Feedback linearization in the presence of uncertainties

After a brief review of feedback linearization, several approaches to the problem of parametric uncertainty are outlined, and the adaptive approach to both SISO and MIMO plants is discussed in detail. Based on experience with adaptive control of linear plants, two parameter update laws are chosen for nonlinear plants according to their relative degrees. The simpler of the two adaptive laws is applicable to plants with relative degree one. For plants with higher relative degree the adaptive scheme is more complex, but a simple update law can still be employed in the special case when the state diffeomorphism, required for linearization, does not depend on uncertain parameters. The simpler adaptive scheme is shown to be robust with respect to unmodelled dynamics.     

BLDC位置伺服系统的离散变结构控制

针对离散变结构控制系统的抖振，在离散趋近率的基础上，提出了一种扰动动态补偿的离散趋近率，设计了离散变结构控制(DVSC)策略，研究了变结构控制系统的鲁棒性能，应用于无刷直流电动机(BLDC)位置伺服系统。理论推导与仿真实验表明：新的离散变结构控制策略可以明显改善系统的品质，增强系统的鲁棒性。     

Model reference adaptive control for the azimuth-pointing system of a balloon-borne stabilized platform

A balloon-born stabilized platform has been developed for the remotely operated altitude-azimuth pointing of a millimetre wave telescope system. This paper presents a development and implementation of model reference adaptive control (MRAC) for the azimuth-pointing system of the stabilized platform. The primary goal of the controller is to achieve pointing RMS better than 0.1°. Simulation results indicate that MRAC can achieve pointing RMS better than 0.01°. Ground test results show pointing RMS better than 0.03°. Data from the first flight at the National Scientific Balloon Facility (NSBF), Palestine, TX show pointing RMS better than 0.02°.     

一类不确定非线性系统的鲁棒镇定

针对一类含有不确定性的非线性系统,首先通过精确线性化方法,将其变为一种规范型形式,在一定的不确定性的假设条件下,构造了鲁棒控制器,仿真算例说明了方法的有效性。     

一般非线性离散系统P-D型迭代学习收敛性研究

针对非线性离散系统的控制问题,介绍了当前的研究现状,分析了开环迭代学习控制的不足,将某一类的非线性离散系统推广到了一般的非线性离散系统,对于一般的非线性离散系统,改变了以往只能借助前次的运行信息,提出了基于当前误差和前次运行误差信息的P-D型开闭环迭代学习控制律,使得对系统运行信息的利用更加充分、准确.此外,利用λ范数和归纳法给出了该学习律收敛的充分条件,证明了它的收敛性.仿真结果表明了它的有效性.     

Preliminary results about robust lagrange stability in adaptive non-linear regulation

We are concerned with the problem of regulating the equilibrium point of a non-linear system in the presence of both parametric and dynamic uncertainties. For the parametric uncertainty we propose a new adaptive controller based on a Lyapunov design and guaranteeing the global boundedness of the solution if a growth condition is satisfied. For the dynamic uncertainty we propose a new way of characterizing the unmodelled effects which encompasses some singular and regular perturbations as illustrated by our worked example. Finally we show how, by modifying the above controller, the boundedness property can be made robust to these unmodelled effects.     

Robustness of VS-MRAC with respect to unmodelled dynamics and external disturbances

A variable structure model reference adaptive controller (VS-MRAC) using only input and output measurements was recently proposed and shown to be globally asymptotically stable with superior transient behaviour and disturbance rejection properties. In this paper a singular perturbation approach is used in order to establish the robustness of the controller in the presence of unmodelled dynamics (parasitics) and disturbances. We develop a new Lyapunov-based technique to analyse the overall system and show that for sufficiently small parasitics the system remains stable and the output error is small in some sense.     

基于Hammerstein模型的超声波电机非线性补偿

超声波电机的运行过程具有明显的非线性特征,影响电机控制精度。超声波电机Hammerstein模型中的非线性环节,反映了电机控制关系中非线性的主要特征。通过求取模型非线性环节的逆函数,实现对控制非线性的有效补偿,简化控制器设计,有利于提高控制性能。基于非线性逆补偿,给出一种迭代学习转速控制方法,并进行实验研究。实验表明了所述非线性逆补偿及控制方法的有效性     

基于自适应迭代学习的多智能体系统编队控制

针对带未知时变参数的非线性多智能体系统的编队问题,提出一种分布式自适应迭代学习控制策略.首先,通过傅里叶级数对系统的不确定参数进行展开,采用一个收敛级数序列处理傅里叶级数展开产生的截断误差,结合多智能体运行过程中的编队误差推导自适应迭代学习控制律和参数更新律;其次,针对领导者动态对大部分智能体都是未知的情况,设计新的辅助控制来补偿未知动态和避免未知有界干扰;然后,基于李亚普诺夫能量函数证明了在所设计控制律作用下多智能体系统编队误差随着迭代次数的增加在有限时间内趋于0;最后,将该控制策略运用到多无人机编队系统中,并通过搭建半物理实验平台,验证了控制方法的有效性.实验结果表明该控制方法可以确保多智能体快速形成所需编队,并且每个智能体在有限时间内可以精确跟踪期望轨迹.所提方法充分考虑了多智能体系统的参数不确定性以及抗干扰的能力,为实际应用中复杂多智能体系统的精确控制提供了有效的方法.