基于微粒群算法的非线性系统建模方法研究

针对非线性系统多模型自适应控制中的模型覆盖问题,提出一种基于微粒群算法的多模型建模方法.首先,对非线性系统定义了基于混合逻辑模型的多模型描述,建立了非线性系统的混合线性多模型;然后,基于微粒群优化算法对非线性系统进行优化建模,在保证建模准确性的同时采用最少的子模型逼近非线性系统;最后,通过一个仿真算例表明了该建模方法的有效性.     

电动舵机非线性系统的辨识研究

针对所设计的三闭环电动舵机系统,为了提高电动舵机的控制精度,建立具有良好动态性能的电动舵机控制系统,提出了采用非线性系统辨识的方法,并且利用辨识的参数对电动舵机系统进行补偿;根据电动舵机的数学模型分别采用了前馈补偿的方法和反馈辨识的方法对系统的摩擦进行仿真建模研究,利用Lugre模型对系统的摩擦进行辨识,使得系统能够进行自适应摩擦补偿,并且将建模数据加入到电动舵机样机中进行试验验证,经过试验验证,系统的位置跟踪误差和速度跟踪误差均有大幅度减小,验证了非线性模型的准确性及补偿的有效性;试验结果证明,文章所提出的电动舵机非线性辨识研究方法可以准确地实现摩擦模型的建立,并且从该模型出发进行补偿可以有效地提高电动舵机的控制精度。     

滚珠丝杠式电动舵机系统非线性特性分析

针对速度反馈控制的滚珠丝杠式电动舵机系统,建立更加完整的系统模型,模型中充分考虑间隙和摩擦等非线性因素的影响,并通过数值仿真,观察分析不等间隙和摩擦对系统输出的影响规律。采用LuGre模型和死区模型分别描述电动舵机运行时的摩擦和间隙行为,然后根据传动链结构特点提出非线性研究方案,并根据此方案将非线性环节加入所建立的电动舵机二阶模型中,分别对间隙和摩擦的影响进行仿真分析。仿真结果表明,间隙和摩擦会使伺服系统产生跟踪误差,影响定位精度,甚至影响系统稳定。     

基于区间二型模糊摩擦补偿的鲁棒自适应控制

针对不确定机械系统中普遍存在的摩擦力,由于其非线性和不确定性,传统基于摩擦模型的补偿控制方法难以达到满意的系统性能要求.本文提出基于自适应区间二型（Type-2）模糊逻辑系统对系统摩擦进行补偿建模,并在该摩擦补偿方法的基础上设计出鲁棒自适应控制器,保证系统输出精度,且对摩擦环境的变化具有较强自适应性.区间二型模糊逻辑系统相对于传统一型模糊逻辑系统具有较强的处理不确定性问题的能力,在本文中使用自适应区间二型模糊逻辑系统不断逼近摩擦力,根据李雅普诺夫稳定性理论求出自适应律并证明系统跟踪误差的有界性.在不同摩擦环境下的仿真结果验证了本文所提摩擦建模方法与控制策略的有效性与实用性.     

基于微粒群算法的非线性系统建模方法研究

针对非线性系统多模型自适应控制中的模型覆盖问题,提出一种基于微粒群算法的多模型建模方法.首先,对非线性系统定义了基于混合逻辑模型的多模型描述,建立了非线性系统的混合线性多模型;然后,基于微粒群优化算法对非线性系统进行优化建模,在保证建模准确性的同时采用最少的子模型逼近非线性系统;最后,通过一个仿真算例表明了该建模方法的有效性.

     

基于二次仿射传播聚类的非线性系统多模型LSSVM建模

针对复杂非线性系统单模型建模存在计算量大和精度差的问题,提出一种采用仿射传播聚类的多模型LSSVM建模方法,通过仿射传播聚类对样本数据按输入集和输出集二次聚类划分,并分别建立LSSVM子模型,非线性系统模型通过子模型加权合成.将该方法应用于两电机变频调速系统的张力和速度模型辨识,仿真结果表明,该建模方法具有较高的精度,能准确拟合系统的非线性特性.     

基于神经网络的电机摩擦力混合模型研究

在电机伺服系统优化建模的研究中,要求高精度伺服系统。由于系统摩擦力具有强非线性和非光滑特性,传统的神经网络无法进行有效辨识。将非线性摩擦特性理解成为由稳态部分和突变部分串联构成,以电机伺服系统为对象,引入柔性sigmoid函数描述非线性摩擦特性中的突变部分,并与传统的RBF神经网络串联,构造出描述非线性摩擦特性的神经网络混合模型。仿真结果表明,与传统的RBF神经网络辨识方法相比,模型在输入变化响应下均具有较高的模型精度,从而验证了建模方法的有效性。     

