基于闭环降阶模型的pH中和过程自适应控制

针对pH中和过程,提出了基于闭环系统降阶模型的自适应PI控制器.首先利用三阶模型直接辨识高阶闭环系统,然后从闭环系统模型中提取反映闭环系统动态性能的特征量,并利用特征量建立相应的规则来整定控制器.该方法解决了传统自适应控制中难以获取精确对象模型的问题.仿真结果表明,所设计的方法实现了对pH中和过程的有效控制.     

一种新的数据驱动的非线性自适应切换控制方法

针对一类非线性离散时间动态系统, 提出了一种新的非线性自适应切换控制方法. 该方法首先把非线性项分解为前一拍可测部分与未知增量和的形式, 并充分利用被控对象的大数据信息和知识, 把非线性项前一拍可测数据与未知增量都用于控制器设计, 分别设计了线性自适应控制器, 带有非线性项前一拍可测数据补偿的非线性自适应控制器以及带有非线性项未知增量估计与补偿的非线性自适应控制器. 三个自适应控制器通过切换函数和切换规则来协调控制被控对象. 既保证了闭环系统的稳定性, 同时又提高了闭环系统的性能. 分析了闭环切换系统的稳定性和收敛性. 最后, 通过水箱液位系统的物理实验, 实验结果验证了所提算法的有效性.     

电容式微陀螺自适应闭环驱动系统的研究

对微陀螺闭环驱动系统理论进行分析讨论,结合微陀螺结构特性和自适应锁相环特点,设计一种微陀螺自适应闭环驱动系统。利用数学工具Matlab分别建立Simulink传统的自动增益控制器（AGC）方式闭环驱动系统模型和自适应闭环驱动系统模型,对其进行系统性能对比分析。分析结果表明：微陀螺自适应闭环驱动系统建立时间比传统AGC方式闭环驱动系统建立时间缩短69％,系统频率偏差仅为1Hz,频率稳定性是传统闭环驱动系统的38．46％,系统抗噪声能力优于传统闭环驱动系统。因此,采用自适应闭环驱动系统可以提高微陀螺的检测灵敏度。     

一类非线性系统基于Backstepping的自适应鲁棒神经网络控制

针对一类未知非线性系统提出了一种基于Backstepping的自适应神经网络控制方法, 放松了满足匹配条件, 要求神经网络逼近误差的边界已知等一些限制性的假设. 扩展了自适应backstepping和自适应神经控制的适用范围, 整个闭环系统表明是最终一致有界的, 跟踪误差收敛于原点的一个大小可调的邻域.     

不确定机器人的自适应神经网络控制与学习

针对具有未知动态的电驱动机器人,研究其自适应神经网络控制与学习问题.首先,设计了稳定的自适应神经网络控制器,径向基函数(RBF)神经网络被用来逼近电驱动机器人的未知闭环系统动态,并根据李雅普诺夫稳定性理论推导了神经网络权值更新律.在对回归轨迹实现跟踪控制的过程中,闭环系统内部信号的部分持续激励(PE)条件得到满足.随着PE条件的满足,设计的自适应神经网络控制器被证明在稳定的跟踪控制过程中实现了电驱动机器人未知闭环系统动态的准确逼近.接着,使用学过的知识设计了新颖的学习控制器,实现了闭环系统稳定、改进了控制性能.最后,通过数字仿真验证了所提控制方法的正确性和有效性.     

基于反推滑模的船舶动力定位有限时间控制

考虑存在未知外界干扰的船舶动力定位控制问题,提出一种基于有限时间理论的自适应反推非奇异快速终端滑模算法,并对未知干扰进行自适应估计.利用有限时间Lyapunov理论证明了设计的控制律能够保证闭环系统的状态在有限时间内收敛到平衡点附近小的邻域内.仿真结果表明,与传统渐近稳定控制律相比,设计的控制策略保证闭环系统具有更快的收敛速度及更好的稳定性和鲁棒性.此外,通过对干扰的自适应补偿,进一步降低了系统的稳态误差,增强了系统的抗干扰能力.     

