全系数自适应控制方法对参数慢时变对象的鲁棒性及其应用

本文研究了全系数自适应控制方法对参数慢时变对象的鲁棒稳定性。证明了存在未建模动态及有界扰动情况下，采用经投影修正的梯度算法估计标称对象，黄金分割自适应控制器仍能稳定标称对象。对含乘性不确定性的线 性慢时变对象，给出了自适应控制系统鲁棒稳定的充分条件。作为理论结果的一个应用，本文分析了液体元地点发动机工作期间卫星姿态的全系数自适应控制方案的鲁棒稳定性。     

一种自适应鲁棒控制方法及其闭环稳定性分析

研究了含有未建模动态的慢时变系统的自适应镇定问题.考虑的对象具有非最小相 位、含未建模动态和大范围时变参数等不良特性,且存在未知但有界外部扰动.这类对象很难 用时不变鲁棒控制器或传统自适应控制器进行镇定.利用l1优化设计结合参数估计的投影算 法,提出了一种自适应鲁棒控制策略.基于l1优化设计的连续性和投影算法的收敛性,证明了 这种控制策略能够持续适应慢时变对象并且保持闭环系统一致稳定性.鲁棒性分析表明这种 控制策略具有良好的鲁棒镇定性.     

连续时间时变参数系统的指数稳定性和Lp指数稳定性

本文给出具有慢时变参数的连续时间线性齐次系统指数稳定的一个一般性条件，并给出具有随机时变参数的连续时间线性齐次系统Ｌｐ指数稳定的一个充分条件。这些结果可用来分析带有未建模动态，确定性噪声或随机噪声及慢时变参数的自适应控制系统的稳定性问题。     

具有未建模动态和输出约束的耦合系统的分散自适应控制

针对一类具有输入、状态未建模动态和非线性输入的耦合系统,提出一种自适应神经网络控制方案.利用径向基函数神经网络逼近未知非线性连续函数;引入动态信号和正则化信号处理状态及输入未建模动态;通过引入非线性映射,将具有时变输出约束的严格反馈系统化为不含约束的严格反馈系统.最后,通过理论分析验证闭环系统中所有信号是半全局一致最终有界的,仿真结果进一步验证了所提出控制方案的有效性.     

基于联合滤波算法的复杂时变系统在线自适应逆控制

针对一类结构和参数均具备时变特性的复杂时变系统,提出一种新的基于联合滤波算法的在线自适应逆控制方法.该方法在处理参数时变问题的同时可兼顾系统的结构时变特性,实现复杂动态系统的在线跟踪控制.同时提出新的联合Volterra核函数滤波算法,该算法克服了原Volterra滤波器计算复杂运算速度慢的缺点,实现了动态非线性系统的在线跟踪控制.通过仿真分析可以得出,对于此类线性、非线性复杂时变系统,基于新的联合滤波器的自适应逆控制方法可以快速有效的实现动态对象在线建模与控制.     

带有死区输入的非线性系统的自适应控制

针对一类具有死区非线性输入的非线性系统,同时考虑系统中存在未建模不确定项,设计了自适应控制器及未知参数的自适应估计率.该控制器使得闭环系统全局稳定且实现了输出信号对参考信号的精确跟踪.仿真结果进一步证实了该控制器能对未知死区及未建模动态进行有效的补偿。     

一类不确定非线性系统基于特征模型的复合自适应控制

针对一类不确定非线性系统的跟踪控制问题,提出一种基于特征模型的复合自适应控制方法.该方法的创新性在于基于系统的误差特征模型,构建一种综合跟踪控制误差和模型估计误差的特征参量复合自适应律,该自适应律用于控制器设计和分析,可同时实现跟踪控制误差和模型估计误差的收敛.此外,为便于特征参量自适应律的设计和分析,根据特征参量的慢时变特性,将其视为未知标称常数项和时变误差项之和,并且选用其中常数项的估计量作为自适应控制参数.进一步,为抑制特征参量中时变误差项对系统稳定性和模型估计误差收敛性的影响,在控制器及复合自适应律设计中引入带饱和函数的非线性环节.理论分析证明闭环控制系统稳定,且跟踪控制误差和模型估计误差收敛到原点的一个邻域内.仿真结果表明,与现有仅根据模型估计误差调节的基于特征模型的自适应控制方法相比,所提出的复合自适应控制方法具有更好的控制性能.     