基于Preisach模型的迟滞系统建模与控制

针对一种复杂的非线性系统一迟滞系统，研究了基于KP算子Preisach模型对迟滞系统进行建模的方法。利用Preisach模型与其边界线之间的映射关系，建立了容易在线更新的迟滞模型。基于Preisach模型进行迟滞非线性系统的控制，采用PID方法来控制一类带有未知非线性特性迟滞的单输入单输出非线性系统。对迟滞非线性系统的建模与控制进行的数值仿真研究结果表明，该迟滞非线性系统的建模和控制方法具有理论意义和应用价值。     

含有摩擦补偿的全方位移动机器人自抗扰控制

本文针对全方位移动机器人轨迹追踪中的摩擦补偿问题,提出了一种改进的非线性自抗扰控制器.首先建立了含有经典静态摩擦模型的全方位移动机器人动力学模型.其次,基于该模型设计非线性控制器和线性扩张状态观测器并给出了系统的稳定性分析.通过将模型已知项加入线性扩张状态观测器中得到摩擦力的估计值,并将估计值用于非线性控制器中摩擦补偿部分.为减小摩擦力对机器人低速运动轨迹追踪控制的影响,非线性控制器采用变增益控制器进行轨迹追踪控制.最后通过仿真结果验证本文提出控制器的有效性.     

滑模自适应控制在光电稳定平台中的应用

针对高精度光电伺服稳定平台系统中摩擦和各种非线性干扰对跟踪精度的影响问题,提出了一种基于LuGre摩擦模型的积分型滑模自适应控制算法。首先建立了基于动态LuGre摩擦的伺服系统模型,根据LuGre模型,构造了一个非线性观测器来估计摩擦中的未知状态变量;然后设计积分型滑模自适应控制算法实现摩擦补偿和各种扰动的估计,通过设计最优的反馈控制律,保证了积分型滑模的收敛速度,并引入自适应思想设计滑模控制器,有效的减弱了滑模控制中的颤抖现象;最后利用Lyapunov理论证明了控制系统的稳定性。仿真结果表明:所提方法有效的抑制了摩擦等各种干扰对稳定平台系统的影响,在提高系统跟踪精度的同时增强了系统的鲁棒性能,该方法也简化了设计过程,具有一定的应用价值。     

Research on nonlinear friction compensation of harmonic drive in gimbal servo-system of DGCMG

The purpose of this paper is to present our results in overcoming the influence of the nonlinear friction afforded by harmonic drive to the gimbal servo-system of double-gimbal control momentum gyro (DGCMG). The existence of compliance and oscillation inherent in harmonic drive systems, and the lack of any technical information on the internal dynamics of the transmission, make the development of friction compensation in harmonic drive system extremely challenging. In this paper, the modeling of nonlinear friction in harmonic drive gear transmission in gimbal servo-system of the DGCMG is proposed. The relationship among the nonlinear friction, the angular velocity and the angular position with an improved Coulomb-Viscous model is derived, and the experiments to identify the various parameters of the improved model are given. At last a feed-forward compensation controller based on the improved model is designed to carry out the friction compensation study.     

Fuzzy-enhanced Adaptive Control for Flexible Drive System with Friction Using Genetic Algorithms

When a mechatronic system is in slow speed motion, serious effect of nonlinear friction plays a key role in its control design. In this paper, a stable adaptive control for drive systems including transmission flexibility and friction, based on the Lyapunov stability theory, is first proposed. For ease of design, the friction is fictitiously assumed as an unknown disturbance in the derivation of the adaptive control law. Genetic algorithms are then suggested for learning the structure and parameters of the fuzzy-enhancing strategy for the adaptive control to improve system's transient performance and robustness with respect to uncertainty. The integrated fuzzy-enhanced adaptive control is well tested via computer simulations using the new complete dynamic friction model recently suggested by Canudas de Wit et al. for modeling the real friction phenomena. Much lower critical velocity of a flexible drive system that determines system's low-speed performance bound can be obtained using the proposed hybrid control strategy.     

高加速精密直线电机运动平台键合图建模与分析

针对高加速度精密直线电机运动平台的特点,在宽频范围内对其进行了伞局耦合动力学建模和分析.利用键合图法建立了包括电机、机械部分并考虑柔性连接和系统非线件摩擦作用的运动平台全局耦合动力学模型,摹于建立的模型利用Matlab/Simulink仿真语言分析了电机的机械时间常数、电气时间常数、摩擦以及螺栓连接对系统响应性能的影响...     

基于新型补偿控制策略的柔性关节控制器设计

柔性关节机器人控制系统中存在不确定性扰动、摩擦力、参数变化以及建模误差等问题,而常规PID控制无法兼顾稳定性和控制精度的要求,为此设计了一种基于新型补偿控制策略的柔性关节鲁棒控制器.控制器设计中,将摩擦力分为线性和非线性两部分,扰动分为确定性和不确定性两部分:机器人自身结构相关量、确定性扰动以及摩擦力的线性部分可以通过...     

