非线性电路与系统的脉冲建模及其稳定性

非线性电路和系统作为非线性科学的一部分在各个领域受到关注。在本文中,我们研究了非线性电路系统的脉冲建模和稳定性。首先,鲁里叶系统和时间变化滞后非线性系统的脉冲建模和控制。其次,研究了混沌和滞后脉冲模糊系统的稳定性。第三,研究不确定时变系统的脉冲建模和控制。非线性电路和系统作为非线性科学的一个分岔,近年来在各个领域受到关注。本文的主要内容是关于非线性电路系统的脉冲建模和稳定性的研究。包括蔡司混沌电路的脉冲建模和控制研究。2)研究混沌系脉冲控制的稳定性洛伦兹。3)研究了时变滞后鲁里叶系统和非线性系统的脉冲建模和控制。4)研究了暧昧混沌系和滞后脉冲暧昧系的稳定性。5)不确定时变系统的脉冲建模和控制的研究。     

平衡倒立摆系统的鲁棒稳定性分析

本文介绍了一种非线性系统的频域稳定性判据,这种方法把非线性系统的稳定性总理２转化为级数的收敛问题,文中重点利用该稳定性判据,讨论了倒立摆系统的稳定性问题,所得的理论分析结果与实验数据基本吻合。     

不确定非线性系统的鲁棒稳定性分析

基于泛函分析和算子理论的方法,从输入输出的角度研究了一类不确定反馈非线性系 统的鲁棒稳定性问题.利用K类函数分别描述非线性不确定环节和非线性系统的算子增益,对 非线性反馈互联不确定系统的鲁棒稳定性问题进行了研究.接着利用小增益条件的形式进行等 价变化,就可将反馈不确定非线性系统的稳定性条件转化成系统非线性算子的范数条件.并通 过举例证明了该方法的有效性.     

一类非线性组合大系统的稳定性

本文研究一类比较广泛的非线性组合大系统的稳定性问题.基于矩阵代数Riccati 方程存在对称正定解的新判据(引理2),给出了检验这类非线性组合大系统稳定性的新准 则,所给出的稳定性准则只涉及子系统的稳定性以及子系统之间的互联项,因此,当应用该准 则检验这类非线性组合大系统的稳定性时,避免了构造子系统以及大系统的Lyapunov函数 的困难.     

非线性滞后It(?)随机系统的滞后无关均方渐近稳定性

研究非线性滞后Ito随机系统的滞后无关均方渐近稳定性,将关于线性时滞不等式的Halanay不等式推广到非线性情形,用Lyapunov函数和关于时滞随机系统的比较原理,得到了非线性滞后Ito随机系统滞后无关均方渐近稳定性的一些判据。     

衰减扰动下非线性预测控制系统的鲁棒稳定性

考虑一般形式的非线性预测控制系统,研究带终端约束的稳定预测控制策略在衰减扰动下的鲁棒稳定性问题,首先引入离散非线性系统的输入状态稳定性,然后针对上述预测控制策略得出了系统的稳定性结论。     

基于Volterra级数的离散MIMO非线性闭环系统的稳定性

基于非线性传递函数矩阵的Volterra级数表示,利用多维Z变换,讨论了一类MIMO离散非线性系统的闭环稳定性,给出了直接利用开环稳定性来判别系统闭环稳定性的新方法,并用实例仿真来验证其有效性。     

基于非线性传递函数的非线性闭环控制系统的稳定性研究

基于Ｖｏｌｔｅｒｒａ级数的非线性传递函数表示,针对一类离散非线性系统,提出了直接利用开环稳定性判断系统闭环稳定性的新方法,并用实例仿真验证了此判据的有效性。     

一类多时滞非线性系统的稳定性研究

非线性系统的稳定性是目前自动控制理论研究的一个热点问题。本文通过构造多时滞非线性系统的李雅普诺夫函数,运用线性矩阵不等式方法,研究了一类不确定的时滞系统的稳定性问题,给出了该类系统鲁棒性稳定的充分条件。     

非线性滞后Ito随机系统的滞后无关均方渐近稳定性

研究非线性滞后Ｉｔｏ随机系统的滞后无关均方渐近稳定性，将关于线性时滞不等式的Ｈａｌａｎａｙ不等式推广到非线性情形，用Ｌｙａｐｕｎｏｖ函数和关于时滞随机系统的比较原理，得到了非线性滞后Ｉｔｏ随机系统滞后无关均方渐近稳定性的一些判据。     

基于神经网络的一类非线性系统的稳定自适应控制器设计方法*

针对一类非线性连续时间系统,其中非线性函数未知,提出了一种基于神经网络的稳定自适应控制方案,由于控制律的选择基于Ｌｙａｐｕｎｏｖ稳定性理论,因此,该控制方案不仅能够解决这类非线性系统的跟踪问题。     

Receding horizon control via Bolza-type optimization

This paper deals with the stabilization problem of nonlinear control systems. A variant of the receding horizon control method is proposed which is based on the solution of a certain Bolza problem. The stabilizing property of the method is proved in the case of Lipschitz continuous value function. This version of receding horizon scheme can be considered as a straightforward generalization of the so-called Fake Riccati Equation Technique for nonlinear constrained systems.     

