基于状态全反馈的动态迟滞非线性系统

该文针对不平滑、多映射动态迟滞非线性系统,提出了一种基于神经网络自适应控制方案.在该方案中,通过利用神经网络来逼近模型误差,避免了目前常用逆模型补偿方案中,需求取复杂逆模型的问题.应用Lyapnov稳定定理,证明了整个闭环系统的跟踪误差及神经网络权值将收敛到零点一个有界邻域内.仿真结果表明,所提出的控制方案能够有效补偿迟滞非线性对系统的影响.     

基于状态全反馈的动态迟滞非线性系统自适应控制

该文针对不平滑、多映射动态迟滞非线性系统，提出了一种基于神经网络自适应控制方案。在该方案中，通过利用神经网络来逼近模型误差，避免了目前常用逆模型补偿方案中，需求取复杂逆模型的问题。应用Lyapnov稳定定理，证明了整个闭环系统的跟踪误差及神经网络权值将收敛到零点一个有界邻域内。仿真结果表明，所提出的控制方案能够有效补偿迟滞非线性对系统的影响。     

机器人系统的加权快速终端滑模主动容错控制

针对受执行器故障的非线性机器人系统,提出一种加权快速终端滑模主动容错控制方法。首先利用观测器估计机器人系统中的执行器故障信息,并通过自适应律对故障未知的界进行估计。然后根据机器人各关节的加权位置误差进一步设计快速终端滑模控制器对获得的故障信息做出补偿,从而实现有限时间主动容错控制。通过李雅普诺夫函数法证明了闭环系统的稳定性,并采用两关节机器人验证了方案的有效性。该方案可以通过对不同关节位置误差权重值的分配来相应地补偿故障的影响,能够在有限时间内使得机器人位置跟踪误差快速收敛且跟踪精度得到提高。     

基于逆系统迭代学习观测器的故障调节方法

针对一类满足Lipschitz条件的多输入多输出非线性可逆系统执行器故障问题,提出了一种基于迭代学习观测器的逆系统内模故障调节方法。引入PD型迭代学习策略,设计了迭代学习故障诊断观测器,用于对执行器未知时变故障进行快速、准确估计。根据故障估计值,结合逆系统方法对逆模型进行补偿,使得补偿后的逆模型与非线性被控对象串联仍为伪线性系统;再结合内模控制实现了伪线性系统的容错控制。最后,通过仿真算例验证了该方案的有效性。     

大型飞机神经网络重构控制

介绍了一种通过神经网络在线进行飞行控制重构的方案.本文以非线性动态逆方法为基本控制律,通过设计的自适应神经网络,实时补偿因操纵面故障而引起的逆误差.最后的仿真结果表明,该方案是可行的.     

基于自适应神经网络的一类不确定非线性系统的鲁棒H∞控制

针对一类不确定非线性多输入时变系统，提出了一种新的鲁棒H∞控制方案．通过引入2个自适应神经网络逼近器，提出了一个简化的Hamilton—Jaeobi—like不等式，并据此设计了非线性H∞控制器和匹配不确定项补偿控制器，消除了输入摄动项和估计器最优逼近误差的有界性假设．机器人系统的鲁棒跟踪控制仿真算例证实了所提出控制方案的有效性．     

一类具有未知死区MIMO系统的自适应模糊控制

针对一类具有未知死区并具有下三角函数控制增益矩阵的不确定MIMO非线性系统, 根据滑模控制原理, 并利用Nussbaum函数的性质, 提出了一种自适应模糊控制器的设计方案. 该方案取消了函数控制增益符号已知和死区模型参数上界、下界已知的条件. 通过引入积分型李亚普诺夫函数及最优逼近误差与死区扰动上界的自适应补偿项，证明了闭环系统是稳定的，跟踪误差收敛到零. 仿真结果表明了该方法的有效性.     

基于组合测量的机器人运动误差特性分析及定位补偿技术

研究了机器人各轴运动角度误差的测量与控制补偿技术.讨论了各因素对运动角度误差影响的显著性,独立分析了电机运动控制误差和关节连杆变形误差模型.设计了双轴正交惯性测量方案,通过预先测量机器人各轴运动范围回转角度误差,得到机器人运动空间几何误差分布,基于多元统计学分析确立了不同因素对运动角度误差的影响系数,基于正交多项式拟合建立了各轴定位误差分布模型,在机器人运行前利用该模型计算误差补偿量,控制机器人进行定位补偿.同时,对所提出的机器人各轴运动误差分布规律和正交多项式拟合方法进行了分析,并使用激光跟踪仪测量验证了机器人末端定位精度的补偿效果.研究结果表明,通过对机器人自身性能的研究和补偿可以提高机器人控制精度.     

