间接型的混杂模型参考自适应控制

针对相对阶为1的理想系统,本文考虑了具有混杂自适应律的间接型模型参考自适应控制问题.通过建立系统和控制器的离散参数估计和它们的插值四者之间关系的性质,严格地分析了闭环系统的稳定性,证明了闭环系统中所有的信号都一致有界,并且跟踪误差渐进收敛于零.     

带扰动模型失配史密斯预估器的自适应控制

在扰动情况下,针对传统史密斯预估器的模型失配问题,提出了一种基于模型参考自适应控制的史密斯预估器。首先,在控制对象输入端添加前馈增益矩阵和反馈补偿矩阵,和控制对象相互结合,通过调整矩阵参数,使控制对象和预估模型匹配,消除系统的模型失配误差。其次,在前向控制器输出端引入扰动补偿矩阵,调整扰动补偿矩阵的相关参数,对系统的扰动进行补偿。最后,选取合适的李雅普诺夫函数,求取自适应率。利用MATLAB中的SIMULINK模块进行仿真,仿真结果验证了方法的有效性。     

自适应容错控制的发展与展望

自适应容错控制出现了一些新进展及其代表性工作.首先,给出自适应容错控制的内涵;然后,将其分为四大类:基于故障参数估计的自适应容错控制,基于近似模型的自适应容错控制,基于多模切换与校正的自适应容错控制和直接自适应容错控制,重点论述了模型参考自适应容错控制;最后,提出了一些具有挑战性的问题.     

基于电阻估计的PMSM模型参考自适应系统

在无速度传感器交流伺服系统中, 估计转速受电机定子电阻不确定性影响较大, 本文针对该问题进行了研究, 提出了改进的MRAS方法. 利用永磁同步电机的电流模型作为可调模型, 设计了MRAS的自适应律, 在传统方法的基础上加入了对定子电阻的估计, 并利用控制方法的特点对估计算法进行了简化. 最后, 在Matlab/Simulink环境下对改进方案进行了仿真研究, 结果表明该算法使得转速的估计对电阻的不确定性有较强的鲁棒性, 并且使电机能够在较宽的速度范围内正常运行.     

稳定的多级自适应控制器设计

本文设计了一种电液伺服系统的多级自适应控制器，用正实的概念，给出了控制器的设计程序。并用仿真方法对这种控制器进行了研究。     

模型参考自适应控制,模糊控制及PID控制的综合应用

模型参考自适应控制、模糊控制及ＰＩＤ控制的综合应用ＴｈｅＣｏｍｐｒｅｈｅｎｓｉｖｅＡｐｐｌｉｃａｔｉｏｎｏｆＭＲＡＣ、ＦｕｚｙａｎｄＰＩＤＣｏｎｔｒｏｌ●陈玉东刘红波ＣｈｅｎＹｕｄｏｎｇＬｉｕＨｏｎｇｂｏ１概述ＰＩＤ控制由于算法简单、可靠性高、稳定性...     

基于模型参考自适应的速度估计方法

为解决无速度传感器感应电动机矢量控制系统的速度估计问题,以模型参考自适应的理论为基础,利用感应电压矢量进行转速估计并进行仿真,转速估计结果理想,仿真实验所得到的波形与理论分析结果是一致的。并且使相应的无速度传感器矢量控制系统具有良好的静、动态性能。证明了采用模型参考自适应系统,利用感应电压矢量进行转速估计方法的正确性和可行性。     

Adaptive process control using pattern-based performance feedback

Two adaptive control strategies using pattern-based performance feedback are studied. Both strategies are based on an analysis of patterns exhibited in the recent history of the controller error and focus on adaptation opportunities that arise as a result of sustained set-point changes. The strategies consider a two-parameter adaptation where model gain and time constant are updated after each pattern analysis. The first method uses a vector quantizing neural network for the pattern analysis task. The network is constructed using exemplar controller error patterns developed by deliberately mismatching parameters of a PI controller's internal model against those of an ‘ideal’ simulated system. Once on-line, the network then receives controller error response patterns from the actual application and outputs controller model parameter updates. The second method uses a rule-based pattern analysis to determine features of the controller error response pattern. These pattern features are evaluated relative to user-specified desired features, and rules and procedures again produce PI controller model parameter updates. Both methods are compared in setpoint tracking demonstrations to determine qualitative robustness for non-ideal situations such as measurement noise, constraint of the manipulated variable, model order mismatch and unmeasured oscillatory disturbances. Results reveal that rule-based feature analysis has the benefit of being time independent but the disadvantage of not being able to handle non-ideal situations without a cumbersome rule base. Although the neural network approach requires specification of a time window for pattern analysis, the method proves to be easier to implement and more robust when confronted with non-ideal operating situations.     

