具有不等式路径约束的微分代数方程系统的动态优化

针对具有不等式路径约束的微分代数方程（Differential-algebraic equations,DAE）系统的动态优化问题,通常将DAE中的等式路径约束进行微分处理,或者将其转化为点约束或不等式约束进行求解.前者需要考虑初值条件的相容性或增加约束,在变量间耦合度较高的情况下这种转化求解方法是不可行的;后者将等式约束转化为其他类型的约束会增加约束条件,增加了求解难度.为了克服该缺点,本文提出了结合后向差分法对DAE直接处理来求解上述动态优化问题的方法.首先利用控制向量参数化方法将无限维的最优控制问题转化为有限维的最优控制问题,再利用分点离散法用有限个内点约束去代替原不等式路径约束,最后用序列二次规划（Sequential quadratic programming,SQP）法使得在有限步数的迭代下,得到满足用户指定的路径约束违反容忍度下的KKT（Karush Kuhn Tucker）最优点.理论上证明了该算法在有限步内收敛.最后将所提出的方法应用在具有不等式路径约束的微分代数方程系统中进行仿真,结果验证了该方法的有效性.     

区间非线性路径约束的动态优化问题求解

本文研究路径约束中含有区间参数形式的动态优化问题,提出了一种新的非线性路径约束的确定化描述形式,和采用惩罚函数法的求解算法。对于转化后的极大极小优化命题,论文提出采用Lagrangian多项式加权和的方法得到有限维的确定性优化求解形式。该算法可有效地描述和求解不确定参数动态优化问题。     

一类具有未知参变量的非线性系统的鲁棒故障诊断

讨论了一类含有状态和输入未知参变量的非线性系统的故障诊断问题,在满足一些几何条件的情况下,该类系统可化成两类不同的子系统。对于其中一类不受故障影响的子系统,得到了其非线性自适应观测器及参数自适应律的设计;另一类受故障影响的子系统的状态是可测的,给出了该类子系统的基于模型监测和故障诊断的观测方案。然后,给出了用于验证本文所提方案的数字示例。     

基于自适应未知输入观测器的非线性动态系统故障诊断

针对以往故障诊断研究中要求故障或故障导数及系统干扰的上界是已知的不足,以及难以同时诊断执行器故障和传感器故障的问题,提出一种自适应未知输入故障诊断观测器,能够同时重构非线性动态系统的执行器故障和传感器故障.首先,利用H_∞性能指标抑制未知输入对故障重构的影响,采用Lyapunov泛函得到观测误差动态系统的稳定性;然后,通过线性矩阵不等式求解观测器增益阵,并实现故障重构;最后,通过直流电机系统的仿真验证了所提出方法的有效性.     

无未知参数先验信息的非线性自适应观测器设计

研究了一类具有未知参数的非线性系统自适应观测器设计问题．不同于现有结果,本文所研究的非线性系统更为。一般,已知的系统信息更少：1）系统未知参数的范数的上界未知;2）具有关于可测输出非Lipschitz连续的非线性动态;3）系统输出显式地依赖于控制输入．通过设计自适应调节器来估计未知参数范数,从而给出了不基于未知参数先验信息的非线性自适应观测器设计的新方法．所设计的观测器为令局渐近收敛的,即实现了系统状态的渐近重构,确保了未知参数估计的一致有界性．此外,在系统输出不显式地依赖于控制输入的条件下,研究了降维观测器的设计问题．仿真例子验证了本文理论结果的正确性．     

一类非线性系统辨识的参数空间优化遗传算法

提出一种用于非线性系统辨识的具有较强参数空间优化能力的遗传算法,给出一种新的复合型变异算子,并设计相应的选择策略,较好解决了非线性系统辨识中的“维数灾难”问题。实验研究结果表明,该方法具有较高的精度和鲁棒性。     

无未知参数先验信息的非线性自适应观测器设计

研究了一类具有未知参数的非线性系统自适应观测器设计问题.不同于现有结果,本文所研究的非线性系统更为一般,已知的系统信息更少:1)系统未知参数的范数的上界未知;2)具有关于可测输出非Lipschitz连续的非线性动态:3)系统输出显式地依赖于控制输入.通过设计自适应调节器来估计未知参数范数,从而给出了不基于未知参数先验信息的非线性自适应观测器设计的新方法.所设计的观测器为全局渐近收敛的,即实现了系统状态的渐近重构,确保了未知参数估计的一致有界性.此外,在系统输出不显式地依赖于控制输入的条件下,研究了降维观测器的设计问题.仿真例子验证了本文理论结果的正确性.     

含未知动态与扰动的非线性系统神经网络嵌入学习控制

针对带有不确定性与扰动的非线性系统的性能优化问题,提出一种基于神经网络嵌入的学习控制方法.对一类常见的Lyapunov函数导数形式,将神经网络控制器集成到某种对系统稳定的基准控制器中,其意义在于将原控制器改进为满足Lyapunov稳定的神经网络参数可调控制器,从而能够利用先进的神经网络学习技术实现控制器的在线优化.建立...     

