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首页> 外文期刊>Advances in Natural and Applied Sciences >An Input-Constrained NARMAX Model-Based Adaptive Tracker with Fault Tolerance for Unknown Systems with an Input-Output Direct Feed-Through Term
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An Input-Constrained NARMAX Model-Based Adaptive Tracker with Fault Tolerance for Unknown Systems with an Input-Output Direct Feed-Through Term

机译：具有输入-输出直接馈通项的未知系统的基于输入约束NARMAX模型的具有容错能力的自适应跟踪器

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摘要

A modified NARMAX (nonlinear autoregressive moving average with exogenous inputs) model-based state-space self-tunerwith input-constrained fault tolerance is proposed in this paper for unknown nonlinear stochastic hybrid systems with aninput-output direct transmission term. First, the on-line system identification process is enhanced by using the off-lineobserver/Kalman filter identification method to obtain a good initial guess of the modified NARMAX model. Then, based onsuch system identification, a corresponding adaptive digital control scheme is presented for an unknown continuous-timenonlinear proper system with system and measurement noises and inaccessible system states. