非线性系统控制的逆系统方法（Ⅱ）：多变量控制理论

介绍基于逆系统方法的多变量非线性控制理论，包括高阶微分方程组系统的控制设计方法，多输入多输出状态方程系统的控制设计方法，可解耦性与可镇定性分析，应用与相关理论研究等内容。     

神经网络广义逆系统控制

提出适合于高阶非线性系统线性化解耦的广义逆系统，它与被控系统复合后，不但能实现原系统的线性化和解耦，而且通过合理地设计逆系统，可使伪线性复合系统的极点在复平面上任意配置，进一步提出由静态神经网络和若干积分惯性等线性环节组成的神经网络广义逆系统，为模型未知且内部状态不易测量的高阶非线性系统的线性化解耦控制提供一条有效途径，进一步拓展了神经网络逆系统控制方法的适用范围。     

神经网络广义逆系统控制

提出适合于高阶非线性系统线性化解耦的广义逆系统.它与被控系统复合后,不但能实现原系统的线性化和解耦,而且通过合理地设计逆系统,可使伪线性复合系统的极点在复平面上任意配置.进一步提出由静态神经网络和若干积分惯性等线性环节组成的神经网络广义逆系统,为模型未知且内部状态不易测量的高阶非线性系统的线性化解耦控制提供一条有效途径,进一步拓展了神经网络逆系统控制方法的适用范围.     

大型空气重介流化床逆系统控制与仿真

1　引言我国和国外很多煤炭储藏地严重缺水 ,传统的湿法选煤工艺受到限制 .流化态分离技术是矿物分离的一种新型高效干法分选方法 .目前的研究主要在选煤行业 ,称之为“空气重介流化床干法选煤技术”,具有重大经济价值和广阔的应用前景 ,因而受到产煤大国的高度重视 .我国在气     

非线性锅炉-汽轮机系统的鲁棒控制

本文采用环路成形Ｈ∞控制方法对一非性性锅炉－汽轮机系统进行设计,为了能方便地在实际中工程中实现复杂的控制器,本文提出用多变理ＰＩＤ控制器逼近所得控制器的方法,最终简化的由位于主对角通道的三个ＰＩ控制器及位于次对角通道的一个ＰＩ控制器实现,仿真表明所得控制器具有较好的信号跟踪、抗干扰性能,并能在较大范围操作点工作,从而具 鲁棒性。     

非线性系统控制的逆系统方法（Ⅰ）

介绍非线性系统控制的逆系统方法及相应的单一非线性系统的控制理论。包括：逆系统方法的基本概念和设计原理，单变量系统α阶积分逆的设计方法，连续单变量系统的系统的控制，逆系统在一类分布参数系统和离散时间系统上的推广示例。     

连续非线性系统的神经网络α阶逆系统控制方法

对连续非线性系统,采用连续系统的设计方法,利用神经网络对非线性函数的逼近能力加上用来表征系统动态特性的若干积分器,构造工程上可实现的原系统的神经网络α阶逆系统(将α阶逆系统直接与原系统串接,构成α阶积分伪线性复合系统),并对α阶积分伪线性复合系统设计线性控制器,实现对原非线性系统的有效控制.仿真结果表明,该方法适用于较一般的线性、非线性连续系统,且结构简单,易于工程实现.     

基于SVM逆系统控制方法的电液变气门系统的升程控制

针对电液可变气门系统的升程控制,提出了基于支持向量机(SVM)的α阶逆系统控制模型.该方法适合高阶非线性系统的控制问题.根据系统的输入输出,离线建立变气门逆系统的辨识模型,然后将SVM逆系统串接在原系统之前,构成伪线性系统.仿真结果表明:基于SVM的α阶逆系统控制模型,对电液变气门系统的升程,表现出了良好的控制特性.     

基于逆系统方法的广义非线性系统控制及电力系统应用

对一般广义非线性系统的反馈线性化问题,通过将代数变量视为虚拟输入,给出了构造性递归求逆算法,实现了系统的精确反馈线性化.作为该算法的应用,讨论了带有非线性负荷的三节点电力系统的励磁控制问题,设计了非线性励磁控制器.仿真结果表明了方法的有效性.     

一般非线性系统的构造性逆系统方法

针对由于逆系统方法之求逆算法中求解隐函数方程的困难, 通过消元法避开对隐函数方法的求解, 提出了一般非线性多变量系统的构造性求逆算法. 在此基础上讨论对逆系统方法的相应改进, 通过动态补偿进一步提出了构造性逆系统方法. 这种改进使得逆系统方法原则上可以构造性地适用于任意足够光滑的非线性系统.     

GA-based nonlinear predictive switching control for a boiler-turbine system

In this paper,the problem of designing a controller for a highly coupled constrained nonlinear boilerturbine system is addressed with a predictive controller.First,a nonlinear predictive control is imp...     