分数阶鲁棒自适应控制

研究了分数阶模型参考自适应控制系统.引入了分数阶微积分的概念,利用系统的输入输出,通过构造辅助信号设计了分数阶的自适应控制器和一类新的有界干扰系统的变结构分数阶鲁棒自适应控制器.基于分数阶微积分和Lyapunov稳定性理论,证明了所设计的闭环系统的稳定性.最后,仿真实验验证了此方法的有效性.     

基于多变量特征模型的黄金分割自适应控制系统稳定性分析

主要研究了基于多变量特征模型的黄金分割自适应控制器的闭环系统稳定性.对从特定区间取值的双输入输出特征模型,通过计算离散系统的矩阵导出1范数,一个充分条件被用来证明闭环系统是指数稳定的.结果不仅表现了系统稳定性,还同时给出了一个黄金分割自适应控制器中可调参数数值的选取方法.最后对中继星的数学仿真验证了系统的稳定性.     

时变系统具有MT-滤波器的反推自适应控制

研究线性时变系统具有MT-滤波器的反推自适应控制问题。给出了具有MT-滤波器和σ修正自适应律的反推自适应控制器的设计，分析了闭环系统的稳定性和跟踪性能。同已有文献相比，难点在于误差系统的构造，并且稳定性的证明更加复杂。     

一类非线性系统的自适应鲁棒输出反馈镇定

讨论了一类不确定非线性系统的鲁棒输出反馈镇定问题,其不确定性是部分已知 的.文中所得连续自适应鲁棒输出反馈控制器确保闭环系统终极一致有界.与已有文献结果 相比,关于未知参数估计的自适应律是连续的,而且闭环系统解的存在性在通常情况下能被 保证.进而,由于输出反馈控制器和自适应律的连续性,使得自适应鲁棒输出反馈控制器在 实际控制问题中易于实现,且使系统具有良好的品质.最后,通过数值算例进一步说明该文 的设计方案是有效的.     

Model-Reference Fuzzy Adaptive Control as a Framework for Nonlinear System Control

The paper presents a general methodology of adaptive control based on fuzzy model to deal with unknown plants. The problem of parameter estimation is solved using a direct approach, i.e. the controller parameters are adapted without explicitly estimating plant parameters. Thus, very simple adaptive and control laws are obtained using Lyapunov stability criterion. The generality of the approach is substantiated by Stone-Weierstrass theorem, which indicates that any continuous function can be approximated by fuzzy basis function expansion. In the sense of adaptive control, this implies the adaptive law with fuzzified adaptive control parameters. The proposed control algorithm may be viewed as an extension of classical adaptive control for linear plants, but compared to the latter it provides higher adaptation ability and consequently better performance if the plant is nonlinear. The global stability of the control system is assured and the tracking error converges to the residual set that depends on fuzzification properties. The main advantage of the approach is simplicity that suits control engineers since wide range of industrial processes can be controlled by the proposed method. In the paper, the control of heat exchanger is performed.     

Direct adaptive control using an adaptive reference model

Direct model reference adaptive control is considered when the plant-model matching conditions are violated due to large changes in the plant or incorrect knowledge of the plant's mathematical structure. Because of the mismatch, the plant can no longer track the original reference model, but may be able to track a modified reference model that still provides satisfactory performance. The proposed approach uses a time-varying ‘adaptive’ reference model that reflects the achievable performance of the changed plant. The approach consists of direct adaptation of state feedback gains for state tracking and simultaneous estimation of the plant-model mismatch. The reference model adapts to the changed plant, and is redesigned if the estimated plant-model mismatch exceeds a bound determined via robust stability and/or performance criteria. The resulting controller offers asymptotic state tracking in the presence of plant-model mismatch as well as matched parameter deviations.     

具有降阶模型的多模型鲁棒自适应控制

传统鲁棒自适应控制由于考虑了实际系统存在的不确定性,在一定程度上扩大了常规自适应控制的应用范围,但是传统鲁棒自适应控制大多只是从系统全局稳定性的角度出发来设计控制器而忽略系统动态和稳态性能,导致其无法在工况多变的实际被控系统中取得令人满意的效果。针对传统鲁棒自适应控制的不足,本文对由ARMA模型描述并包含未建模动态的系统设计了多模型鲁棒自适应控制器。首先采用正则化技术将系统未建模动态转化为系统有界扰动,并在系统降阶模型的基础上根据系统工况的变化设计了多个固定控制器和2个鲁棒自适应控制器,并根据性能指标函数选择最佳控制器作为当前系统控制器以提高系统性能。仿真实验说明当系统存在未建模动态以及系统工况发生变化时,本文设计的控制器能获得较好的控制效果。     

