一类具有高增益观测器的非线性系统的输出调节

针对具有由非线性外部系统产生的未知不确定性函数和未建模动态的非线性不确定系统,研究了其跟踪和干扰抑制问题。首先运用状态变换将输出调节问题转化为非线性系统的镇定问题,接着引入动态信号解决了动态扰动,并设计出高增益的状态观测器去估计不可测的状态。然后根据外系统信息设计自适应的非线性内模,结合自适应控制理论、Backstepping设计方法、模糊控制方法和Lyapunov法给出了输出反馈的自适应模糊控制器和自适应控制律,所提出的输出反馈控制器和自适应律能够实现整个闭环系统的跟踪和干扰抑制,并使得跟踪误差能渐近收敛到给定的任意小的领域内。最后仿真结果验证了所提出的控制器的有效性。     

考虑控制饱和的连铸结晶器振动位移系统预设性能控制

针对伺服电机驱动的连铸结晶器控制系统执行器输入饱和和状态受限问题,同时考虑系统存在负载扰动、参数摄动等不确定性问题,提出一种基于扩张状态观测器的跟踪误差预设性能反步控制策略.首先,针对执行器输入饱和问题,建立系统的数学模型;然后,采用一种线性扩张状态观测器实时观测系统时变负载扰动、参数摄动等不确定性,并对观测误差的收敛性进行分析;接着,针对伺服电机电流饱和与跟踪误差预设性能控制问题,通过引入辅助状态变量确保系统跟踪误差限定在允许范围内,设计基于扩张状态观测器的反步(Backstepping)控制器;最后,根据Lyapunov稳定性理论证明闭环系统的稳定性,并通过系统仿真验证所提出控制策略的有效性.     

基于自适应模糊反步法的永磁同步电机位置跟踪控制

研究具有参数不确定性的永磁同步屯动机位置跟踪控制问题.利用模糊逻辑系统逼近系统中非线性函数,采用反步设计方法实现永磁同步电动机的自适应模糊控制.所提出的自适应模糊控制器在电机参数不确定和负载扰动的情况下,实现了永磁同步电动机的高性能位置跟踪控制.仿真结果表明了所提出方法的有效性.     

带输入饱和的不确定非线性系统自适应模糊触发式补偿控制

针对一类带有输入饱和特性的不确定非线性系统,为了在保证系统跟踪性能的同时最大限度节省系统通讯资源,结合Backstepping技术,提出一种自适应模糊触发式补偿控制方法.由于安全需求或者物理限制等因素,输入饱和特性往往不可避免地存在于实际物理系统中,给系统的控制性能和稳定性造成不利影响.为有效解决该问题,将光滑的双曲正切函数融入自适应控制设计过程,以实现对系统输入饱和约束的补偿.此外,由于实际系统模型难以精确建立,系统描述中难免会存在未知不确定部分,对此,利用模糊逻辑系统对系统的未知不确定部分进行逼近处理.为节省系统的通讯资源,引入一种基于相对阈值的事件触发控制策略,以减小系统传输压力.通过Lyapunov 稳定性理论分析,系统的所有信号都是半全局一致最终有界的.仿真结果验证了所提出方法的有效性.     

基于自适应神经网络Backstepping 空中加油编队飞行控制

为解决无人机自主空中加油过程加油机和受油机编队飞行控制的问题,提出了对受油机采用导引回路和姿态回路分离控制策略,采用L1 导引算法设计一种基于自适应神经网络Backstepping 飞行控制器。将改进的L1 导引算法应用于受油机的横向和纵向的导引,使用自适应神经网络补偿受油机受到的外界的干扰和系统模型误差,神经网络权值矩阵通过自适应律在线更新,并结合Backstepping 控制方法设计受油机的控制律。仿真结果表明：在受油机与加油机编队飞行过程中,该设计方法能有效提高受油机的跟踪精度和抗干扰能力,解决空中加油编队飞行控制问题。     

考虑输入约束的半主动悬架非线性自适应控制

针对具有输入约束及参数不确定性问题的汽车半主动悬架系统,提出一种考虑输入饱和的非线性自适应Backstepping控制器.该方法引入一个辅助系统,通过设计新的误差变量,实现对控制饱和的补偿,解决控制输入的幅值约束问题.同时,考虑到悬架系统的参数不确定性问题,采用映射自适应算法设计自适应律,通过构造适当的Lyapunov函数,保证悬架系统的稳定性.仿真结果表明,所设计的控制器具有良好的隔振性能,而且能够有效降低输入约束和不确定参数对系统性能的影响.     

基于Backstepping的倒立摆鲁棒跟踪控制

针对内部参数不确定及存在外部干扰的非线性倒立摆系统,提出了基于Backstepping方法的滑模变结构控制律,并且采用RBF神经网络逼近系统不确定非线性函数,同时引入滑模误差对其神经网络权值进行在线自适应调整,使神经网络的逼近速度加快,改善了动态性能.该控制律能保证倒立撰轨迹跟踪误差的快速收敛性以及对外部扰动和内部参数不确定的不敏感性,最后给出的仿真实例证明了该理论分析结果的正确性,控制效果良好.     

Adaptive neural control for strict-feedback stochastic nonlinear systems with time-delay

The problem of robust stabilization is investigated for strict-feedback stochastic nonlinear time-delay systems via adaptive neural network approach. Neural networks are used to model the unknown packaged functions, then the adaptive neural control law is constructed by a novel Lyapunov-Krasovskii functional and backstepping. It is shown that all the variables in the closed-loop system are semi-globally stochastic bounded, and the state variables converge into a small neighborhood in the sense of probability.     

Adaptive fuzzy tracking control of nonlinear time-delay systems with unknown virtual control coefficients

In this paper, a novel adaptive fuzzy control scheme is proposed for a class of uncertain single-input and single-output (SISO) nonlinear time-delay systems with the lower triangular form. Fuzzy logic systems are used to approximate unknown nonlinear functions, then the adaptive fuzzy tracking controller is constructed by combining Lyapunov-Krasovskii functionals and the backstepping approach. The proposed controller guarantees uniform ultimate boundedness of all the signals in the closed-loop system, while the tracking error converges to a small neighborhood of the origin. An advantage of the proposed control scheme lies in that the number of adaptive parameters is not more than the order of the systems under consideration. Finally, simulation studies are given to demonstrate the effectiveness of the proposed design scheme.     

Adaptive backstepping control of uncertain systems with unknown input time-delay

In this paper, we establish the robustness of adaptive controllers designed using the standard backstepping technique with respect to unmodeled dynamics involving unknown input time delay. While noting that some results on robust stabilization of non-minimum phase systems using the backstepping technique are available, we realize that the standard adaptive backstepping technique has only been shown applicable to unknown minimum phase systems. Another significance of our result is to enable the class of systems stablizable by adaptive backstepping controllers to cross the boundary of minimum phase systems, since systems with input time delay belong to non-minimum phase systems. Moreover, the L₂ and L_∞ norms of the system output are also established as functions of design parameters. This implies that the transient system performance can be adjusted by choosing suitable design parameters.