基于动态神经网络的一类非线性不确定系统的自适应观测器

基于动态神经网络，对一类非线性不确定系统提出了相应的观测器设计方法，在观测设计中，充分考虑了不确定性和神经网络逼近误差对观测器性能的影响，增加了鲁棒控制项并设计了相应的参数自适应律，以保证良好的观测性能，神经网络的权值在线进行调整，而无需离线学习。     

一类不确定非线性系统的鲁棒自适应轨迹线性化控制

针对一类不确定非线性系统,研究了一种新的鲁棒自适应轨迹线性化控制方案.利用径向基神经网络的在线逼近能力以及被控对象分析模型的有用信息设计一种径向基神经网络干扰观测器来估计系统中存在的不确定性.观测器输出用于设计补偿控制律抵消不确定性对系统性能的影响,鲁棒自适应控制律用于克服逼近误差.采用Lyapunov方法严格证明了在自适应调节律作用下闭环系统所有误差信号最终有界.最后利用倒立摆系统验证了新方法的有效性.     

一类非线性系统的神经网络自适应区间观测器设计

本文研究了一类单输入单输出非线性系统的神经网络自适应区间观测器设计问题. 针对由状态和输入所描述的未知非线性函数的界不可测, 现有的区间观测器方法并未有效地处理系统含有参数不确定性的未知非线性函数. 首先, 本文构造两个径向基函数神经网络来逼近未知非线性部分, 进而分别估计系统状态的上下界; 然后, 选择合适的Lyapunov函数, 采用网络权值校正和网络误差选择机制确保所设计的误差动态系统有界和非负性, 并证明了神经网络自适应区间观测器的稳定性; 最后, 通过仿真实例验证了所提出的神经网络自适应区间观测器的有效性.     

自适应比例–积分H2滑模观测器设计

传统的龙伯格观测器的观测精度极易受到未知外部扰动的影响.为了解决这个问题,本文设计了一种基于径向基神经网络的自适应比例–积分H₂滑模观测器,实现了参数不确定性和外部扰动下非线性系统的鲁棒确切估计.首先,利用径向基神经网络自适应逼近系统模型的复杂非线性项;其次,设计基于误差的线性滑模面,将比例–积分滑模项注入观测器中,使得滑模动态在有限时间内收敛于滑模面,实现对外部扰动和系统模型非线性的完全补偿;最后,基于H₂次优控制和区域极点配置,提出观测器参数自整定方法.通过对单连杆机器人的仿真结果表明,该方法能够保证非线性系统具有较好的鲁棒性和自适应性.     

基于观测器的机械手神经网络自适应控制

提出了一种基于观测器的机械手神经网络自适应轨迹跟随控制器设计方法,这里机 械手的动力学非线性假设是未知的,并且假设机械手仅有关节角位置测量.文中采用一个线 性观测器重构机械手的关节角速度,用神经网络逼近修正的机械手动力学非线性,改进系统 的跟随性能.基于观测器的神经网络自适应控制器能够保证机械手角跟随误差和观测误差的 一致终结有界性以及神经网络权值的有界性,最后给出了机械手神经网络自适应控制器-观 测器设计的主要理论结果,并通过数字仿真验证了所提方法的性能.     

基于模糊神经网络的非线性系统故障诊断

研究故障观测器优化设计,针对一类非线性动态系统,在考虑系统的输入输出包含外部扰动及建模误差等不确定性项[1]的情况下,为了提高所设计观测器对系统数学模型的在线跟踪能力从而进一步提高故障诊断的鲁棒性减少系统的误报警率,提出了基于模糊神经网络的诊断方法。利用神经网络以及模糊系统对非线性函数的无限逼近能力,设计了基于T-S模糊模型[2]的神经网络自适应观测器来拟合系统的非线性模型和系统的非线性故障特性。由Lyapunov稳定性方法获得调整观测器权重的规律。对所用改进方法的收敛性进行了证明,并通过仿真实例说明了诊断方法的有效性和使用性。     

基于动态神经网络的非线性组合系统自适应观测器

基于动态神经网络，对一类非线性组合系统提出一种观测器设计方法．在观测器设计中，充分考虑了神经网络逼近误差项对观测器性能的影响，增加了鲁棒控制项，并设计了相应的参数自适应律，以保证良好的观测性能．神经网络的连接权值在线调整，无需离线学习．仿真结果表明了该方法的有效性．     