Simple adaptive control of processes with time-delay

This paper is concerned with the development of tuning guidelines and robustness evaluation tools for a simple adaptive control (SAC) scheme. The adaptive technique requires knowledge of only the relative degree of the plant and an upperbound of the process gain. This is an explicit or direct adaptive scheme. The SAC method is evaluated by simulated applications to two processes. The application of SAC to a process with time-delay is also considered in this paper. This issue has both theoretical, because of the strictly positive real (SPR) requirements, as well as practical appeal. Simulation results show the practicality and usefulness of the proposed algorithm.     

Smooth indirect adaptive sliding mode control

In this paper, an indirect approach to the dual‐mode adaptive robust controller (DMARC) is proposed, which combines the typical transient and robustness properties of variable structure systems with a smooth control signal in steady state, typical of conventional adaptive controllers, as model reference adaptive controller. The aim of this indirect version, here named indirect DMARC, is to provide a more intuitive controller design, based on physical plant parameters, as resistances, inertia moments, capacitances, and so on, maintaining DMARC properties. In this paper, a stability analysis for the proposed controller and simulations to an unstable second‐order plant will be presented. Copyright © 2013 John Wiley & Sons, Ltd.     

一种具有强鲁棒性的模型参考自适应控制系统

提出一种模型参考自适应控制系统．该系统只要求知道对象阶次的上界,不要求对象严格正实和最小相位,并且允许存在建模误差和作用在对象任何部位的大幅值高频干扰．系统的强鲁棒性来源于特殊的系统结构和对信号的良好滤波．Lyapunov稳定性理论证明了系统的渐近稳定性,Matlab仿真验证了系统的强鲁棒性．     

离散时间间接型模型参考自适应控制的一种系统化的分析方法

This paper presents the design and analysis of indirect model reference adaptive control(MRAC)with normalized adaptive law for a class of discrete-time systems.The main work includes three parts.Firstly,the constructed plant parameter estimation algorithm not only possesses the same properties as those of traditional estimation algorithms but also avoids the possibility of division by zero.Secondly,by finding the relationship between the plant parameter estimate and controller parameter estimate and using the properties of plant parameter estimate,the similar properties of controller parameter estimate are also established. Thirdly,based on the relationship properties between the normalizing signal and all the signals in the closed-loop system and on some important mathematical tools on discrete-time systems,as in the continuous-time case,a systematic stability and convergence analysis approach to the discrete indirect MRAC scheme is developed rigorously.     

Adaptive Control of A Servo System Based on Multiple Models

This paper presents an alternative technology for adaptive control of a DC motor servo system based on multiple models. A dynamic mechanical model of the controlled plant is built, where the unmeasurable variables can be estimated by a filter observer. According to the mechanical model, an adaptive controller is designed. Specific attention is given to the jumping parameters in the control process, which motivate the proposition of multiple models, including fixed models, identified model, and adaptive model, to approximate the global dynamic characteristics of the plant model. A model switching rule is proposed to select the optimal model matching the plant, and the identified and adaptive models are reset when switching occurs, minimizing the effect caused by jumping parameters. Simulation results demonstrate that the introduced scheme is superior to the conventional adaptive control in that it yields a significant improvement of transient stability and response speed as well as steady accuracy, guaranteeing better low‐speed performance.     

On the convergence of a model reference adaptive control algorithm with unknown high frequency gain

A model reference adaptive control algorithm is shown not to be exponentially convergent when the high frequency gain of the plant is unknown. The implication is that such an algorithm will be lacking in some way in robustness.     

Some design guidelines for discrete-time adaptive controllers

There have been many algorithms proposed for adaptive control which will provide globally asymptotically stable controllers if some stringent conditions on the plant are met. The conditions on the plant cannot be met in practice as all plants will contain high frequency unmodeled dynamics. This paper uses a linearization analysis of a nonlinear adaptive controller to demonstrate analytically some design guidelines which alleviate some of the problems associated with adaptive control in the presence of unmodeled dynamics. The points made are further demonstrated by simulation results.