On the Modeling of Friction and Rolling in Flexible Multi-Body Systems

This paper is concerned with the dynamic analysis of flexible, nonlinear multi-body systems undergoing contact involving friction and rolling. A continuous friction law is used to model the friction forces between contacting bodies. This avoids the numerical problems associated with the discontinuity inherent to Coulomb's friction law and eliminates the need for different sets of equations modeling sliding and rolling as distinct phenomena. On the other hand, continuous friction laws eliminate specific physical phenomena implied by Coulomb's friction law. The condition of vanishing relative velocity between two contacting bodies is not possible: sticking or rolling are replaced by creeping with a small relative velocity. Discrete events such as transition from slipping to rolling or rolling to slipping are eliminated, together with the high frequency phenomena they are likely to cause. The computational issues associated with the continuous friction law and with the enforcement of the non-holonomic rolling constraint are addressed in this paper. This work is developed within the framework of energy preserving and decaying time integration schemes that provide unconditional stability for nonlinear, flexible multi-body systems undergoing contact involving friction and rolling.     

Adaptive control of a nonlinear dc motor drive using recurrent neural networks

A model-following adaptive control structure is proposed for the speed control of a nonlinear motor drive system and the compensation of the nonlinearities. A recurrent artificial neural network is used for the online modeling and control of the nonlinear motor drive system with high static and Coulomb friction. The neural network is first trained off-line to learn the inverse dynamics of the motor drive system using a modified form of the decoupled extended Kalman filter algorithm. It is shown that the recurrent neural network structure combined with the inverse model control approach allows an effective direct adaptive control of the motor drive system. The performance of this method is validated experimentally on a dc motor drive system using a standard personal computer. The results obtained confirm the excellent disturbance rejection and tracking performance properties of the system.     

Friction compensation of double inverted pendulum on a cart using locally linear neuro-fuzzy model

Double inverted pendulum on a cart (DIPC) is a highly nonlinear system. Due to its complex dynamics, it is widely used as a test-bed plant for the verification of newly designed controllers. In DIPC, two pendulums are kept upward by linear movements of cart. Because of this linear motions and frequent switching of velocity directions, another nonlinearity caused by friction becomes dominant around the equilibrium point. Friction introduces limit cycles to the system and results in a poor steady-state response. To eliminate these negative effects, the locally linear neuro-fuzzy (LLNF) approach is used to build an inverse model for friction compensation. This model is compared with multilayer perceptron network in order to demonstrate the better performance of LLNF. To stabilize DIPC, a common optimal controller is used, and despite its limited performance, experimental results show that the application of inverse modeling for friction compensation improves the steady-state response outstandingly.     

Adaptive integral backstepping sliding mode control for opto-electronic tracking system based on modified LuGre friction model

In order to improve the control accuracy and stability of opto-electronic tracking system fixed on reef or airport under friction and external disturbance conditions, adaptive integral backstepping sliding mode control approach with friction compensation is developed to achieve accurate and stable tracking for fast moving target. The nonlinear observer and slide mode controller based on modified LuGre model with friction compensation can effectively reduce the influence of nonlinear friction and disturbance of this servo system. The stability of the closed-loop system is guaranteed by Lyapunov theory. The steady-state error of the system is eliminated by integral action. The adaptive integral backstepping sliding mode controller and its performance are validated by a nonlinear modified LuGre dynamic model of the opto-electronic tracking system in simulation and practical experiments. The experiment results demonstrate that the proposed controller can effectively realise the accuracy and stability control of opto-electronic tracking system.     

考虑LuGre 摩擦的伺服系统自适应模糊控制

针对摩擦非线性的存在会使伺服系统控制精度难以提高的问题,建立了考虑动态LuGre摩擦的伺服系统数学模型,在系统参数和负载转矩未知的情况下设计了自适应模糊控制器,用自适应模糊逻辑系统在线逼近包含LuGre摩擦在内的非线性环节,从而实现了伺服系统高精度的位置跟踪。利用Lyapunov函数证明了闭环系统的稳定性。仿真结果表明,该控制器能有效地补偿摩擦非线性的影响,并对负载转矩变化具有较强的鲁棒性。     

伺服系统摩擦的支持向量回归建模与反步控制

研究了一种伺服系统摩擦建模和控制的新方法.首先,根据实验数据,提出了基于支持向量机回归的自适应库仑摩擦和固定库仑摩擦建模方法,以解决在速度为零时摩擦力矩不连续导致的建模不准确问题.然后应用所建立的摩擦模型,使用反步法设计了控制器从而实现了摩擦的自适应补偿.通过使用Lyapunov理论证明了闭环系统的稳定性.仿真结果验证了该方法的有效性.