基于模型参考自适应方法的对偶控制方法研究

针对未知参数随机系统，基于模型参考自适应方法和双准则函数，设计了具有控制和学习作用的对偶控制器．通过控制器的学习作用提高了控制精度，有效地减少了启动时的超调．仿真结果表明，设计的控制器能够具有较好的跟踪性能．     

逆变电源的自适应重复控制方案

克服逆变电源系统参数、负载变化和电源死区效应的影响,设计了一种自适应重复控制方案。自适应方案采用跟踪参考序列的模型参考自适应控制设计方法。不需线性补偿器,既简化了设计,又减少了计算量,适合快速跟踪,并适应系统参数和负载的变化。重复控制作为补偿器,补偿死区效应的影响。     

基于MIT MRAC的Buck变换器电压控制仿真研究

为改善Buck变换器的控制性能,对基于模型参考自适应控制的Buck变换器电压控制方案进行了研究．首先基于小信号扰动法建立了Buck变换器的小信号解析模型;然后分析给出了自适应参考模型的设计依据,总结了设计步骤,并对自适应控制器的设计方法、关键参数的取值和影响进行了归纳;最后在Matlab/Simulink平台下搭建了频域和时域仿真模型,对文中重要结论进行了验证,证实了MIT MRAC用于Buck变换器控制的可行性和有效性．     

新的e修正鲁棒自适应控制方法在航空发动机上的应用

针对航空发动机模型的结构和参数的不确定性,提出一种基于非李亚普诺夫方程(NQLF)的e修正鲁棒模型参考自适应控制法。该方法无需知道发动机精确数学模型,抗干扰能力强,在输出误差信号的作用下具有比普通方法更快的收敛性,振荡性能够得到很好的控制。从仿真结果可见控制器能使系统比较好地跟踪参考模型的输出,对发动机模型参数和结构的变化有一定的适应能力,鲁棒性较好。     

四旋翼无人机飞行姿态模型参考自适应控制

四旋翼无人机具有机械结构简单、可垂直起降等优势,在各个领域都有极广泛的应用.经典控制算法如PID对无人机在受扰动或环境变化时的控制效果不理想,难以调节至平衡状态.论文对动力学建模加以适当简化,设计了自适应控制律,数值仿真实验结果表明,模型参考自适应控制能在很大范围内有效屏蔽干扰,验证该算法是有效的.     

利用输入输出测量值设计控制系统的新方法

针对自适应控制的实际应用情况 ,在已有自适应模型的基础上 ,通过严密的数学推导 ,提出一种设计 MRAC系统的新算法。该算法不必引入增广误差信号而得到自适应控制律 ,简单而又实用 ,并且便于工程实现。仿真结果验证了所提出方法的有效性。     

Neural network modelling and control strategies for a pH process

The control of a pH process using neural networks is examined. The neural network as a universal approximator is used to good effect in this nonlinear problem, as is shown in the simulation results. In the modelling task, the dynamics of the process was carefully examined to determine a suitable structure for the net. In particular, a multilayer net consisting of two single hidden layers was constructed to reflect the Wiener model of the pH process. This led to much simpler training compared to similar modelling attempts by other researchers. For the control task, two schemes were simulated. In one approach, a net was used to deal with the static nonlinearity to achieve control over a wide working range. The dynamic controller used was the PID, with its parameters tuned on a relay auto-tuner. This control design was compared with the strong acid equivalent method. In the second approach, a direct model reference adaptive neural network control scheme was proposed. The training procedure uses the more efficient least squares algorithm developed by Loh and Fong.     

Discrete-time minimal control synthesis adaptive algorithm

This article proposes a discrete-time Minimal Control Synthesis (MCS) algorithm for a class of single-input single-output discrete-time systems written in controllable canonical form. As it happens with the continuous-time MCS strategy, the algorithm arises from the family of hyperstability-based discrete-time model reference adaptive controllers introduced in (Landau, Y. (1979), Adaptive Control: The Model Reference Approach, New York: Marcel Dekker, Inc.) and is able to ensure tracking of the states of a given reference model with minimal knowledge about the plant. The control design shows robustness to parameter uncertainties, slow parameter variation and matched disturbances. Furthermore, it is proved that the proposed discrete-time MCS algorithm can be used to control discretised continuous-time plants with the same performance features. Contrary to previous discrete-time implementations of the continuous-time MCS algorithm, here a formal proof of asymptotic stability is given for generic n-dimensional plants in controllable canonical form. The theoretical approach is validated by means of simulation results.