非线性时变系统自适应backstepping学习控制

针对含有混合未知参数的高阶非线性系统,利用backstepping方法,提出了一种自适应重复学习控制方法,该方法与分段积分机制相结合,可以处理时变参数在一个未知紧集内周期性快时变的非线性系统,通过构造微分-差分参数自适应律,设计了一种自适应控制策略,使跟踪误差在误差平方范数意义下渐近收敛于零,利用Lyapunov泛函,给出了闭环系统收敛的一个充分条件.实例仿真结果说明了该方法的有效性.     

基于观测器的非严格反馈时滞非线性系统的神经网络自适应控制

针对一类非严格反馈的时滞非线性系统, 研究了一类基于观测器的自适应神经网络控制问题.针对系统中存在未知状态变量的问题, 设计了一个状态观测器.利用反步法和径向基神经网络的逼近特性, 提出了一种自适应神经网络输出反馈控制方法.所设计的控制器保证了闭环系统中所有信号的半全局一致有界性.最后, 通过仿真验证了所提控制方法的有效性.     

网络化Euler-Lagrange系统的分布式编队机动控制北大核心CSCD

研究了网络化Euler-Lagrange系统自适应编队机动控制问题.针对参数不确定的Euler-Lagrange系统,利用滑模控制方法提出了一种自适应编队机动控制算法.基于Lyapunov稳定性理论,证明了闭环系统的稳定性.该算法的显著特点是通过引入一种特殊的有向网络拓扑来描述智能体之间的通信交互行为,使得系统中跟随者在不需要知道或估计时变机动参数的情况下,能够实现编队的方向、平移、形状的连续改变.最后对提出的自适应编队机动控制算法进行数值模拟以验证该控制方案的有效性.     

自适应Fuzzy系统及其逼近性能分析

本文提出了一种新的Fuzzy推理方法——自适应Fuzzy推理方法,基于该方法构造了自适应Fuzzy系统,证明了该系统不但具有泛逼近性,而且具有光滑性.基于该方法也得到了一种构造Fuzzy系统推理前件Fuzzy集的方法.使用该前件,CRI方法也具有光滑性,这使得CRI方法具有更广泛的意义.     

基于生物态势理论的Backstepping模糊控制

针对一类单输入单输出非线性系统,提出了一种基于生物态势理论的backstepping模糊自适应控制方法.设计中,将系统误差及其导数作为模糊规则的前件,将反应生物特性的生态位态势理论函数作为模糊规则的后件,设计了基于生物态势理论的Backstepping模糊控制器.该控制器将生物个体对外界扰动的适应对策引入设计中,使得控制器的自适应律具有生物自适应特性.并利用Lyapunov方法证明了闭环系统的稳定性.仿真结果进一步验证了方法的有效性.     

基于免疫函数的输入死区未知严格反馈非线性系统投影自适应指令滤波反步控制

为解决含有未知项以及输入死区的严格反馈非线性系统跟踪控制问题,提出一种基于免疫函数的投影自适应指令滤波有限时间控制方法.该方法使用免疫函数构造扩张状态观测器对具有输入死区控制系统中的未知项进行逼近,并使用指令滤波解决反步法中微分爆炸问题,建立滤波误差补偿机制降低滤波误差对跟踪精度的影响,同时使用投影算子保证了自适应参数的有界性.与现有文献中基于障碍李雅普诺夫函数的自适应反步约束控制相比较,文章可同时约束系统状态、补偿跟踪误差以及自适应参数在预设的范围内,保证了闭环系统中所有信号有界,结合有限时间控制加快了控制系统的收敛速度.最后仿真结果表明了文章控制方法的有效性.     

仿生模糊直接自适应控制

针对一类不确定非线性系统,设计了一种具有仿生特性的模糊直接自适应控制方法.方法将反应生物特性的生态位态势理论函数作为模糊规则的后件,将生物个体对外界扰动的适应对策引入控制器设计中,利用超稳定理论设计自适应控制律,保证了闭环系统的稳定性和收敛性.仿真结果表明了方法的有效性,比常规控制方法有更好的控制效果.     