Receding horizon control via Bolza-type optimization

This paper deals with the stabilization problem of nonlinear control systems. A variant of the receding horizon control method is proposed which is based on the solution of a certain Bolza problem. The stabilizing property of the method is proved in the case of Lipschitz continuous value function. This version of receding horizon scheme can be considered as a straightforward generalization of the so-called Fake Riccati Equation Technique for nonlinear constrained systems.     

基于神经网络的一类非线性系统的自适应控制

基于递归神经网络给出了仅含一个非线性环节的一类非线性系统的自适应控制方案。该方案采用递归神经网络辨识非线性系统中的未知非线性环节。沿用广义最小方差自校正控制方法,可以解决非线性环节未知和工作点变化时传统方法无法控制的自适应控制问题。理论分析和仿真结果表明,该方法具有很好的控制效果。     

一种非线性系统的模糊自适应控制

针对一类非线性系统提出一种模糊自适应控制方案，设计中用模糊逻辑系统逼近非线性函数，骨于滑模原理及Ｌｙａｐｕｎｏｖ函数方法给出了闭环系统的稳定性分析。     

Adaptive tracking control for non-affine nonlinear systems with non-affine function possibly being discontinuous

A novel adaptive control scheme is presented for a class of non-affine nonlinear systems with non-affine nonlinear function possibly being discontinuous. A discontinuous condition for non-affine nonlinear systems is present to guarantee the controllability of system. The non-affine nonlinear function is modelled appropriately by using piecewise functions. Based on Lyapunov analysis method, the basic idea of invariant set theory is constructively introduced to prove the boundedness of all the signals in the closed-loop system. Finally, simulation example is provided to demonstrate the effectiveness of the proposed approach.     

分散模型参考自适应控制

本文针对由参数未知、存在有界扰动和非线性关联的子系统组成的大规模互联系统,提出 了一种新的分散模型参考自适应控制法.它适用于孤立子系统传递函数的相对阶次n*i为任 意值的情况.根据李雅普诺夫稳定性理论,文中证明了这种分散自适应控制系统全局稳定的 充分条件.与有关文献所介绍的方法相比,本文的方法可用于n*i>2的场合,因而它更具有 一般性和实用性.     

RBFN-based decentralized adaptive control of a class of large-scale non-affine nonlinear systems

For a class of large-scale decentralized nonlinear systems with strong interconnections, a radial basis function neural network (RBFN) adaptive control scheme is proposed. The system is composed of a class of non-affine nonlinear subsystems, which are implicit function and smooth with respect to control input. Based on implicit function theorem, inverse function theorem and the design idea of pseudo-control, a novel control algorithm is proposed. Two neural networks are used to approximate unknown nonlinearities in the subsystem and unknown interconnection function, respectively. The stability is proved rigidly. The result of simulation validates the effectiveness of the proposed scheme.     

Adaptive discrete-time sliding-mode control of nonlinear systems described by Wiener models

In this paper, we propose an adaptive control scheme that can be applied to nonlinear systems with unknown parameters. The considered class of nonlinear systems is described by the block-oriented models, specifically, the Wiener models. These models consist of dynamic linear blocks in series with static nonlinear blocks. The proposed adaptive control method is based on the inverse of the nonlinear function block and on the discrete-time sliding-mode controller. The parameters adaptation are performed using a new recursive parametric estimation algorithm. This algorithm is developed using the adjustable model method and the least squares technique. A recursive least squares (RLS) algorithm is used to estimate the inverse nonlinear function. A time-varying gain is proposed, in the discrete-time sliding mode controller, to reduce the chattering problem. The stability of the closed-loop nonlinear system, with the proposed adaptive control scheme, has been proved. An application to a pH neutralisation process has been carried out and the simulation results clearly show the effectiveness of the proposed adaptive control scheme.     

Adaptive iterative learning control for nonlinear time-delay systems with periodic disturbances using FSE-neural network

An adaptive iterative learning control scheme is presented for a class of strict-feedback nonlinear time-delay systems, with unknown nonlinearly parameterised and time-varying disturbed functions of known periods. Radial basis function neural network and Fourier series expansion (FSE) are combined into a new function approximator to model each suitable disturbed function in systems. The requirement of the traditional iterative learning control algorithm on the nonlinear functions (such as global Lipschitz c...