基于二次稳定的仿真转台鲁棒控制器设计

针对摩擦力矩干扰,系统参数摄动等非线性扰动环节对仿真转台控制系统的影响,提出了基于二次稳定理论的仿真转台鲁棒控制器设计思想。分析了二次稳定理论的基本原理及设计方法,给出了某型飞行仿真转台基于二次稳定的状态反馈控制器和PID鲁棒控制器设计实例。仿真结果表明,基于二次稳定理论的控制器克服了摩擦力矩干扰以及转动惯量摄动对仿真转台的影响,实现了转台转角的精确控制,动态指标优越,满足了高精度飞行仿真转台系统的鲁棒控制要求。     

关于射频目标跟踪半实物仿真系统设计北大核心CSCD

在雷达精确制导中,需要通过精确控制自动地把导弹引向目标。在研发测试过程中一般使用射频目标跟踪半实物仿真系统,通过控制天线阵馈电方案模拟目标位置,使三轴模拟转台控制导引头指向目标位置来测量精度。但是磨损等原因使得三轴模拟转台产生的机械误差和在天线阵设计过程中产生的计算误差都会对测试精度产生影响。根据以上提出了估算转台机械误差和天线阵计算误差对于制导系统精度影响的方法。转台误差的影响分析利用了多种坐标系转换,在计算误差的分析过程中使用了误差圆的概念,给出了计算误差对制导系统精度影响的分析方法。仿真结果表明,上述方法能够对使半实物仿真系统的测试精度进行较好优化,最终提高雷达制导精度。     

基于惯量辨识的测试转台自适应内模控制

为了降低系统模型参数变化对测试转台控制系统的影响,提出了基于惯量辨识技术的自适应内模控制方案.首先建立了某型号测试转台数学模型,设计了测试转台内模控制器,在此基础上采用基于扰动转矩观测器的惯量辨识算法来辨识测试转台转动惯量的变化情况,然后通过模糊控制器根据转动惯量的变化自动调整内模控制器的参数,从而确保了控制器的控制性能.仿真和实验结果表明,该控制方案对惯量的变化有着很强的自适应性,提高了测试转台控制系统的抗干扰性能和系统的鲁棒性,取得了较好的控制效果.     

FT型三轴飞行模拟转台的设计与实现

介绍一种最近研制成功的三轴飞行模拟转台的设计技术和测试结果，由于采用了微型计算机直接控制，同时引入智能控制器，从而显著地提高了转台系统性能，特别是在展宽了速比范围，该转台可以满足多种类型飞机，导弹飞行控制系统地面测试和半实物仿真的要求。     

自适应反推神经网络的转台鲁棒控制器设计

针对未知非线性、外界干扰和参数摄动等不确定因素对实际转台控制系统的影响,提出了自适应反推神经网络的转台鲁棒控制器设计.首先给出自适应Backstepping控制器的设计方法及步骤,接着采用RBF神经网络对转台对象参数的不确定因素上界值加以辨识,实现转台系统的鲁棒控制.其中Backstepping鲁棒控制作为主控制器,RBF神经网络实现了不确定上界值的在线辨识.仿真结果表明,自适应Backstepping神经网络控制很好地克服了对象的不确定性,实现控制系统的较强鲁棒性,适于高精度飞行仿真转台系统的实时鲁棒控制.     

通用测试转台控制系统的设计与研究

设计并研制了一款针对弹舱测试的通用型测试转台,并针对其在测试不同负载过程中所出现的系统振荡、系统响应时间长等问题进行了深入研究。提出了一种模糊自适应PID控制方法,以误差和误差变化率ec作为输入,利用模糊规则在线对PID参数进行修正以满足不同负载下的e和ec对PID参数自整定的需求,从而消除系统振荡并提高系统响应速度。通过进行系统建模及仿真,对比不同负载下的普通PID控制与模糊自适应PID控制的控制效果。由仿真分析可知,相对于普通PID控制,模糊自适应PID控制可以及时识别到负载的变化,从而相应地调节系统的PID参数,使系统保持一定的响应速度。     

三自由度转台系统参数辨识及控制器的设计

为了提高三自由度转台的系统建模及其控制精度,首先根据三自由度转台的特点,用系统辨识的方法辨识系统的参数,建立其数学模型。然后在此基础上,设计了控制器,利用MATLAB对其进行了仿真,得到了很好的仿真效果。最后将控制器应用到实际系统,实验结果表明,设计的控制器能较好的控制转台。     

仿真转台计算机控制系统设计与实现

计算机控制系统是保证仿真转台功能和性能的重要部分,文章结合飞行仿真转台讨论了计算机控制系统的设计问题,比较了单机集中控制、多机集散控制等几种不同结构形式的转台计算机控制系统方案,并以某型仿真转台为背景,采用Dcs机制设计r计算机控制系统,对系统工程实现中的接口线路设计、采样频率选取、程序设计等一系列关键问题进行了讨论,设计结果在转台系统中应用取得了良好效果。     

七自由度测试转台的控制系统设计

对于航空飞行器等设备,在实际运行前可通过多自由度测试转台对其进行动态性能分析,以获得理想控制效果;以七自由度航空设备测试转台为研究对象,采用开放式结构形式和通用外围设备建立系统硬件平台,以VC++为开发工具设计了控制系统的软件结构;为了保证该转台的可靠性,利用OpenGL建立了三维物理模型的虚拟运动仿真系统,进行控制指令的虚拟运动仿真,发现可能的运动干涉,有效避免错误指令;经过试验验证,该系统稳定可靠,达到预期的控制效果.     

基于模型的转台系统故障检测方法

研究了飞行模拟转台的建模与故障检测问题.飞行模拟转台不仅是导弹制导回路半实物仿真闭环系统中的一个环节,也是一个反馈控制系统,转台内部参数与仿真系统中的各种参数相互影响,导致故障传递特性复杂,无法简单的根据转台参数测试判断转台运行状态.根据转台系统的跟踪特性,提出了基于模型的转台系统故障检测方法.由于传统的转台建模方法存在建模困难、精度不高的问题,因此采用系统辨识方法,通过辨识实验的设计,借助Matlab软件系统辨识工具箱进行模型的辨识,建立了转台系统三个框架的数学模型,利用相同输入条件下的数学模型输出和实际系统输出之间的残差变化,对转台运行情况进行检测,较好的解决了转台系统的故障检测问题.通过对三种转台故障的检测,验证了改进方法的有效性.     

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.