单神经元自适应PID控制器及其应用

研究了单神经元自适应PID控制器,阐述了该控制器的特点、控制律、适用对象及工程整定方法,在和利时公司的SmartPro系统平台上开发出单神经元自适应PID控制器,进行了单神经元自适应PID控制器的典型一、二阶对象闭环仿真,最终将单神经元自适应PID控制器应用于制药厂发酵罐温度控制回路中。单神经元控制器具有可调参数少、易于整定、控制输出平稳、鲁棒性强的独特优点,适用于大滞后且要求平稳控制输出的工业过程。     

非线性不确定系统的直接自适应输出反馈模糊控制

针对一类单输入单输出非线性不确定系统，基于状态观测器并结合自适应模糊系统和滑模控制，提出一种稳定的直接自适应模糊输出反馈控制算法。该算法不需要系统状态可测的条件，并能保证闭环系统稳定。仿真结果表明了该方法的有效性。     

单神经元自适应PID控制器的性能优化设计

研究了单神经元自适应PID摔制器性能优化问题,阐述了该摔制器的特点、控制律;给出了一种控制灵敏度的快速近似求取方法,实现了PID参数的在线自学习;使单神经元控制器具有可调参数少、易于整定、控制输出平稳、鲁棒件强的独特优点,适用于大滞后且要求平稳控制输出的工业过程。     

基于灵敏度的大滞后过程对象自适应PI控制器

针对较难控制的大滞后过程对象,提出一种基于灵敏度鲁棒性能指标的自适应PI控制器,使控制回路在运行过程中始终保持在最佳运行状态,最终提高工业过程设备的运行效率.利用控制回路在正常运行过程中产生的过程对象输入和输出信号,通过信号分解和傅立叶分析运算在线辨识过程对象在重要频率点上的频率响应特性,然后通过同一阶加纯滞后模型在幅值和相位两方面的匹配获得一个可以很好地描述大滞后对象的传递函数模型.基于模型计算出满足灵敏度性能指标的PI控制器参数,实现了大滞后过程对象的自适应PI控制.所提出的PI控制器的自适应过程不需要过程对象的任何先验知识,也不需要中断控制回路的正常运行.仿真实验表明模型的在线辨识精确,而且自适应PI控制可以保证系统的鲁棒性能和预期的控制性能.     

基于观测器非线性不确定系统的自适应模糊控制

针对一类单输入单输出不确定非线性系统，提出一种稳定的自适应模糊控制方法，该方法不需要系统状态可测的条件，而是通过设计模糊状态观测器来估计系统的状态，证明了所提出的控制方法不但能使闭环系统稳定，而且输出误差可取得H∞跟踪控制性能，仿真结果进一步验证了该控制算法的实用性和有效性。     

多变量非线性系统的间接模糊输出反馈自适应控制

针对一类多输入多输出非线性不确定系统，提出一种基于观测器的模糊间接自适应控制方法，并基于李亚普诺夫函数方法，导出了输出反馈控制律以及参数的自适应律，证明了整个控制方案不但能保证闭环系统稳定，而且取得了良好的跟踪控制性能。     

非线性系统的直接自适应输出反馈监督模糊控制

针对一类单输入单输出非线性不确定系统，提出一种稳定的直接自适应模糊输出反馈监督控制算法，该算法不需要系统的状态完全可测的假设条件，监督控制不仅迫使系统的状态在指定的集合内，而且当模糊自适应控制处于良好的工作状态时，监督控制可以关闭，证明了整个模糊自适应输出反馈控制算法可以保证闭环系统稳定。     

改进的非线性鲁棒自适应动态面控制

针对不确定多输入多输出严格反馈块控非线性系统,提出一种鲁棒自适应动态面控制方法.该方法在反推自适应神经网络控制中引入动态面控制简化控制律,同时对自适应律进行改进以改善系统的过渡过程动态品质,保证了系统在简化的控制律下仍具有良好的动态特性.通过Lyapunov方法证明了闭环系统所有信号均有界,系统的跟踪误差指数收敛到有界紧集内.最后给出的某新型战斗机六自由度仿真结果表明了该方法的有效性.     

一阶纯滞后智能非线性PI 控制器优化设计

针对大惯性以及纯滞后严重的工业过程对象控制精度低的问题，采用专家控制与单神经元PI控制相结合的算法，并利用自适应变尺度混沌优化方法对其进行最优整定，从而实现了智能非线性PI控制器的优化设计．仿真结果表明，该智能非线性PI控制器具有较高的控制精度．     

一种大时滞系统的无模型自适应控制改进算法

利用无模型自适应控制（Model—freeadaptive control,MFAC）方法仅需要被控对象输入和输出数据,而不需要其他任何信息的优点。针对工业生产过程中普遍存在的大时问滞后的特点,提出针对未知模型的大时滞对象的无模型自适应控制改进算法（Improved MFAC on Large Time—delay System,LTDS-IMFAC）。在改进算法中,在基本无模型自适应控制算法的基础上引入了带有滞后时间的输入变化率的约束项．以此来减小大时间滞后对整个控制过程的影响。通过MATLAB仿真试验证明了改进算法对于大时滞系统的控制具有较好的有效性。     

具有不确定性时滞及仿射结构不确定性过程的简易自适应控制

讨论了具有不确定性时滞及仿射结构不确定系统的简易自适应控制问题.给出了对系统再建模方法,所提出的方法可更精确评价不确定性时滞.通过对受控对象附加鲁棒并行补偿器补偿,设计了简易自适应控制器.     

