窄带随机噪声作用下强非线性Duffing-Rayleigh振子的响应

将参数变换法和随机多尺度法结合起来,研究窄带随机噪声激励下强非线性Duffing-Rayleigh振子的响应及稳定性问题．首先借助参数变换思想引入小参数,然后用多尺度法分离了系统的快变量,最后由摄动法和矩方程法得到了系统的稳态响应．并利用Routh-Hurwitz准则得到了稳态解稳定的充要条件．理论分析与数值计算表明：在一定条件下,系统存在两个稳定的稳态解．数值模拟的结果表明：参数变换法结合随机多尺度法研究强非线性随机系统的响应、稳定性等问题是有效的．     

脉冲微分混沌系统动力学数值模拟与分析

由于脉冲微分混沌系统具有复杂的性态,在理论分析时具有一定的难度,而数值分析在一定程度上可以提供一些指导,所以数值模拟方法成为脉冲微分混沌系统研究的重要手段.该文设计了脉冲微分混沌系统的动力学分析算法,并将数值解以可视化的形式展现,绘制出方程组解的相图、分岔图、Poincaré截面.以具有Holling type-II功...     

随机Duffing-van der Pol系统响应的Chebyshev多项式逼近

对一类含随机参数的Dulling-van der Pol系统,运用Chebyshev多项式逼近法,将其转化成等价的确定性扩阶系统;通过求解等价系统在谐和激励下的稳态响应,可得Duffing-van der Pol系统相应的稳态随机响应,研究了当谐和激励的振幅变化时,含随机参数的Dulling-van der Pol系统的对称破裂分岔和倍周期分岔．数值模拟结果与数值解比较表明：正交多项式逼近法能有效地解决此类非线性随机动力系统的响应问题．     

解非线性等式约束优化问题的新锥模型信赖域算法术

应用新锥模型信赖域子问题解非线性等式约束问题,提出了一个解此问题的新锥模型信赖域算法,证明了新算法的全局收敛性,并进行了数值比较实验．理论与数值结果表明这个算法是一个值得关注的有效算法．     

Painlevé方程解的渐近性态的数值分析方法

Painlevé方程是六类重要的二阶代数微分方程,它们的发展一直受到人们的关注.解的渐近性态是重要的研究方向.由于解的渐近性态难以直接观察出来,所以我们用微分方程数值解研制出Painlevé方程解的渐近性态的分析系统.通过此系统对Painlevé方程解的渐近性态进行分析,已经得到一些结果,部分结果与有关文献的结果[1]相当吻合,进而为从理论上找出具体的相关性质提供了方法和依据.     

非线性压电振动能量采集器的振动特性与实验研究

为了提高线性压电振动能量采集器的输出特性,在线性压电振动能量采集器悬臂梁末端引入Duffing非线性磁力,构造了一种双稳态非线性压电振动能量采集器;综合考虑能量采集器的动态振型与轴向应变分布情况,建立了系统非线性机电耦合集总参数运动控制模型,并利用4阶、5阶Runge-Kutta算法对能量采集器的非线性振动特性进行了数值模拟;利用谐波平衡法计算获得了能量采集器的幅频响应方程,数值分析了激励频率、激励幅值以及磁铁间距等对系统非线性振动特性的影响,发现双稳态运动可以极大地提高能量采集器的频率响应范围和能量采集效率,并且能量采集器在低频、低幅值激励情况下可以产生大幅值周期运动;最后,通过实验对数值计算结果进行了验证.     

最小欧氏距离下（1＋1）竞争小生境遗传算法

探讨了避开事先了解和设定任何小生境相关参数的小生境方法.考虑NGA进化的特点,利用最小欧氏距离下的成对个体具有性态相似性及其大概率地同属同一小生境的特点,采用改进的进化算子建立(1 1)模拟自然小生境内性态相似个体的竞争机制,各个小生境内多对性态相似个体的竞争能够保证各小生境的同时进化,同时引入整体解空间的动态交叉和变异概率来保持群体的多样性,构造了一种全新的小生境算法.4个多峰函数优化数值试验结果证明此方法稳定、显效.     

反馈非线性系统随机梯度辨识算法及其收敛性

反馈非线性受控自回归系统是由前向通道的受控自回归模型和反馈通道的静态非线性构成, 这类系统经过参数化后得到双线性参数辨识模型. 本文通过对辨识模型中双线性参数乘积项进行分解, 基于梯度搜索原理, 提 出了反馈非线性系统的随机梯度辨识算法. 为了改善随机梯度算法的收敛速度, 引入遗忘因子, 文章给出了遗忘因子随机梯度算法, 利用随机过程理论, 建立了随机梯度算法的参数估计收敛定理, 证明了算法的收敛性. 最后, 通过数值仿真验证了算法的有效性.     

