基于行为法多智能体系统构形控制研究

为实现多智能体系统的构形控制,针对二阶多智能体系统,采用了一种基于智能体行为的控制算法,这种控制算法考虑到智能体的驶向目标行为和构形维持行为,可以有效实现智能体相对于期望目标的构形控制,由于采用该算法使得多智能体系统中有明确的队形反馈,因而有利于分布式控制和实时控制。智能体的动力学模型采用多智能体问题研究广泛使用的独轮车模型,通过反馈线性化方法,将这种非线性模型转化成了实用的双积分系统模型。通过Matlab仿真验证了算法的有效性,结果表明控制器参数整定简单,具有很好的稳定性和鲁棒性。     

直线电机的非参数模型直接自适应预测控制

将基于紧格式线性化的非参数模型直接自适应预测控制方法应用到直线电机速度和位置控制中.控制器的设计是直接基于伪偏导数的估计和预报,而伪偏导数信息则足通过参数估计算法和预报算法利用I/O数据在线导出.仿真演示了该方法对电机这种不确知动态非线性系统的有效性和抗干扰能力.     

非线性HAMMERSTEIN系统的预测控制及其pH过程应用

本文基于非线性离散Hammerstein模型,开发了一种非线性Hammerstein系统预测控制(Non-Linear Hammerstein Predic- tive Control,NLHPC)算法。遵循预测控制策略,该算法利用Hammerstein模型进行输出预测。理论分析结果表明,该算法不仅具有好的稳定性和鲁棒性,而且其自身具有积分作用。在一台工业PC机上实现了该NLHPC算法,并用于具有强非线性的酸碱中和过程实验装置pH值的控制。实验结果表明NLHPC有着比工业界常用的非线性PID控制(nonlinear PID,NL-PID)更好的控制性能。     

基于近似模型的多层模糊CMAC自适应非线性控制

针对非线性离散时间系统的控制问题,提出了一种基于近似模型的多层模糊CMAC自适应控制方法.采用多层模糊CMAC对非线性函数进行逼近,并提出了一种新的神经网络学习算法来保证权值的有界性.由于无需满足PE条件,所以文中提出的方法对于离散时间系统的神经网络控制问题具有实际价值.     

Finite-time consensus for multi-agent networks with unknown inherent nonlinear dynamics

The objective of this paper is to analyze the finite-time convergence of a nonlinear but continuous consensus algorithm for multi-agent networks with unknown inherent nonlinear dynamics. Due to the existence of the unknown inherent nonlinear dynamics, the stability analysis and the finite-time convergence analysis are more challenging than those under the well-studied consensus algorithms for known linear systems. For this purpose, we propose a novel comparison based tool. By using this tool, it is shown that the proposed nonlinear consensus algorithm can guarantee finite-time convergence if the directed switching interaction graph has a directed spanning tree at each time interval. Specifically, the finite-time convergence is shown by comparing the closed-loop system under the proposed consensus algorithm with some well-designed closed-loop system whose stability properties are easier to obtain. Moreover, the stability and the finite-time convergence of the closed-loop system using the proposed consensus algorithm under a (general) directed switching interaction graph can even be guaranteed by the stability and the finite-time convergence of some well-designed nonlinear closed-loop system under some special directed switching interaction graph. This provides a stimulating example for the potential applications of the proposed comparison based tool in the stability analysis of linear/nonlinear closed-loop systems by making use of known results in linear/nonlinear systems.     

一类非线性系统基于Backstepping的自适应鲁棒神经网络控制

针对一类未知非线性系统提出了一种基于Backstepping的自适应神经网络控制方法, 放松了满足匹配条件, 要求神经网络逼近误差的边界已知等一些限制性的假设. 扩展了自适应backstepping和自适应神经控制的适用范围, 整个闭环系统表明是最终一致有界的, 跟踪误差收敛于原点的一个大小可调的邻域.     

