On near optimal neural control of multiple-input nonlinear systems

It has been a common consensus that general techniques for stabilization of nonlinear systems are available only for some special classes of nonlinear systems. Control design for nonlinear systems with uncertain components is usually carried out on a per system basis, especially when physical control constraints, and certain control performance measures such as optimum time control are imposed. Elegant adaptive control techniques are difficult to apply to this type of problems. A new neural network based control design is proposed and presented in this paper to deal with a special class of uncertain nonlinear systems with multiple inputs. The desired system dynamics are analyzed and utilized in the process of the proposed intelligent control design. The theoretical results are provided to justify the design procedures. The simulation study is conducted on a second-order bilinear system with two inputs and uncertainties on its parameters. The simulation results indicate that the proposed design approach is effective.     

A framework for design of scheduled output feedback model predictive control

We present a stabilizing scheduled output feedback Model Predictive Control (MPC) algorithm for constrained nonlinear systems with large operating regions. We design a set of local output feedback predictive controllers with their estimated regions of stability covering the desired operating region, and implement them as a single scheduled output feedback MPC which on-line switches between the set of local controllers and achieves nonlinear transitions with guaranteed stability. This algorithm provides a general framework for scheduled output feedback MPC design.     

Stability and control of nonlinear systems described by retarded functional equations: a review of recent results

This paper reports on recent results in a series of the work of the authors on the stability and nonlinear control for general dynamical systems described by retarded functional differential and difference equations. Both internal and external stability properties are studied. The corresponding Lyapunov and Razuminkhin characterizations for input-to-state and input-to-output stabilities are proposed. Necessary and sufficient Lyapunov-like conditions are derived for robust nonlinear stabilization. In particular, an explicit controller design procedure is developed for a new class of nonlinear time-delay systems. Lastly, sufficient assumptions, including a small-gain condition, are presented for guaranteeing the input-to-output stability of coupled systems comprised of retarded functional differential and difference equations.     

一类高阶非线性不确定系统自适应稳定控制

研究了一类高阶非线性不确定性系统的自适应稳定控制设计问题.因该系统的非线性程度高,其控制系数不等同、符号已知、但数值未知,故在此之前其稳定控制设计问题没有得到解决.本文应用自适应技术,结合设计参数的适当选取,从而得到了设计该类非线性系统状态反馈稳定控制器的新方法,并基于反推技术,给出了稳定控制器的设计步骤.所设计的状态反馈控制器使得闭环系统的状态全局渐近收敛于零,其余闭环信号一致有界.最后通过一个仿真例子说明了控制设计方法的有效性.     

检验门限协整模型中的线性协整

考虑门限协整回归模型中线性的检验问题.在原假设为线性协整的条件下,构造TSupLM(supremumLagrange multiplier)统计量,并给出了极限分布.Monte Carlo实验研究了SupLM检验的有限样本性能,结果表明SupLM检验不受回归误差的序列相关性影响,也不受广义的自回归条件异方差GARCH(generalized autoregressiveconditional heteroskedastic)的影响.应用SupLM检验方法检测美国国库券收益率之间的关系,结果表明不同到期时间的国库券收益率之间存在门限协整关系.     

基于广义回归神经网络的传感器非线性误差校正

介绍了径向基函数网络的函数逼近原理和方法,提出了一种基于广义回归神经网络(GRNN)的传感器非线性误差校正方法。通过Matlab的Network Toolbox(神经网络工具箱),GRNN训练程序实现了输出特性曲线逼近。仿真分析表明:GRNN能够很好地满足传感器非线性拟合的要求,网络结构简单,收敛速度快。     

混合编码免疫算法在船舶载重计量的应用

提出一种混合编码免疫辨识算法, 并应用于船舶载重货物计量的模型辨识. 该算法结合免疫算法优越的全局搜索性能与GP算法简洁的结构树编码方法, 将抗体编码为模型结构树编码与模型参数编码的组合, 通过对抗体结构与参数的免疫操作, 实现全局寻优及非线性模型的结构与参数的一体化辨识. 算法不依赖对象的先验知识, 辨识的模型结构简单、易于理解. 仿真及船舶载模型的辨识应用验证了本算法的有效性及较强的非线性逼近能力.     

一类不确定非线性系统的停息时间可调的有限时间镇定

研究了一类带有零动态不确定非线性系统有限时间镇定问题, 给出了具有更强实用性的停息时间可调的有限时间稳定控制设计方法. 基于有限时间稳定的Lyapunov理论和反推技术, 给出了停息时间可调有限时间稳定控制器的设计步骤. 所设计的状态反馈控制器使得闭环系统全局有限时间稳定, 且停息时间可调整, 特别是当系统初始值已知时, 停息时间可以任意调节. 仿真例子验证了本文的主要结论.     

随机非线性系统的输出反馈控制

针对满足线性增长条件的一类随机非线性系统, 本文研究了输出反馈镇定问题. 然而不同于现有的所有文献, 由于线性增长条件中含有不可量测的状态, 引入了一个待定的高增益观测器. 利用反推设计技术, 构造性地给出了一个输出反馈控制器的设计, 通过适当地选取高增益参数, 保证了闭环系统的零解是概率意义下全局渐近稳定的, 输出几乎处处调节于零.     

磁浮列车搭接结构的非线性解耦控制

磁浮列车的搭接结构是一个多输入多输出的非线性系统, 对该系统进行稳定控制是确保磁浮列车可靠性的关键. 本文采用微分几何方法对系统解耦; 采用反馈线性化方法对解耦后的系统进行线性化, 确保了系统的全局稳定性; 对线性化之后的系统采用极点配置的方法进行控制律设计, 使系统的动态性能达到一定的指标; 最后给出了应用此方法的一个实际例子.