Qi系统的Hopf分叉分析与幅值控制

通过非线性状态反馈,不改变Hopf分叉点,实现对四维Qi系统极限环的幅值控制．推导出Qi系统在第一类非零平衡点上产生Hopf分叉的条件,绘制第一类平衡点的分叉图．采用washout filter非线性控制律,利用中心流形定理对受控系统降维,得到极限环的幅值与控制增益之间的近似解析式．通过数值模拟以及幅值解析解与数值解的比较,验证幅值预测的正确性与控制的有效性．     

非线性弦振动方程的多辛算法

利用Hamiltonian空间体系下的多辛理论研究了非线性弦微小横向振动问题的数值解法．基于Bridges意义下的多辛积分理论,首先推导了非线性弦振动方程的一阶多辛偏微分方程组及其多种守恒律,随后构造了一种等价于Box多辛格式的新隐式多辛格式,最后,运用该多辛格式对非线性弦振动方程进行了数值模拟,并将模拟结果与吕克璞等人得到的解析解进行比较．数值实验结果显示利用本文构造的多辛格式得到的数值解与吕克璞等人得到的解析解非常接近,这说明该多辛格式能够较为精确地模拟非线性弦振动问题,同时数值结果也反映出了多辛方法的两大优点：精确的保持多种守恒律和良好的长时间数值行为．     

Neural-Network-Based Control for Discrete-Time Nonlinear Systems with Input Saturation Under Stochastic Communication Protocol

In this paper, an adaptive dynamic programming (ADP) strategy is investigated for discrete-time nonlinear systems with unknown nonlinear dynamics subject to input saturation. To save the communication resources between the controller and the actuators, stochastic communication protocols (SCPs) are adopted to schedule the control signal, and therefore the closed-loop system is essentially a protocol-induced switching system. A neural network (NN)-based identifier with a robust term is exploited for approximating the unknown nonlinear system, and a set of switch-based updating rules with an additional tunable parameter of NN weights are developed with the help of the gradient descent. By virtue of a novel Lyapunov function, a sufficient condition is proposed to achieve the stability of both system identification errors and the update dynamics of NN weights. Then, a value iterative ADP algorithm in an offline way is proposed to solve the optimal control of protocol-induced switching systems with saturation constraints, and the convergence is profoundly discussed in light of mathematical induction. Furthermore, an actor-critic NN scheme is developed to approximate the control law and the proposed performance index function in the framework of ADP, and the stability of the closed-loop system is analyzed in view of the Lyapunov theory. Finally, the numerical simulation results are presented to demonstrate the effectiveness of the proposed control scheme.      

Optimal control of unknown nonaffine nonlinear discrete-time systems based on adaptive dynamic programming

An intelligent-optimal control scheme for unknown nonaffine nonlinear discrete-time systems with discount factor in the cost function is developed in this paper. The iterative adaptive dynamic programming algorithm is introduced to solve the optimal control problem with convergence analysis. Then, the implementation of the iterative algorithm via globalized dual heuristic programming technique is presented by using three neural networks, which will approximate at each iteration the cost function, the control law, and the unknown nonlinear system, respectively. In addition, two simulation examples are provided to verify the effectiveness of the developed optimal control approach.     

Adaptive dynamic programming and optimal control of nonlinear nonaffine systems

In this paper, a novel optimal control design scheme is proposed for continuous-time nonaffine nonlinear dynamic systems with unknown dynamics by adaptive dynamic programming (ADP). The proposed methodology iteratively updates the control policy online by using the state and input information without identifying the system dynamics. An ADP algorithm is developed, and can be applied to a general class of nonlinear control design problems. The convergence analysis for the designed control scheme is presented, along with rigorous stability analysis for the closed-loop system. The effectiveness of this new algorithm is illustrated by two simulation examples.     

Dual iterative adaptive dynamic programming for a class of discrete-time nonlinear systems with time-delays

In this paper, a new dual iterative adaptive dynamic programming (ADP) algorithm is developed to solve optimal control problems for a class of nonlinear systems with time-delays in state and control variables. The idea is to use the dynamic programming theory to solve the expressions of the optimal performance index function and control. Then, the dual iterative ADP algorithm is introduced to obtain the optimal solutions iteratively, where in each iteration, the performance index function and the system states are both updated. Convergence analysis is presented to prove the performance index function to reach the optimum by the proposed method. Neural networks are used to approximate the performance index function and compute the optimal control policy, respectively, for facilitating the implementation of the dual iterative ADP algorithm. Simulation examples are given to demonstrate the validity of the proposed optimal control scheme.     