基于小波网络的非线性系统建模与控制

提出了种基于小波网络的非线性系统的建模和控制方法。使用小波网络对未知控制系统建立一步预测模型,基于Ｄｓａｖｉｄｏｎ最小二乘法得到自适应控制律。小波网络的权值由广义递推最小二乘法来学习,尺度参数和平移参数通过稳定的Ｄａｖｉｄｏｎ最小二乘法来获得。仿真结果表明了该方法的有效性。     

一类非线性系统的自适应模糊滑模控制

对一类具有不确定性的非线性系统,根据滑模控制原理并利用模糊系统的逼近能力,提出了一种自适应模糊滑模控制系统的设计方法.控制结构中采用模糊系统自适应朴偿过程的不确定性.利用李雅普诺夫理论,证明了控制算法是全局稳定的,跟踪误差可收敛到零的一个邻域内.     

MIMO系统的多模型预测控制

针对非线性多变量系统提出一种多模型预测控制(MMPC)策略.首先给出一种多模型辨识方法,利用模糊满意聚类算法将复杂非线性系统划分为若干子系统,并获得多个线性模型,通过模型变换得出全局系统模型,接着对全局MIMO系统设计MMPC,并进行了系统的性能分析,最后以pH中和过程为例,通过仿真研究验证了辨识和控制算法的有效性.     

Improved adaptive fuzzy backstepping control of a magnetic levitation system based on Symbiotic Organism Search

Magnetic levitation systems have become very important in many applications. Due to their instability and high nonlinearity, such systems pose a challenge to many researchers attempting to design high-performance and robust tracking control. This paper proposes an improved adaptive fuzzy backstepping control for systems with uncertain input nonlinear function (uncertain parameters and structure), and applies it to a magnetic levitation system, which is a typical representative of such systems. An adaptive fuzzy system is used to approximate unknown, partially known or uncertain input nonlinear functions of a magnetic levitation system. An adaptation law is obtained based on Ljapunov analysis in order to guarantee closed-loop stability and good tracking performance. Initial adaptive and control parameters have been initialized with Symbiotic Organism Search optimization algorithm, due to strong non-linearity and instability of the magnetic levitation system. The theoretical background of the proposed control method is verified with a simulation study and implementation on a laboratory experimental application.     

不确定系统的变结构鲁棒自适应控制及其在机械手控制中的应用

本文针对一类不确定非线性系统,通过状态微分同坯变换和反馈控制建立了系统的变结构鲁棒控制设计过程,并在此基础上提出了一种新型的变结构鲁棒自适应控制算法。该算法优点是:1)不需要确切知道系统不确定性,也不需要知道不确定性的界,而是利用自适应规律对系统不确定性范围进行在线估计;2)能够保证系统获得较为满意的动态性能;3)控制规律是连续性的,因而避免了一般变结构系统中的不连续控制所导致的颤振现象。为了说明本文所提算法的正确性,本文还以二连杆机械手为例讨论了其终端夹持不定载荷时的轨迹跟踪问题。     

模糊/神经自适应控制及其在非线性系统中的应用

针对连续未知非线性系统,提出一种基于观测器并保证稳定性和有界性的自适应模糊历中经算法。本算法利用T—S模糊系统或者神经网络径向基函数构成间接自适应控制器,其参数根据控制率和自适应率进行在线调整,并利用Lyapunov综合法确保对非线性环节渐进跟踪的稳定性。最后,通过对倒立摆系统的仿真,证明该算法在非线性系统控制中应用的可行性。     

非线性系统模糊神经网络变结构自适应控制

在非线性系统的模糊动力学模型基础上,提出一种模糊神经网络变结构自适应控制器;网络的结构根据非线性系统特性动态构成,基于该网络提出非线性预测器,基于梯度法提出了一种网络参数学习算法,并分析了收敛性及其性质。将网络预测器与参数学习算法相结合,构成自适应控制算法,证明了算法的收敛性。仿真结果证实了算法的有效性。     

Nonlinear Feedback Control for the Lorenz System

The Lorenz system is well known for its ability to produce chaotic motion and the control problem of this system has attracted much attention in recent years. In this paper, control of the Lorenz chaotic systems based on a nonlinear feedback technique is presented. The objective of control is two-fold: one is to drive the system to one of equilibrium points associated with uncontrolled chaotic motion and the other is to let one of the closed-loop system states track a given signal. The controllers designed here are based on exact linearization theory of nonlinear systems and can regulate the closed-loop system states globally to a given point. Finally, illustrative examples show the effectiveness of the proposed design method.     