基本积分型李亚普诺夫函数的直接自适应神经网络控制

针对一类具有下三角形函数控制增益矩阵的非线性系统,基于滑模控制原理,并利用 多层神经网络的逼近能力,提出了一种直接自适应神经网络控制器设计的新方案.通过引入积 分型李亚普诺夫函数及残差与逼近误差和的上界函数的自适应补偿项,证明了闭环系统是全局 稳定的,跟踪误差收敛到零.     

基于改进型非线性干扰观测器的爬壁机器人轨迹跟踪

针对爬壁机器人建模不准确及容易受外部扰动的影响造成位置及姿态误差的问题,提出了一种基于改进型非线性干扰观测器的轨迹跟踪控制方案.首先通过反演控制设计了一个运动学控制器为机器人动力学控制提供参考质心速度与角速度.其次应用改进型非线性扰动观测器作为前馈控制对建模误差及外部扰动进行估计,并保证扰动误差以指数形式收敛.最后针对引入干扰观测器的动力学模型设计了滑模控制器.该方案对外界干扰进行了快速补偿,并通过Lyapunov定理证明了其稳定性.仿真结果表明该控制方法对于克服建模误差及外界干扰具有较好的效果.     

一类不确定非仿射非线性系统自适应模糊控制

针对一类非仿射非线性系统提出了自适应模糊控制方法,该方法把不确定非线性系统表示为定常线性子系统加非线性项的形式,然后采用模糊逻辑系统设计补偿器来消除非线性项的影响。引入时变死区函数对模糊逻辑系统中的未知参数进行自适应调节,并对时变死区设计了自适应律。证明了该方法可使闭环系统的所有信号均有界,且使跟踪误差收敛到原点的小邻域内。仿真结果表明了该方法的有效性。     

Robust adaptive tracking control for servo mechanisms with continuous friction compensation

In this paper, a robust adaptive tracking control scheme is developed for servo mechanisms with nonlinear friction dynamics. A continuously differentiable friction model is used to capture the friction behaviors (e.g. Stribeck effect, Coulombic friction and Viscous friction). The robust integral of the sign of the error (RISE) feedback term is employed to design an innovative adaptive controller to compensate nonlinear friction and bounded disturbances. To reduce the effect of noise pollution, the desired trajectory is employed to replace the output signal in controller design. The developed adaptive controller can guarantee the asymptotic tracking performance for nonlinear servo mechanisms in the presence of nonlinear friction and bounded disturbances. Comparative experimental results are used to validate the effectiveness of the developed control algorithm.     

Friction compensation for a process control valve

A nonlinear locally intelligent actuator design is developed to control a valve independently of the distributed control system. Nonlinear control is implemented through the direct synthesis of a sliding-stem valve model within a nonlinear structure. Input–output linearization with discontinuity smoothing is used to cancel friction nonlinearities as well as to reduce control action chattering. A closed-loop nonlinear Luenberger observer is used to reconstruct an unmeasurable state as well as to provide robust control action in the presence of parametric uncertainty. A model-based fault detector is developed to monitor serious faults such that a warning may be sent to an operator describing the exact nature of the fault. Fault diagnostic approaches are also provided in the form of threshold detection and fault tree analysis. Setpoint tracking results are provided to compare against linear proportional–integral control. The nonlinear controller is shown to outperform linear control set-point tracking measured by integral absolute error (IAE). In conclusion, the advantages of local nonlinear control are discussed.     

基于神经网络的非线性时间序列故障预报

对模型未知非线性系统, 将系统输出组成时间序列并通过空间嵌入的方法转化为一个离散动态系统. 利用线性 AR 模型拟合时间序列的线性部分, 用神经网络拟合时间序列的非线性部分并补偿外界未知的扰动, 提出了通过对状态的观测实现时间序列一步预测的方法. 利用滚动优化的思想将一步预测推广, 提出了时间序列的 N 步预测方法, 证明了时间序列预测误差有界. 通过对预测误差进行概率密度估计和检验, 提出了故障的预报方法. 对 F-16 歼击机的结构故障预报结果表明了方法的有效性.     