大时滞系统自适应模糊Smith控制

针对大时滞对象，把史密斯（Smith）预估控制原理和模糊控制器参数的自适应调整方法结合起来，即在Smith预估控制系统中，利用自适应机制在线整定模糊控制器的参数，即根据控制系统在各个阶段呈现出的不同特点来适时调节模糊控制器的量化和比例因子，以适应对象特性的变化。仿真研究表明，所提方法能有效克服普通模糊控制算法不适应大时滞系统控制和常规Smith算法过于依赖模型精度的缺陷，提高了普通模糊控制器对大滞后系统的控制能力。同时该算法还具有很强的鲁棒性和良好的控制品质。     

Observer-based force reflecting robust coordination control for networked bilateral shared telerobotic system

This paper proposes observer-based output feedback force reflecting robust coordination control for networked bilateral shared telerobotic system with asymmetrical delay and uncertainty. We design state feedback-based force reflecting control algorithm provided that all the states are available for feedback. We then replace the unknown velocity states by model-free observer to develop output feedback-based force reflecting robust coordination control algorithm for bilateral shared autonomous system. The coordination control algorithm is designed by combining delayed position and velocity states with the reflected interaction forces from human and environment. Robust adaptive control theory is employed to deal with uncertainty. The stability analysis is shown by using Lyapunov method. The method does not require linear matrix inequality and uncertainty. Compared with other force reflection methods, the design uses reflected forces from both interaction between master and human and between slave and environment. Experimental results are presented to demonstrate the validity of the proposed design for real-time applications.     

时变时滞系统的参数辨识及自适应控制

基于最小二乘法一类辨识算法的自适应控制,一般只适用于时滞已知且时不变的被控过程,本文提出了一种包括可估计时变时滞在内的参数辨识方法,该方法扩展了最小二乘一类辨识算法及相应的自适应控制的应用范围,文中通过一个实例讨论了该方法在自适应控制中的应用,并谈及下一步的研究工作。     

Evaluate time delay from sensor's data by trend similarity search

Time delay estimation is a general issue in both signal processing and process control fields. Neither offline step impulse response-based methods nor least squares methods in control field estimate time delay directly from the real running data. Although the methods for signal processing directly evaluate the delay from signals, such as correlation calculation, coherence analysis and least mean square methods, they are mainly suitable for two signals only different at a time delay part and an attenuation factor. In this article, an estimation method is proposed which is directly based on the real running input and output data of a control plant. The input and output signals of a plant show raw monotony from each other in many cases. According to this feature, we estimate the delay by comparing the trend of two signals. Furthermore, it is extended to an adaptive method for estimating piecewise time-varying delay by sliding window and forgetting factor. The experiments on real plant show the good performances of our methods. The simulation experiments demonstrate that our basic method performs better than CCF or coherence analysis for the nonlinear plant and the adaptive one performs better than least mean square methods for the signals with transfer function except time delay.     

Robust Adaptive Control of Uncertain Stochastic Hamiltonian Systems with Time Varying Delay

This paper investigates the robust adaptive control problem for a class of time‐delay stochastic Hamiltonian systems. The system under study involves stochastics, parameter uncertaintiess, and time varying delay. The aim of this study is to design an uncertainty‐independent adaptive control law such that, for all admissible uncertainties, as well as stochastics, the closed‐loop Hamiltonian system is robustly asymptotically stable in mean square. Sufficient conditions are proposed to guarantee the rationality and validity of the proposed control laws, which are derived based on Lyapunov functional method. The performance of the controllers is validated through digital simulations.     

Adaptive Output Feedback based Output Tracking Control for a Time‐Delay System with a PFC

This output tracking control system for time‐delay systems with a parallel feedforward compensator (PFC). It has been clarified that one can design an adaptive output tracking control system for systems with almost strictly positive real (ASPR) properties. However, the robust stability for non‐ASPR time‐delay systems with a PFC introduced for making the resulting augmented system ASPR has not been analyzed. In this paper, it will be clarified that one can design a stable adaptive output tracking control system for time‐delay systems with a PFC, even if the resulting augmented system is not exactly ASPR.     

ADAPTIVE SLIDING MODE BACKSTEPPING CONTROL OF NONLINEAR SYSTEMS WITH UNMATCHED UNCERTAINTY

This paper considers an adaptive backstepping algorithm for designing the control for a class of nonlinear continuous uncertain processes with disturbances that can be converted to a parametric semi‐strict feedback form. Sliding mode control using a combined adaptive backstepping sliding mode control (SMC) algorithm, is also studied. The algorithm follows a systematic procedure for the design of adaptive control laws for the output tracking of nonlinear systems with matched and unmatched uncertainty.     

A model reference adaptive control system for a class of discrete linear time-varying systems with time delay

This paper presents a new method for designing a model reference adaptive control system for a class of discrete linear time-varying systems with time delay. Based on Lyapunov's stability theorem, adaptive algorithms guarantee the asymptotic stability of the error between the plant output and the reference sequence without using anticipated values of the plant output. The validity of the theoretical result is illustrated by numerical examples.     

非线性时滞系统的稳定自适应控制

针对模型不确定性的连续时间时滞系统，提出了一种新的神经网络自适应控制。系统的辨识模型是由神经网络和系统的已知信息组合构成，在此基础上，建立时滞系统的预测模型。基于神经网络预测模型的自适应控制器能够实现期望轨线的跟踪，理论上证明了闭环系统的稳定性。连续搅拌釜式反应器仿真结果表明了该控制方案的有效性。