基于特征模型自适应方法的一类最小相位非线性系统稳定性分析

在特征建模理论中,由全系数自适应控制器组成的闭环系统是一个非常复杂的混合系统,采用传统自适应框架难以进行分析,因此,稳定性分析一直是该领域的一个难点.本文以一类最小相位、相对阶为2的单输入单输出(SISO)高阶非线性系统为例,通过一种新的特征建模方法,把高阶混合系统变换为一个含有稳定未建模误差的、参数有界慢时变的采样间接自适应控制问题,并利用基于欧拉近似离散化模型的采样系统稳定性分析方法进行了系统分析.该方法可进一步推广到任意相对阶的SISO或多输入多输出(MIMO)系统甚至无限维最小相位系统中去.     

数据与未建模动态驱动的非线性PID切换控制

本文充分利用系统的数据信息和知识,把数据驱动控制、PID控制与一步超前最优控制策略相结合,提出了数据与未建模动态驱动的非线性PID切换控制方法.该方法首先利用被控对象往往运行在工作点附近的特点及系统丰富可测的数据信息,把被控对象表示成低阶控制器设计模型与高阶非线性项(未建模动态)和的形式.与以往方法的本质区别在于,所提的方法直接将未建模动态分解为前一拍数据与未知增量的和,并充分利用未建模动态可测数据信息补偿系统未知的非线性动态特性,设计非线性PID控制器,对未建模动态的未知增量采用自适应神经模糊推理系统(ANFIS)进行估计,从而设计带有未建模动态增量估计的非线性PID控制器.将控制器的跟踪误差引入切换指标,两个控制器通过切换机制协调控制系统,既保证系统的稳定,同时提高系统的性能.为解决PID控制器参数难以选择的问题,采用一步超前最优控制策略进行参数设计,从理论上给出了PID控制器参数选择的一般原则和方法,推导了保证闭环系统输入输出稳定性的条件;最后,通过数值仿真实验以及在水箱液位控制系统的物理对比实验,实验结果验证了所提算法的有效性和实用性.     

高动态临近空间飞行器的姿态自适应解耦控制

研究飞行器空间动态特性优化控制问题,高动态临近空间飞行器的姿态运动模型是一个多变量非线性时变不确定系统,由于小扰动线性化理论和系数冻结基本假设的传统三通道独立综合(设计)方法已很难适用。针对多变量非线性时变不确定系统对象的非线性设计方法来解决控制问题。提出了一种神经网络自适应反馈线性化方法,用FCMAC神经网络估计实际模型和标称模型间精确线化反馈阵的误差,修正实际系统的反馈线性化模型,有效克服了反馈线性化需要被控对象精确建模的局限性,实现了高马赫、大空域、快角度机动条件下的姿态解耦控制,并进行仿真。仿真结果表明:解耦控制系统在气动拉偏30%下超调小于7%,通道间交叉耦合小于1°,能够满足对象的控制要求。     

Robust continuous-time adaptive control by parameter projection

The problem of adaptive control of a continuous-time plant of arbitrary relative degree, in the presence of bounded disturbances as well as unmodeled dynamics, is addressed. The adaptation considered is the usual gradient update law with parameter projection, the latter being the only robustness enhancement modification employed. It is shown that if the unmodeled dynamics, which consists of multiplicative as well as additive system uncertainty, is small enough, then all the signals in the closed-loop system are bounded. This shows that extra modifications are not necessary for robustness with respect to bounded disturbances and small unmodeled dynamics. In the nominal case, where unmodeled dynamics and disturbances are absent, the asymptotic error in tracking a given reference signal is zero. Moreover, the performance of the adaptive controller is also robust     

Robust adaptive regulation of linear time-varying systems

Given reference signals generated by a stable, linear time-invariant exosystem, to be tracked by the output, the problem of robust adaptive regulation by output feedback is addressed for single-input, single-output linear systems with unknown time-varying parameters and unmodeled bounded additive disturbances. Without requiring parameters or disturbances to be slowly varying or to have known bounds, a robust adaptive regulator is designed that ensures boundedness of closed-loop signals and arbitrarily improved transient performance for the regulation error. Asymptotic regulation is achieved when parameters belong to a compact set determined by the controller parameters' time derivatives are L₁∩L_∞, and disturbances and parameter variations from unknown constant nominal values are L₂∩L_∞     