基于菌群优化算法的非线性系统模型参数辨识

非线性系统模型参数估计一直是自动控制领域的研究热点。针对非线性系统,结合菌群优化(BSFO)算法的特点,提出了一种新型的非线性系统模型参数辨识方法。通过将待辨识参数设置为群体细菌在参数空间的位置,并模拟细菌群体觅食的动态行为来实现对系统参数的辨识,有效地提高了参数辨识的精度和效率。通过对重油热解三集总模型进行了仿真研究,得到了较为精确的过程模型,模型输出与实际输出基本一致。仿真结果表明,菌群优化算法为非线性系统模型参数估计提供了一种有效的途径。     

外激励参数未知系统的同步控制及其参数识别

考虑外激励的振幅和频率未知的混沌系统.把系统未知参数扩张成系统的新的状态量,构造新的驱动系统.用参数自适应控制方法构造一个响应系统,使之与驱动系统的结构相同.基于级联系统的稳定性理论,通过两个步骤来设计控制器和参数自适应律,使得驱动—响应系统能够达到完全同步.当驱动—响应系统达到完全同步时,就可以识别出系统的未知参数振...     

Backstepping design for time-varying nonlinear systems with unknown parameters

A simple backstepping design procedure is proposed and sufficient conditions for global partial state- and dynamic- feedback stabilization for a class of triangular systems with unknown time-varying parameters are derived.     

Random search techniques for optimization of nonlinear systems with many parameters

This paper concerns the application of adaptive random search techniques to large parameter optimization and identification problems. A brief review of the algorithm is presented, followed by a discussion of 3 examples: (1) identification of 25 unknown parameters in a nonlinear 5-degree of freedom mechanical system (2) identification of 17 parameters in a nonlinear model of soil mechanics and (3) determination of optimum values of 24 parameters to obtain a match of two response spectra. The results indicate the robustness and applicability of adaptive random search to a wide variety of nonlinear optimization problems.     

Stabilization of stochastically singular nonlinear jump systems with unknown parameters and continuously distributed delays

In this paper, the problem of robustly asymptotic stabilization for a class of stochastically nonlinear singular jump systems is investigated. The jumping parameters are modeled as a continuous-time, finite-state Markov chain. Based on the Lyapunov-Krasovskii functional and stochastic analysis theory as well as a state feedback control technique, some new sufficient conditions are derived to ensure the asymptotic stability of the trivial solution in the mean square. A key feature of this paper is that singular, nonlinear, noise perturbations, unknown parameters and continuously distributed delays are all considered. In particular, the obtained stabilization criteria in this paper are expressed in terms of LMIs, which can be solved easily by recently developed algorithms. Finally, two numerical examples are presented to illustrate the effectiveness of the theoretical results. Moreover, the second example shows that delay-dependent stabilization criteria are less conservative than delay-independent criteria.     

一类非仿射非线性时滞系统的动态状态反馈镇定

本文讨论了一类非仿射非线性时滞系统的全局镇定问题.通过引入辅助积分系统和构造合适的LyapunovKrosovskii泛函,提出了一种基于反推法的时滞无关动态状态反馈控制器,所提控制方法无需时滞的任何先验知识.利用Lyapunov稳定性理论证明了该控制策略能够保证非仿射时滞系统状态渐近收敛于原点,且所有闭环信号全局有界.一个仿真实例进一步验证了所得控制方案的可行性与有效性.     

Adaptive output-feedback control of nonlinear systems with unknown nonlinearities

In this paper, we consider global adaptive output-feedback control of nonlinear systems in output-feedback form, without a priori knowledge of system nonlinearities. Our proposed adaptive controller is a high-gain linear controller (since we have no knowledge on system nonlinearities), with the high-gain parameter tuned online via a switching logic. Global stability results of the closed-loop system have been proved.     

An observation algorithm for nonlinear systems with unknown inputs

This paper deals with the design of unknown input observers for nonlinear systems. An algorithm which determines if the state and the unknown input of the system can be recovered in finite time is introduced. This algorithm leads to the transformation of the system into an extended block triangular observable form suitable for the design of finite time observers. The proposed method is useful to relax some restrictive conditions of the existing nonlinear unknown input observer design procedures.     

The optimal control of linear systems with unknown parameters

The optimal open-loop controller for a linear system with some unknown parameters is determined. It is assumed that the unknown parameters do not vary during the process, and that their probability distribution function is given. The criterion for optimality is a quadratic function of the state and the input whoso expectation with respect to the unknown parameters has to be minimized. An explicit solution for the optimal controller is obtained by using the methods of the calculus of variations. Further, an alternate approach is presented which, for the problem solved, leads to the same optimal controller.     

Adaptive anti-synchronization of chaotic systems with fully unknown parameters

This paper centers on the chaos anti-synchronization between two identical or different chaotic systems using adaptive control. The sufficient conditions for achieving the anti-synchronization of two chaotic systems are derived based on Lyapunov stability theory. An adaptive control law and a parameter update rule for unknown parameters are introduced such that the Chen system is controlled to be the Lorenz system. Theoretical analysis and numerical simulations are shown to verify the results.     

含有未知控制方向的非线性时滞系统学习控制

本文对于一类含有未知控制方向及时滞的非线性参数化系统,设计了自适应迭代学习控制算法.在设计控制算法过程中采用了参数分离技术和信号置换思想来处理系统中出现的时滞项,Nussbaum增益技术解决未知控制方向等问题.为了对系统中出现的未知时变参数和时不变参数进行估计,分别设计了差分及微分参数学习律.然后通过构造的Lyapunov-Krasovskii复合能量函数给出了系统跟踪误差渐近收敛及闭环系统中所有信号有界的条件.最后通过一个仿真例子说明了控制器设计的有效性.