不确定关联时滞大系统的自适应分散镇定控制

针对时变的满足一定匹配条件的不确定关联时滞大系统,利用自适应界化技术,给出了设计分散镇定控制器的自适应的方法.其特点是在假设中系统不确定项是有界的,但界是未知的,且在关联项存在时变时滞的情况下证明了闭环自适应系统的渐近稳定性.最后举例说明了该方法的有效性.     

舵角输入饱和下的导弹自动驾驶仪系统自适应控制研究

针对一类导弹自动驾驶仪系统,从实际出发考虑系统舵角输入具有饱和非线性特性,在系统建模中利用多项式模型充分描述系统的气动参数不确定性,在此基础上深入研究了系统自适应控制器的设计方法.在控制器设计中,通过引入二阶辅助信号系统,对饱和非线性输入进行精确补偿.针对系统中未知气动参数,通过设计参数的自适应估计率实现对未知参数的精确估计,进而补偿了未知气动参数不确定性影响.自适应控制器在保障系统稳定的同时也实现了系统输出对指令信号的良好跟踪效果.     

一类非线性时滞混沌系统的自适应脉冲同步

针对一类非线性时滞混沌系统,提出了一种新的自适应脉冲同步方案.首先基于Lyapunov稳定性理论、自适应控制理论及脉冲控制理论设计了自适应控制器、脉冲控制器及参数自适应律,然后利用推广的Barbalat引理,理论证明响应系统与驱动系统全局渐近同步,并给出了相应的充分条件.方案利用参数逼近Lipschitz常数,从而取消了Lipschitz常数已知的假设.两个数值仿真例子表明本方法的有效性.     

Chaos control for Willamowski–Rössler model of chemical reactions

Real systems evolving towards complex state encounter chaotic behavior. This behavior is very important in chemical processes or in biological structures because it defines the direction of the evolution of the system. From this point of view, the capability of deliberate control of these phenomena has a great practical impact despite the fact that it is very difficult; this is the reason why theoretical models are useful in these situations. In order to obtain chaos control in chemical reactions, the analysis of the dynamics of Willamowski–Rössler system involving the synchronization of two Minimal Willamowski–Rössler (MWR) systems based on the adaptive feedback method of control is presented in this work. As opposed to previous studies where in order to obtain synchronization 3 controllers were used, implying from a practical point of view the control of the concentrations of three chemical species, in this study we showed that the use of just one is sufficient which in practice is important as controlling the concentration of a single chemical species would be much easier. We also showed that the transient time until synchronization depends on initial conditions of two systems, the strength and number of the controllers and we attempted to identify the best conditions for a practical synchronization.     

Dimension and local structures of attracting manifolds of smooth dynamical systems

We give a numerical algorithm to compute the dimension and local structures of attracting sets or invariant manifolds of smooth dynamical systems. This algorithm is based on local adaptive Galerkin bases and is tested for several examples. The important degrees of freedom that are preferentially selected, from the geometrical point of view, are found.     

An adaptive updating full-Newton step interior-point algorithm with modified Newton direction

We adopt the self-adaptive strategy to update the barrier parameter of a feasible primal-dual interior-point algorithm. We obtain two adaptive updating methods, namely, cheap updates and sharp updates. We compare the effectiveness of the short updates with the adaptive update methods on some benchmark problems. The numerical results show that the sharp updates method is superior to short updates and cheap updates methods.     

Self-Adaptive Extrapolated Gauss-Seidel Iterative Methods

In this paper, we consider a self-adaptive extrapolated Gauss-Seidel method for solving the Hermitian positive definite linear systems. Based on optimization models, self-adaptive optimal factor is given. Moreover, we prove the convergence of the self-adaptive extrapolated Gauss-Seidel method without any constraints on optimal factor. Finally, the numerical examples show that the self-adaptive extrapolated Gauss-Seidel method is effective and practical in iteration number.     