Pole placement direct adaptive control for time-varying ill-modeledplants

The problem is discussed of preserving the stability of pole placement direct adaptive control in spite of output bounded disturbances, time varying plant model parameters, and unmodeled dynamics, assumed to be small in the mean. The controller parameter estimates are shown to track, in the mean, their true (time varying) parameter values. Such a convergence property is achieved using an ad hoc, internally generated, excitation sequence that ensures persistent excitation. In the ideal case the convergence of the parameter estimates is exponential, avoiding, in particular, possible chaotic phenomena     

A new adaptive law for robust adaptation without persistent excitation

A new adaptive law motivated by the work of loannou and Kokotovic (1983) is proposed for the robust adaptive control of plants with unknown parameters. In this adaptive law the output error plays a dual role in the adjustment of the control parameter vector. The advantages of using the adaptive law over others proposed in the literature are discussed. In the ideal case the adaptive system has bounded solutions; in addition, the origin of the error equations is exponentially stable when the reference input is persistently exciting and has a sufficiently large amplitude. The adaptive system is also shown to be robust under bounded external disturbances. Finally, it is shown that, by suitably modifying the adaptive law, the overall system can be made robust in the presence of a class of unmodeled dynamics of the plant. Simulation results are presented throughout the paper to complement the theoretical developments.     

A robust direct adaptive controller

This paper proposes a new direct adaptive control algorithm which is robust with respect to additive and multiplicative plant unmodeled dynamics. The algorithm is designed based on the reduced-order plant, which is assumed to be minimum phase and of known order and relative degree, but is analyzed with respect to the overall plant which, due to the unmodeled dynamics, may be nonminimum phase and of unknown order and relative degree. It is shown that if the unmodeled dynamics are sufficiently small in the low-frequency range, then the algorithm guarantees boundedness of all signals in the adaptive loop and "small" residual tracking errors for any bounded initial conditions. In the absence of unmodeled dynamics, the residual tracking error is shown to be zero.     

Stable discrete-time adaptive control in the presence of unmodeleddynamics

Results obtained previously on the stability of adaptive predictive control systems are generalized by reducing the requirements for stability to a single condition that is applicable to linear discrete processes with disturbances and unmodeled plant dynamics. The proposed design approach uses a normalized parameter estimation scheme, which permits a formal proof that the modeling errors can be treated as a bounded disturbance, and a parameter adaptation stopping criterion to guarantee global stability     

具有任意非线性输入的鲁棒模型参考自适应控制

提出了一种分析具有任意非线性输入的模型参考自适应(MRAC)的新方法,它不要 求对象是稳定的,允许系统存在未建模动态和有界扰动.同时给出了一种提高非线性输入自 适应系统特性的补偿方案,它通过在控制律中引入跟踪误差,可以提高系统的暂态及稳态精 度,而又不破坏MRAC系统的收敛特性.     

Robust adaptive control of nonlinear systems represented by input-output models

A robust adaptive control scheme is proposed for a class of nonlinear systems represented by input-output models with unmodeled dynamics. The scheme does not require the unknown parameters to satisfy the linear dependence condition and parameter estimation is not needed. With the proposed control scheme, all the variables in the closed-loop system are bounded in the presence of unmodeled dynamics and bounded disturbances. Moreover, the mean-square tracking error can be made arbitrarily small by choosing some design parameters appropriately.     

Adaptive control of linear time-varying plants: a new modelreference controller structure

The problem of developing a control law which can force the output of a linear time-varying plant to track the output of a stable linear time-invariant reference model is discussed. It is shown that the standard model reference controller, used for linear time-invariant plants, cannot guarantee zero tracking error in general when the plant is time-varying. A new model reference controller is proposed which guarantees stability and zero tracking error for a general class of linear time-varying plants with known parameters. When the time-varying plant parameters are unknown but vary slowly with time, it is shown that the new controller can be combined with a suitable adaptive law so that all the signals in the closed loop remain bounded for any bounded initial conditions and the tracking error is small in the mean. The assumption of slow parameter variations in the adaptive case can be relaxed if some information about the frequency or the form of the fast varying parameters is available a priori. Such information can be incorporated in an appropriately designed adaptive law so that stability and improved tracking performance is guaranteed for a class of plants with fast varying parameters