Adaptive quantised fault-tolerant tracking control of uncertain nonlinear systems with unknown control direction and the prescribed accuracy

In this paper, the problem of adaptive fault-tolerant tracking control for a class of uncertain nonlinear systems in the presence of input quantisation and unknown control direction is considered. By choosing a class of particular Nussbaum functions, an adaptive fault-tolerant control scheme is designed to compensate actuator faults and input quantisation while the control direction is unknown. Compared with the existing results, the proposed controller can directly compensate for the nonlinear term caused by actuator faults and the nonlinear decomposition on the quantiser without estimating its bound. Furthermore, via Barhalant's Lemma, it is proven that all the signals of the closed-loop system are globally uniformly bounded and the tracking error converges into a prescribed accuracy in prior. Finally, an illustrative example is used for verifying effectiveness of the proposed approach.     

Observer-critic structure-based adaptive dynamic programming for decentralised tracking control of unknown large-scale nonlinear systems

In this paper, a decentralised tracking control (DTC) scheme is developed for unknown large-scale nonlinear systems by using observer-critic structure-based adaptive dynamic programming. The control consists of local desired control, local tracking error control and a compensator. By introducing the local neural network observer, the subsystem dynamics can be identified. The identified subsystems can be used for the local desired control and the control input matrix, which is used in local tracking error control. Meanwhile, Hamiltonian-Jacobi-Bellman equation can be solved by constructing a critic neural network. Thus, the local tracking error control can be derived directly. To compensate the overall error caused by substitution, observation and approximation of the local tracking error control, an adaptive robustifying term is employed. Simulation examples are provided to demonstrate the effectiveness of the proposed DTC scheme.     

Simultaneous fault estimation and fault-tolerant tracking control for uncertain nonlinear discrete-time systems

This paper deals with simultaneous fault estimation and control for a class of nonlinear systems with parameter uncertainty, which is described by Takagi–Sugeno (T–S) fuzzy model with parameter uncertainties and unknown disturbance. In this paper, a fuzzy reference model is used to generate error dynamic for tracking control. By considering actuator fault as an auxiliary state vector, we construct an augmented error system and propose a fault estimator/controller to achieve simultaneous fault estimation and fault-tolerant tracking control. H_∞ approach is used in the design of estimator/controller to attenuate the effect of the unknown disturbance and parameter uncertainties. The design conditions are formulated into a set of linear matrix inequalities (LMIs), which can be efficiently solved. Finally, a pitch-axis nonlinear missile model is used to illustrate the effectiveness of the proposed method.     

基于神经网络观测器的一类非线性系统的故障调节

将一般形式的非线性模型线性化为输出反馈型．针对该类系统，首先利用神经网络的一致逼近任意非线性连续函数的性质，构造神经网络自适应观测器，以获取反映故障信息的残差；然后根据残差信息在线估计故障；最后通过修正控制律来补偿故障所带来的影响．并采用Lyapunov稳定性理论证明了系统的稳定性．仿真结果验证了该方法的有效性．     

Recursive filtering adaptive fault‐tolerant tracking control for uncertain switched multivariable nonlinear systems

This article synthesizes a recursive filtering adaptive fault‐tolerant tracking control method for uncertain switched multivariable nonlinear systems. The multivariable nonlinear systems under consideration have both matched and mismatched uncertainties, which satisfy the semiglobal Lipschitz condition. A piecewise constant adaptive law generates adaptive parameters by solving the error dynamics with the neglection of unknowns, and the recursive least squares is employed to minimize the residual error by categorizing the total uncertainty estimates into matched and mismatched components. A filtering control law is designed to compensate the actuator faults and nonlinear uncertainties such that a good tracking performance is delivered with guaranteed robustness. The matched component is canceled directly by adopting their opposite in the control signal, whereas a dynamic inversion of the system is performed to eliminate the effect of the mismatched component on the output. By exploiting the average dwell time principle, the error bounds are derived for the states and control inputs compared with the virtual reference system which defines the best performance that can be achieved by the closed‐loop system. Both numerical and practical examples are provided to illustrate the effectiveness of the proposed switching recursive filtering adaptive fault‐tolerant tracking control architecture, comparisons with model reference adaptive control are also carried out.     

Actuator Fault Reconstruction for Systems with Monotone Nonlinearities

This paper considers observer‐based fault reconstruction for systems with monotone nonlinearities. The nonlinear term in the observer error dynamics satisfies a sector property. Using Lyapunov redesign techniques, a continuous nonlinear error feedback is designed according to the slope property of the nonlinear term to stabilize the error dynamics. Hence, the convergence of observer error is independent of the Lipschitz constant. If the observer error converges to zero asymptotically, then the continuous error feedback can be used to reconstruct the faults. As an extension, an adaptive scheme is developed for systems where the arguments of nonlinear functions are perturbed by unknown parameters. Finally, simulations of an electric driving system are presented to show the effectiveness of the schemes.