Asymptotic rejection of unknown sinusoidal disturbances in nonlinear systems

This paper deals with global disturbance rejection of nonlinear systems. The disturbance is assumed to be sinusoidal with completely unknown phases, amplitude, and frequencies, but the number of distinct frequencies or the order of the corresponding unknown linear exosystem is known. Different from the common structural assumptions of nonlinear systems needed in literature for disturbance rejection of nonlinear systems, the proposed method only requires the information of control design with a known Lyapunov function when the system is disturbance-free, and a mild assumption needed for internal model design. The proposed disturbance rejection algorithm extends complete global rejection of unknown sinusoidal disturbances for nonlinear dynamic systems beyond the common nonlinear models such as the strict feedback forms and the output feedback forms.     

非线性系统鲁棒自校正控制间接算法

提出一种非线性系统鲁棒自校正控制间接算法．借助于神经网的作用，有效地辨识系统的建模误差，其辨识结果在控制算法中加以补偿，于是，使基于低阶线性模型的自校正控制算法有效地应用于复杂的非线性系统．文中给出了算法的鲁棒性分析和仿真结果．     

Adaptive Control Scheme for Large‐Scale Interconnected Systems Described by Hammerstein Models

This paper proposes an adaptive algorithm for the online control of discrete‐time large‐scale nonlinear systems, which reduces the noise effects acting on the system output (regulation problem) and allows the system output to keep track of a time‐varying trajectory (tracking problem). We consider a large‐scale nonlinear system that can be decomposed into single‐input single‐output (SISO) interconnected nonlinear subsystems with known structure variables (orders, delays) and unknown time‐varying parameters. Each interconnected subsystem is described by block‐oriented models, specifically a discrete‐time Hammerstein model. Parameter adaptation is performed using a recursive parametric estimation algorithm based on the adjustable model method and the least squares techniques. Simulation results of an interconnected petroleum process are provided to demonstrate the effectiveness of the developed control scheme.     

Data-based approximate policy iteration for affine nonlinear continuous-time optimal control design

This paper addresses the model-free nonlinear optimal control problem based on data by introducing the reinforcement learning (RL) technique. It is known that the nonlinear optimal control problem relies on the solution of the Hamilton–Jacobi–Bellman (HJB) equation, which is a nonlinear partial differential equation that is generally impossible to be solved analytically. Even worse, most practical systems are too complicated to establish an accurate mathematical model. To overcome these difficulties, we propose a data-based approximate policy iteration (API) method by using real system data rather than a system model. Firstly, a model-free policy iteration algorithm is derived and its convergence is proved. The implementation of the algorithm is based on the actor–critic structure, where actor and critic neural networks (NNs) are employed to approximate the control policy and cost function, respectively. To update the weights of actor and critic NNs, a least-square approach is developed based on the method of weighted residuals. The data-based API is an off-policy RL method, where the “exploration” is improved by arbitrarily sampling data on the state and input domain. Finally, we test the data-based API control design method on a simple nonlinear system, and further apply it to a rotational/translational actuator system. The simulation results demonstrate the effectiveness of the proposed method.     

非线性系统基于I/O扩展线性化的预测控制算法

本文论述单变量非线性系统基于I/O扩展线性化的预测控制算法,这是一种多层的控制策略。首先,设计一个静态的非线性状态反馈,以使闭环系统是I/O扩展线性的;然后,对该闭环系统设计预测控制算法。     

带有不匹配干扰的二阶多自主体系统有限时间包容控制

针对多自主体系统群集运动问题,本文研究了带有不匹配干扰的二阶系统有限时间包容控制.运用现代控制理论,设计了非线性观测器,对系统未知状态和干扰进行估计.在状态估计的基础上,构建了基于干扰观测器的多自主体系统的协同控制算法.应用代数图论和齐次性理论等方法,分析了二阶多自主体系统有限时间包容控制.数据仿真中应用基于观测器的包容控制算法,使得系统的运动状态最终都收敛到由多个领导者所围成的目标区域中,验证了本文结果的有效性.