分数阶双指数混沌系统的自适应滑模同步

研究了分数阶双指数混沌系统的自适应滑模同步问题.通过设计滑模函数和控制器,构造了平方Lyapunov函数进行稳定性分析.利用Barbalat引理证明了同步误差渐近趋于零,获得了系统取得自适应滑模同步的充分条件.数值仿真结果表明:选取适当的控制器及与滑模函数,分数阶双指数混沌系统取得自适应滑模同步.     

Self-adaptive projection method for co-coercive variational inequalities

In some real-world problems, the mapping of the variational inequalities does not have any explicit forms and only the function value can be evaluated or observed for given variables. In this case, if the mapping is co-coercive, the basic projection method is applicable. However, in order to determine the step size, the existing basic projection method needs to know the co-coercive modulus in advance. In practice, usually even if the mapping can be characterized co-coercive, it is difficult to evaluate the modulus, and a conservative estimation will lead an extremely slow convergence. In view of this point, this paper presents a self-adaptive projection method without knowing the co-coercive modulus. We also give a real-life example to demonstrate the practicability of the proposed method.     

一个锥模型的自适应信赖域算法及其收敛性

本文研究了无约束最优化问题的基于锥模型的自适应信赖域算法.利用理论分析得到一个新的自适应信赖域半径.算法在每步迭代中以变化的速率、当前迭代点的信息以及水平向量信息调节信赖域半径的大小.从理论上证明了新算法的全局收敛性和Q-二阶收敛性.用数值试验验证了新算法的有效性.推广了已有的自适应信赖域算法的可行性和有效性.     

Numeric vs. symbolic homotopy algorithms in polynomial system solving: a case study

We consider a family of polynomial systems which arises in the analysis of the stationary solutions of a standard discretization of certain semi-linear second-order parabolic partial differential equations. We prove that this family is well-conditioned from the numeric point of view, and ill-conditioned from the symbolic point of view. We exhibit a polynomial-time numeric algorithm solving any member of this family, which significantly contrasts the exponential behavior of all known symbolic algorithms solving a generic instance of this family of systems.     

Combination of Jacobi–Davidson and conjugate gradients for the partial symmetric eigenproblem

To compute the smallest eigenvalues and associated eigenvectors of a real symmetric matrix, we consider the Jacobi–Davidson method with inner preconditioned conjugate gradient iterations for the arising linear systems. We show that the coefficient matrix of these systems is indeed positive definite with the smallest eigenvalue bounded away from zero. We also establish a relation between the residual norm reduction in these inner linear systems and the convergence of the outer process towards the desired eigenpair. From a theoretical point of view, this allows to prove the optimality of the method, in the sense that solving the eigenproblem implies only a moderate overhead compared with solving a linear system. From a practical point of view, this allows to set up a stopping strategy for the inner iterations that minimizes this overhead by exiting precisely at the moment where further progress would be useless with respect to the convergence of the outer process. These results are numerically illustrated on some model example. Direct comparison with some other eigensolvers is also provided. Copyright © 2001 John Wiley & Sons, Ltd.     

A self-adaptive trust region method with line search based on a simple subproblem model

In this paper, based on a simple model of the trust region subproblem, we propose a new self-adaptive trust region method with a line search technique for solving unconstrained optimization problems. By use of the simple subproblem model, the new method needs less memory capacitance and computational complexity. And the trust region radius is adjusted with a new self-adaptive adjustment strategy which makes full use of the information at the current point. When the trial step results in an increase in the objective function, the method does not resolve the subproblem, but it performs a line search technique from the failed point. Convergence properties of the method are proved under certain conditions. Numerical experiments show that the new method is effective and attractive for large-scale optimization problems.