基于即时学习的非线性系统优化控制

基于数据驱动机制的逆控制是一种非线性系统控制方法,关键问题在于局部逆控制模型的准确性,但尚无校验机制来保证其能否产生期望的输出．为此,提出一种k-VNN即时学习算法,提高了逆控制模型的建模精度．将该算法与性能指标优化策略相结合,在线修正逆控制模型顶估的系统控制量。可得到系统的一步最优控制量。实现非线性系统的跟踪控制,为提高控制系统的泛化能力,提出一种数据库数据更新策略．仿真结果表明了所提出方法的有效性．     

非线性离散系统的贝叶斯-高斯神经网络逆模型设计

针对非线性系统逆模型的学习问题,提出一种基于贝叶斯-高斯神经网络(BGNN)的设计方法.BGNN模型的训练分为两个步骤,首先利用群智能优化算法进行BGNN的离线结构训练:然后用训练好的BGNN模型在线整合历史数据,进行非线性系统逆模型的获取.对水轮发电机组非线性系统进行了BGNN逆模型的仿真,结果表明了BGNN逆模型设计方法具有结构简单、在线辨识效果好等优点,适于非线性离散系统的逆模型设计.     

Adaptive actuator failure compensation for nonlinear MIMO systems with an aircraft control application

A direct adaptive approach is developed for control of a class of multi-input multi-output (MIMO) nonlinear systems in the presence of uncertain failures of redundant actuators. An adaptive failure compensation controller is designed which is capable of accommodating uncertainties in actuator failure time instants, values and patterns. A realistic situation is studied with fixed grouping of actuators and proportional actuation within actuator groups. The adaptive control system is analyzed, to show its desired stability and asymptotic tracking properties in the presence of actuator failure uncertainties. As an application, such an adaptive controller is used for actuator failure compensation of a twin otter aircraft longitudinal model, with design conditions verified and control structure and adaptive laws developed for a nonlinear aircraft dynamic model. The effectiveness of adaptive failure compensation is demonstrated by simulation results.     

Nonlinear system compound inverse control method

A compound neural network is utilized to identify the dynamic nonlinear system. This network is composed of two parts： one is a linear neural network, and the other is a recurrent neural network. Based on the inverse theory a compound inverse control method is proposed. The controller has also two parts： a linear controller and a nonlinear neural network controller. The stability condition of the closed-loop neural network-based compound inverse control system is demonstrated .based on the Lyapunov theory. Simulation studies have shown that this scheme is simple and has good control accuracy and robustness.     

Nonlinear system compound inverse control method

A compound neural network is utilized to identify the dynamic nonlinear system.This network is composed of two parts: one is a linear neural network,and the other is a recurrent neural network.Based on the inverse theory a compound inverse control method is proposed.The controller has also two parts:a linear controller and a nonlinear neural network controller.The stability condition of the closed-loop neural network-based compound inverse control system is demonstrated based on the Lyapunov theory.Simulation studies have shown that this scheme is simple and has good control accuracy and robustness.     

Benchmark problems for nonlinear system identification and control using Soft Computing methods: Need and overview

Using benchmark problems to demonstrate and compare novel methods to the work of others could be more widely adopted by the Soft Computing community. This article contains a collection of several benchmark problems in nonlinear control and system identification, which are presented in a standardized format. Each problem is augmented by examples where it has been adopted for comparison. The selected examples range from component to plant level problems and originate mainly from the areas of mechatronics/drives and process systems. The authors hope that this overview contributes to a better adoption of benchmarking in method development, test and demonstration.     

即时学习算法在非线性系统迭代学习控制中的应用

运用即时学习算法来解决一类非线性系统的迭代学习控制初值问题。对于任何类型的迭代学习控制算法，即时学习算法都能有效地估计初始控制量，减小了初始输出误差，加快了算法的收敛速度，使得经过有限次迭代后系统输出能严格跟踪理想信号。对机器人系统的仿真结果表明了该方法的有效性。     

具有输入齿隙的一类非线性不确定系统自适应鲁棒控制

针对一类具有未知输入齿隙、参数不确定以及未建模动态和干扰的非线性系统,设计了自适应鲁棒控制器.将齿隙非线性模型等价表示为具有有界建模误差的全局线性化模型,在此基础上设计了包含自适应模型补偿、反馈稳定和鲁棒反馈3部分的自适应鲁棒控制器,并给出了系统动态跟踪误差和稳态误差指标.理论分析证明,闭环控制系统信号有界且跟踪误差在任意期望的精度范围内,仿真研究验证了所提出方法的有效性.     

Chattering free adaptive fuzzy terminal sliding mode control for second order nonlinear system

A novel fuzzy terminal sliding mode control （FTSMC） scheme is proposed for position tracking of a class of second-order nonlinear uncertain system. In the proposed scheme, we integrate input-output linearization technique to cancel the nonlinearities. By using a function-augmented sliding hyperplane, it is guaranteed that the output tracking error converges to zero in finite time which can be set arbitrarily. The proposed scheme eliminates reaching phase problem, so that the closed-loop system always shows invariance property to parameter uncertainties. Fuzzy logic systems are used to approximate the unknown system functions and switch item. Robust adaptive law is proposed to reduce approximation errors between true nonlinear functions and fuzzy systems, thus chattering phenomenon can be eliminated. Stability of the proposed control scheme is proved and the scheme is applied to an inverted pendulum system. Simulation studies are provided to confirm performance and effectiveness of the proposed control approach.