真转台系统作为一种非线性、强耦合系统,难以用精确的数学模型进行描述。文中以三轴飞行转台系统中的回转转台伺服控制系统为研究对象,在分析了回转转台的驱动机构一直流无刷电机的数学模型的基础上,对常规PID控制算法和BP神经网络PID控制算法进行了比较分析。对BP神经网络算法和计算流程进行了简介。同时对阶跃和正弦信号进行MATLAB仿真。仿真结果表明,该方法可以实现有效的控制,并且与传统PID算法相比,具有更好的自适应性和鲁棒性。     

基于混沌粒子群优化的约束状态反馈预测控制算法

提出一种基于混沌粒子群优化的约束状态反馈预测控制算法,用于解决带有输入约束和状态约束的控制问题.将混沌粒子群优化引入到约束状态反馈预测控制的滚动优化过程中,增强了算法在约束范围内的局部搜索和全局搜索能力.通过对一个实际的带有约束的线性离散系统控制优化问题的解决,验证了基于混沌粒子群优化的状态反馈预测控制算法的可行性和有效性,与传统的二次规划算法的比较结果说明了此算法的优越性,证明了状态反馈预测控制系统良好的鲁棒性.     

程控双轴姿态转台伺服控制系统设计及应用

本文针对一种双轴姿态转台,采用了一种新型的稳态跟踪控制方法。由于存在摩擦、齿隙和机械变形等扰动因子,使其具有非线性特性,因此,采用滑模变结构控制算法可以很好地解决非线性问题。本文主要针对双轴转台的稳定性与控制问题进行了研究,首先,利用机械模型,建立了双轴转台的单轴数学模型,并利用Lu Gre模型建立了摩擦力干扰的数学模型,通过对两个转台的坐标转换,得出了两个转台的数学模型;其次,根据双轴姿态转台的结构特点,研制了一套具有较好抗磨性的上位机实时控制系统,该算法具有较好的鲁棒性,从而验证了所提算法的正确性。     

基于时域的PID模型预测控制算法改进及仿真

在分析模型算法控制(MAC)算法和PID算法的基础上,针对两种算法的不足,结合两种算法的优点,推导了基于PID性能指标的改进的MAC控制算法PIDMAC算法。这种算法既具有PID算法的优点,结构简单,参数调节方便,又具有预测功能。通过仿真研究的对比,在时域内分析了PIDMAC控制器的参数选择对系统控制性能的影响,说明了PIDMAC算法比基本MAC算法具有更好的控制性能,为工程应用打下较好的理论基础。     

无线传感器网络中故障容忍算法研究

针对无线传感器维护困难,改善故障容忍能力的问题,提出了一种基于故障容忍度的自适应容错拓扑控制算法。首先,阐述了节点故障容忍度衡量指标,其次,详细分析了算法的各个阶段,最后通过仿真实验与常用算法对比,结果表明,基于故障容忍度的自适应容错拓扑控制算法具有更好的控制效果,极大地提高了使用效率。     

Internet拥塞控制系统在不同源控制算法作用下的资源竞争分析

在未来的Internet拥塞控制协议中,不同的用户群根据不同的QoS需求,可以实现不同的控制算法.系统地研究了拥塞控制系统在AIMD和MIMD两类源算法共同作用下的稳态和动态特性,这些特性揭示了配置不同源控制算法的用户群对网络资源的竞争.内容包括AIMD,MIMD算法共同作用下的系统模型、系统稳态分析、系统稳定性分析等.基于NS2系统的仿真结果,证实了给出的分析方法的有效性,并揭示了不同源算法对网络资源的竞争情况.     

一种基于dsPIC30F控制器的高精度实时控制算法

针对汇编程序控制算法精度对大幅度变化输入信号适应性差、运算实时性差的问题,提出了一种通用控制模型迭代算法和累加器归一化处理算法,基于dsPIC30F控制器将算法编制成通用、高效汇编子程序。在硬件平台上采用二阶滤波和微分控制环节对算法进行验证,实验表明了该算法的有效性。     

基于修正后拟牛顿算法的织物实时模拟

选用质点-弹簧模型来描述整个织物运动系统,提出了两种新的基于拟牛顿算法的方法用于织物的实时模拟,同时给出了一种开关算法,根据实时状态对两种方法进行自动选择,以保证更好的收敛速度.对新算法的收敛速度进行了理论上的证明和讨论,并将修正后拟牛顿算法的实验数据和结果与已有的研究成果作了深入的比较和分析,结果表明修正后拟牛顿算法无论在收敛速度还是整体性能上都比已有算法优越.     

控制量离散的轮式小车轨迹跟踪研究

针对机器人小车控制过程中的轨迹跟踪问题,以控制量为离散值的轮式小车为研究对象,提出一种新的预测控制算法.建立小车在离散状态空间下的运动学模型,并根据此模型设计预测控制算法,以克服实际过程中的不确定性.然后,为解决传统预测控制算法在应用上出现的计算量指数增长问题,基于改进模拟退火的快速寻优算法,设计一种新的预测控制策略,以同时保证小车轨迹跟踪的精确性与实时性.通过仿真实验给出了该算法下小车对不同轨迹的跟踪情况及鲁棒性测试,在与传统预测控制算法计算量的比较结果中表明,该算法能够减少计算时间且实现对轨迹有效地跟踪,并保证较高的稳定性,同时,该算法可以推广到各类控制量为离散值的预测控制问题.     

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.