Fuzzy adaptive dynamic programming-based optimal leader-following consensus for heterogeneous nonlinear multi-agent systems

In this paper, a novel online iterative scheme, based on fuzzy adaptive dynamic programming, is proposed for distributed optimal leader-following consensus of heterogeneous nonlinear multi-agent systems under directed communication graph. This scheme combines game theory, adaptive dynamic programming together with generalized fuzzy hyperbolic model (GFHM). Firstly, based on precompensation technique, an appropriate model transformation is proposed to convert the error system into augmented error system, and an exquisite performance index function is defined for this system. Secondly, on the basis of Hamilton–Jacobi–Bellman (HJB) equation, the optimal consensus control is designed and a novel policy iteration (PI) algorithm is put forward to learn the solutions of the HJB equation online. Here, the proposed PI algorithm is implemented on account of GFHMs. Compared with dual-network model including critic network and action network, the proposed scheme only requires critic network. Thirdly, the augmented consensus error of each agent and the weight estimation error of each GFHM are proved to be uniformly ultimately bounded, and the stability of our method has been verified. Finally, some numerical examples and application examples are conducted to demonstrate the effectiveness of the theoretical results.

     

Numerical solution of potential flow equations with a predictor-corrector finite difference method

We develop a numerical solution algorithm of the nonlinear potential flow equations with the nonlinear free surface boundary condition.A finite difference method with a predictor-corrector method is applied to solve the nonlinear potential flow equations in a two-dimensional (2D) tank.The irregular tank is mapped onto a fixed square domain with rectangular cells through a proper mapping function.A staggered mesh system is adopted in a 2D tank to capture the wave elevation of the transient fluid.The finite difference method with a predictor-corrector scheme is applied to discretize the nonlinear dynamic boundary condition and nonlinear kinematic boundary condition.We present the numerical results of wave elevations from small to large amplitude waves with free oscillation motion,and the numerical solutions of wave elevation with horizontal excited motion.The beating period and the nonlinear phenomenon are very clear.The numerical solutions agree well with the analytical solutions and previously published results.     

An advanced equation assembly module

We present an advanced equation assembly module which has been developed for the simulation of semiconductor devices based on the Finite Boxes discretization scheme and is currently used in the general purpose device and circuit simulator Minimos-NT. Such simulations require the solution of a specific set of nonlinear partial differential equations which are discretized on a grid. The resulting nonlinear problem is solved by a damped Newton algorithm that demands the solution of a linear equation system at each step. The presented module is responsible for assembling these systems and takes into account several requirements of the simulation process. The underlying concepts, namely the representation of boundary conditions, physically motivated variable transformation, preelimination and numerical conditioning, are presented together with some examples.     

A neural-network-based iterative GDHP approach for solving a class of nonlinear optimal control problems with control constraints

In this paper, a novel neural-network-based iterative adaptive dynamic programming (ADP) algorithm is proposed. It aims at solving the optimal control problem of a class of nonlinear discrete-time systems with control constraints. By introducing a generalized nonquadratic functional, the iterative ADP algorithm through globalized dual heuristic programming technique is developed to design optimal controller with convergence analysis. Three neural networks are constructed as parametric structures to facilitate the implementation of the iterative algorithm. They are used for approximating at each iteration the cost function, the optimal control law, and the controlled nonlinear discrete-time system, respectively. A simulation example is also provided to verify the effectiveness of the control scheme in solving the constrained optimal control problem.     

Neural-Network-Based Near-Optimal Control for a Class of Discrete-Time Affine Nonlinear Systems With Control Constraints

In this paper, the near-optimal control problem for a class of nonlinear discrete-time systems with control constraints is solved by iterative adaptive dynamic programming algorithm. First, a novel nonquadratic performance functional is introduced to overcome the control constraints, and then an iterative adaptive dynamic programming algorithm is developed to solve the optimal feedback control problem of the original constrained system with convergence analysis. In the present control scheme, there are three neural networks used as parametric structures for facilitating the implementation of the iterative algorithm. Two examples are given to demonstrate the convergence and feasibility of the proposed optimal control scheme.     

Evolutionary-Programming-Based Kalman Filter for Discrete-Time Nonlinear Uncertain Systems

Some observations and improvements on the conventional Kalman filtering scheme to function properly are presented. The improvements can be achieved using the minimal principle evolutionary programming (EP) technique. A new linearization methodology is presented to obtain the exact linear models of a class of discrete-time nonlinear time-invariant systems at operating states of interest, so that the conventional Kalman filter can work for the nonlinear stochastic systems. Furthermore, a Kalman innovation filtering algorithm and such an algorithm based on the evolutionary programming optimal-search technique are proposed in this paper for discrete-time time-invariant nonlinear stochastic systems with unknown-but-bounded plant uncertainties and noise uncertainties to find a practically implementable “best” Kalman filter. The worst-case realization of the discrete-time nonlinear stochastic uncertain systems represented by the interval form with respect to the implemented “best” nominal filter is also found in this paper for demonstrating the effectiveness of the proposed filtering scheme.