【作者】 王卫红；

【导师】 侯忠生；

【作者基本信息】 北京交通大学 ， 系统分析与集成， 2008， 博士

【摘要】 论文以无模型自适应控制理论为出发点,针对其在一般非线性离散时间系统跟踪问题、系统扰动抑制、MIMO系统解耦、以及和其他无模型控制方法相互融合等方面存在的不足进行深入研究。研究过程中,将无模型自适应控制、滑模变结构控制、神经网络、前馈补偿及迭代反馈整定的基本思想有机结合,提出一系列无模型自适应控制的新方法。论文的主要工作及其贡献总结如下:1、借鉴无模型自适应控制方法中SISO系统非参数动态线性化的方法,推导出一般非线性离散时问MIMO系统非参数动态偏格式线性化(Partial FormLinearization,PFL)和非参数动态全格式线性化(Full Form Linearization,FFL)定理,并给出严格的数学证明。2、针对一般非线性离散时间系统,提出一类自适应准滑模控制(AdaptiveQuasi-Sliding Mode Control,ASMC)的新方法。首先采用非参数动态紧格式线性化(Tight Form Linearization,TFL)方法将系统线性化,再设计自适应准滑模控制器,同时给出控制算法的稳定性分析。在此基础上将该算法推广,提出基于偏格式线性化和全格式线性化的自适应准滑模控制。3、针对一般非线性离散时间系统的不确定性和扰动抑制问题,提出一种基于神经网络估计的自适应准滑模控制算法(Neural Network Based AdaptiveQuasi-Sliding Mode Control,NN-SMC)。算法包括两部分,其一是基于紧格式动态线性化模型的自适应准滑模控制器设计,其中动态线性化方法中“伪偏导数”的估计算法仅依赖于系统I/O实时量测值。其二是采用径向基神经网络估计器来估计系统的综合不确定性。理论分析证明了系统的BIBO稳定性。仿真结果验证了所提算法的有效性。4、针对一般非线性离散时间MIMO系统,基于非参数动态线性化模型,提出一种基于神经网络补偿的自适应准滑模解耦控制算法(Neural Network BasedAdaptive Quasi-Sliding Mode Decoupling Control,NN-SMDC)。该算法将其他回路的输入对于某一回路输出的耦合影响视为可测干扰,通过神经网络对这部分干扰进行估计、补偿,再结合滑模控制算法实现系统解耦。理论证明该算法是稳定的。5、针对一般非线性离散时间MIMO系统,在非参数动态线性化基础上,引入自适应前馈补偿思想,提出一种基于自适应前馈补偿的解耦控制(AdaptiveFeedforward Compensation Decoupling Control,AFCDC)方法。理论分析表明所提方法能够保证系统广义跟踪误差渐近收敛到零。6、针对一般非线性离散时间系统,提出一种基于IFT方法的无模型自适应控制器设计及其参数整定算法(IFT Based Model-Free Adaptive Control,IFT-MFAC)。该算法首先依据TFL、PFL、FFL定理推导出3种相应的控制器结构,再采用IFT方法整定控制器参数。控制器结构的合理性由线性化定理来保证,从而解决了现有IFT算法控制器结构给出时存在的盲目性。7、在应用方面,研究了本文所提ASMC算法在直线电机控制中的应用和NN-SMDC算法在三容水箱液位控制中的应用。更多还原

【Abstract】 This dissertation mainly focuses on some issues of the model-free adaptive control(MFAC) theory for a class of general nonlinear discrete-time SISO and MIMO systems.Regarding on the synthesis of several methods,such as the model-free adaptive control,sliding mode control,neural networks,the feedforward compensation and iterative feedback tuning(IFT),a series of new model-free adaptive control algorithms are proposed.These algorithms are designed to solve several problems such as,the tracking problem and disturbance rejection of nonlinear discrete-time SISO systems,the decoupling problem of the MIMO nonlinear discrete-time systems,and the cross-fusion research between MFAC and IFT.The main works and contributions are summarized as the following seven points.1.Based on the non-parametric dynamic linearization method for a class of general nonlinear discrete-time SISO systems,two similar non-parametric dynamic linearization methods are developed for a class of general nonlinear discrete-time MIMO systems.2.A series of new adaptive quasi-sliding mode control(ASMC) schemes are proposed for a class of nonlinear discrete-time systems.The tight format linearization (TFL) based linearization model of the system is adopted firstly.Secondly,based on this linearization model we propose the adaptive quasi-sliding mode control and give the theoretical analysis.Finally,another two similar adaptive quasi-sliding mode control schemes are put forward,which based on partial form linearization(PFL) and full form linearization(FFL),respectively.3.A class of neural network based adaptive quasi-sliding mode control algorithm (NN-SMC) is developed for the disturbances rejection and uncertainty problem to the general nonlinear discrete-time systems.The algorithm includes two parts:one is the design of TFL model based adaptive quasi-sliding mode controller,whose linearization parameters,i.e.pseudo-partial-derivatives(PPD) are estimated on-line from the I/O information of the system,the other is the estimation of the system uncertainty part acquired by a NN-based predictor.The BIBO stability is proven via rigorous theoretical analysis and the simulation results validate the effectiveness of the proposed method.4.A class of neural network based adaptive quasi-sliding mode decoupling control algorithm(NN-SMDC) is proposed for MIMO discrete-time nonlinear system.The algorithm also includes two parts:NN-based predictors and sliding mode controllers. The coupling effects among control loops are treated as the measurable disturbance which is estimated by NN-based predictors.Then the decoupling control of the MIMO systems is carried out with sliding mode control.The rigorous theoretical analysis is given also.5.In this part,an adaptive decoupling controller with feedforward compensation (AFCDC) is presented for a class of nonlinear multivariable discrete-time dynamic systems.This design is model-free,based directly on pseudo Jacobi matrix derived on-line from the input and output information of the system.Theoretical analysis and simulation results show that the model-free indirect adaptive decoupling control system is stable and convergent.6.Based on non-parametric dynamic linearization technique and IFT technique,a parameters’ tuning method of the model-free adaptive controller(IFT-MFAC) is proposed for a class of nonlinear discrete-time systems.Three controller structures are deduced according to TFL、PFL、FFL theorem,then the controller parameters are tuned by IFT technique.The rationality of the 3 structures of the controller is assured by theorem of TFL,PFL and FFL,whereas,the controller structure is designed blindly by IFT.Obviously,the new method settles down this problem more properly.7.In this part,ASMC and NN-SMDC algorithm are introduced into the linear motor control system and the three-tank system,respectively.The simulation results show the validity of these control schemes.更多还原