基于观测器的空间机器人神经自适应鲁棒控制

研究无速度反馈的不确定性自由漂浮空间机器人轨迹跟踪控制问题,为了提高控制性能,采用一种观测器神经网络自适应鲁棒控制方法。利用神经网络设计观测器对系统的速度信息进行估计;其次,利用神经网络对系统模型的非线性进行逼近,设计神经网络自适应鲁棒控制器对系统进行控制,无需建立复杂的数学模型,并且所设计的自适应律能够进行在线学习;最后,根据H∞理论设计的鲁棒控制器保证了系统稳定,并使系统的L2增益小于给定指标。仿真结果表明,上述方法在无速度信息反馈下仍能进行有效的跟踪控制。     

基于自适应重复学习的不确定多涡卷混沌系统同步控制

基于滞环函数提出一种参数可调的多涡卷混沌系统构造方法. 针对复杂不确定性系统, 综合利用自适应神经网络和重复学习控制方法设计一种自适应重复学习同步控制器; 利用自适应重复学习控制方法对周期时变参数化不确定性进行处理; 对函数型不确定性利用神经网络逼近技术进行补偿; 设计鲁棒学习项对神经网络逼近误差和扰动上界进行估计; 通过构造类Lyapunov 复合能量函数证明了同步误差学习的收敛性. 仿真结果验证了所提出方法的有效性.

     

一类基于模糊逻辑系统的非线性不确定系统自适应观测器设计

利用模糊逻辑系统具有充分利用专家语言信息和逼近连续函数的性质, 分析和研究了一类具有未知不确定界的非线性系统的自适应状态观测器设计问题. 观测器和自适应律的构成直接利用了系统的数学结构信息和模糊逻辑系统对不确定性的输出信息, 在较弱的假设条件下, 这种观测器与被控系统状态间的误差及各参数估计误差一致终极有界. 最后的仿真实例说明了所采用的方法的有效性.     

Observer-based variable universe adaptive fuzzy controller without additional dynamic order

A high-precision fuzzy controller, based on a state observer, is developed for a class of nonlinear single-input-single-output (SISO) systems with system uncertainties and external disturbances. The state observer is introduced to resolve the problem of the unavailability of state variables. Assisted by the observer, a variable universe fuzzy system is designed to approximate the ideal control law. Being auxiliary components, a robust control term and a state feedback control term are designed to suppress the influence of the lumped uncertainties and remove the observation error, respectively. Different from the existing results, no additional dynamic order is required for the control design. All the adaptive laws and the control law are built based on the Lyapunov synthesis approach, and the signals involved in the closed-loop system are guaranteed to be uniformly ultimately bounded. Simulation results performed on Duffing forced oscillation demonstrate the advantages of the proposed control scheme.     

Adaptive neural network output feedback control for flexible multi-link robotic manipulators

In this paper, a novel approach for adaptive control of flexible multi-link robots in the joint space is presented. The approach is valid for a class of highly uncertain systems with arbitrary but bounded dimension. The problem of trajectory tracking is solved through developing a stable inversion for robot dynamics using only joint angles measurement; then a linear dynamic compensator is utilised to stabilise the tracking error for the nominal system. Furthermore, a high gain observer is designed to provide an estimate for error dynamics. A linear in parameter neural network based adaptive signal is used to approximate and eliminate the effect of uncertainties due to link flexibilities and vibration modes on tracking performance, where the adaptation rule for the neural network weights is derived based on Lyapunov function. The stability and the ultimate boundedness of the error signals and closed-loop system is demonstrated through the Lyapunov stability theory. Computer simulations of the proposed robust controller are carried to validate on a two-link flexible planar manipulator.     

不确定随机非线性系统自适应观测器设计

考虑带非参数不确定项的随机非线性系统自适应观测器设计问题.不同于已有结果,系统的不确定项无需满足Lipschitz连续性条件,也不必要仅仅是系统输出的函数.通过设计一个带参数自适应律的非线性观测器来重构系统状态,该观测器结构简单目易于实现.应用Lyapunov稳定性理论和随机微分理论证明该观测器是最终有界的,并且它的界可以通过选取适当的参数进行调节.最后,数值仿真结果表明了该观测器的有效性.     

