自抗扰控制器在超机动飞行快回路控制中的应用

提出了一种利用自抗扰控制器算法设计超机动飞行快回路控制律的新方法．根据自抗扰控制器能够动态补偿系统模型扰动和外扰的特性,在超机动飞行的快回路中引入自抗扰控制器。实现了快变量的动态解耦控制．控制律设计直接依据超机动飞行的强耦合、强非线性模型。在较大的包线范围内不需要改变控制器参数．简化了设计过程．仿真结果表明,系统具有良好的动态和稳态性能,控制器具有很强的鲁棒性．     

自适应模糊自抗扰控制器的研究与设计

针对普通PID在实际工程控制中参数整定难,抗干扰能力差的问题,设计了一种基于模糊控制原理的改进型自抗扰控制器.该控制器充分结合了模糊控制器和自抗扰控制器的各自优势,并对其进行了随动系统的仿真.仿真结果表明,该控制器可设计成为一个响应速度快、静差小的控制系统,与经典PID控制器在同样的系统中比较,自适应模糊自抗扰控制有较好的控制性能.     

飞机俯仰运动自抗扰控制器设计

提出了利用自抗扰控制器在大包线范围内设计飞机俯仰运动控制器的新方法．利用二阶自抗扰控制器补偿系统模型扰动和外扰，实现了纵向运动俯仰角变量的跟踪控制．自抗扰控制器直接依据飞机的非线性模型，符合飞机动力学模型摄动大的特点，在很大的包线范围内不需要改变控制器的结构和参数，简化了飞行控制律的设计过程．大包线范围内的仿真结果表明，系统具有良好的动态和稳态性能，控制器具有很强的鲁棒性，为解决大包线范围内的飞行控制问题提供了一种有效的新途径．     

二阶自抗扰控制器的参数简化

为了克服PID控制器自身具有的缺陷，在PID的基础上提出了自抗扰控制器（ADRC）。该控制器由跟踪微分器、扩张状态观测器和非线性状态误差反馈三部分组成，其控制效果优于经典PID控制器，但是参数众多、调节复杂。通过线性简化和参数整合建立简化的线性自抗扰控制器。MATLAB仿真表明，简化的线性自抗扰控制器参数调节过程大大简化，而控制性能并未受到明显影响。     

抄纸过程水分定量自抗扰控制仿真研究

针对抄纸过程水分定量系统控制中的强耦合、时滞、不确定等特点,在常规PID控制的基础上提出了自抗扰控制这种新的法,方法的特点是可以动态补偿系统模型扰动和外扰,即克服了常规PID控制中存在的快速性与超调之间的矛盾.自抗扰控制器由微分一跟踪器、扩张状态观测器和非线性状态误差反馈三部分组成.最后对自抗扰控制和PID控制算法分别进行了仿真.并分析比较了这两种不同控制算法的仿真效果,结果表明自抗扰控制系统具有良好的动态性能,控制器具有很强的鲁棒性.     

基于非线性自抗扰控制器的PMSM直接转矩控制

针对传统的永磁同步电机(PMSM)直接转矩控制中转矩脉动和磁链脉动较大及转速超调等问题,研究一种基于非线性自抗扰控制的PMSM直接转矩控制策略.将传统的PI控制器替换成非线性的自抗扰控制器,设计转速环自抗扰控制器.自抗扰控制器中的扩张状态观测器将外部扰动和未知系统的参数的变化进行估计,并通过补偿手段加以控制,提高系统的抗干扰性能.微分跟踪器将给定转速平滑化,使得系统快速跟踪给定的转速信号,提高系统的响应能力.仿真实验验证了该策略的可靠性和有效性.     

测试转台的输出改进自抗扰控制器设计

当电机存在低频大力矩扰动时,使用经典方法控制测试转台很难达到期望的测量精度,为了有效抑制扰动达到期望精度,提出一种输出改进的自抗扰控制器设计方法。在控制的起始阶段采用普通自抗扰控制器,系统进入稳态后引入非线性饱和环节限定自抗扰控制器的输出幅值。仿真结果表明,系统既具有良好的动态特性,又有很好的抗扰性,达到了较好的输出精度。最后与经典控制方法相比较进一步验证了其良好的控制效果。     

自抗扰PID四旋翼飞行器控制方法研究

针对传统PID控制算法不能很好地适应非线性被控系统、鲁棒性较弱、抗扰能力差等缺点,提出了一种基于传统PID控制与自抗扰控制结合的四旋翼飞行器控制方法。在传统PID控制器的基础上,对飞行器姿态解算过程中的不确定因素和外界干扰予以实时的观测和补偿。最后在Simulink中分别搭建传统串级PID控制器和自抗扰PID控制器的仿真模型,通过分析仿真结果得出自抗扰PID控制器的响应时间比传统串级PID控制器快约30%,稳态误差较传统串级PID控制器降低约15%,超调量降低约20%。由此得出自抗扰PID四旋翼飞行器控制方法能够很好地适应四旋翼飞行器非线性系统,达到抑制外界干扰以及补偿系统控制误差的效果。     

自抗扰控制算法在聚丙烯反应釜过程控制中的应用

本文介绍一种新型实用控制器-自抗扰控制器在聚丙烯反应釜过程控制中的成功应用.在聚丙烯反应釜过程控制中升温过程是控制的难点.自抗扰控制器解决了在升温过程中反应釜夹套的加热水和冷却水的自动切换,从而成功地实现了升温过程的自动控制.而恒压过程的控制在稳定性和准确性等方面都超过了原来的PID的控制效果.     

高超声速飞行器再入姿态改进自抗扰控制

针对高超声速飞行器在无动力再入过程中具有复杂非线性、控制通道间强耦合及气动参数不确定性,增加了控制器设计的困难.通过构造连续光滑扩张状态观测器及自抗扰解耦控制技术,设计了高超声速飞行器自抗扰姿态控制器.采用构造qin函数实现了连续光滑扩展状态观测器的设计,可避免自抗扰控制器应用过程中的高频颤振现象.通过自抗扰解耦控制技术设计了姿态角及姿态角速度联合控制器,无需基于奇异摄动理论分为内外环控制,解决了设计飞行器内外环控制器时需忽略内环对外环的耦合影响问题,并且解决了难于获取精确的飞行器被控模型及精确的气动参数、摄动界限等问题.仿真结果表明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.