Adaptive neural network output feedback control of stochastic nonlinear systems with dynamical uncertainties

In this paper, a robust adaptive neural network (NN) backstepping output feedback control approach is proposed for a class of uncertain stochastic nonlinear systems with unknown nonlinear functions, unmodeled dynamics, dynamical uncertainties and without requiring the measurements of the states. The NNs are used to approximate the unknown nonlinear functions, and a filter observer is designed for estimating the unmeasured states. To solve the problem of the dynamical uncertainties, the changing supply function is incorporated into the backstepping recursive design technique, and a new robust adaptive NN output feedback control approach is constructed. It is mathematically proved that the proposed control approach can guarantee that all the signals of the resulting closed-loop system are semi-globally uniformly ultimately bounded in probability, and the observer errors and the output of the system converge to a small neighborhood of the origin by choosing design parameters appropriately. The simulation example and comparison results further justify the effectiveness of the proposed approach.     

Robust adaptive backstepping tracking control of stochastic nonlinear systems with unknown input saturation: A command filter approach

This paper studies the problem of adaptive observer‐based radial basis function neural network tracking control for a class of strict‐feedback stochastic nonlinear systems comprising an unknown input saturation, uncertainties, and unknown disturbances. To handle the issue of a non‐smooth saturation input signal, a smooth function is chosen to approximate the saturation function and the state observer is used to estimate unmeasured states. By the so‐called command filter method in the controller design procedure, the implementation complexity is reduced in the proposed backstepping method. Moreover, a radial basis function neural network is deployed to reconstruct the unknown nonlinear functions. In addition, the gains of all radial basis function neural networks are updated through one updating law leading to a minimal learning parameter which is independent of the number of neural nodes and the order of the system. Comparing with the existing results, the proposed approach can stabilize a constrained stochastic system more effectively and with less computational burden. Finally, a practical example shows the performance of the proposed controller design.     

带有干扰观测器的线控转向系统复合自适应神经网络控制

考虑车辆线控转向(SbW)系统存在不确定动态特性以及外界干扰影响.本文提出一种带有干扰观测器的复合自适应神经网络实现SbW系统的精确建模与稳定控制.首先,利用神经网络在线逼近系统不确定动态,避免控制器设计中使用到系统模型的先验知识.然后,结合系统的跟踪误差与建模误差提出一种新的复合自适应学习率来更新神经网络的权值,从而加快跟踪误差的收敛速度.最后通过设计干扰观测器补偿系统受到摩擦力矩、回正力矩与神经网络逼近误差的影响,提高了系统的抗干扰能力.李雅普诺夫稳定性理论证明了闭环系统的跟踪误差信号一致最终有界.数值仿真与硬件在环实验结果验证了该控制方法的有效性和优越性.     

未知输出反馈非线性时滞系统自适应神经网络跟踪控制

An adaptive output feedback neural network tracking controller is designed for a class of unknown output feedback nonlinear time-delay systems by using backstepping technique. Neural networks are used to approximate unknown time-delay functions. Delay-dependent filters are introduced for state estimation. The domination method is used to deal with the smooth time-delay basis functions. The adaptive bounding technique is employed to estimate the upper bound of the neural network reconstruction error. Based on Lyapunov-Krasoviskii functional, the semi-global uniform ultimate boundedness (SGUUB) of all the signals in the closed-loop system is proved. The arbitrary output tracking accuracy is achieved by tuning the design parameters and the neural node number. The feasibility is investigated by an illustrative simulation example.     

Adaptive Neural Tracking Control for Unknown Output Feedback Nonlinear Time-delay Systems

An adaptive output feedback neural network tracking controller is designed for a class of unknown output feedback nonlinear time-delay systems by using backstepping technique.Neural networks are used to approximate unknown time-delay functions.Delay-dependent filters are intro- duced for state estimation.The domination method is used to deal with the smooth time-delay basis functions.The adaptive bounding technique is employed to estimate the upper bound of the neural network reconstruction error.Based on Lyapunov-Krasoviskii functional,the semi-global uniform ultimate boundedness(SGUUB)of all the signals in the closed-loop system is proved.The arbitrary output tracking accuracy is achieved by tuning the design parameters and the neural node number. The feasibility is investigated by an illustrative simulation example.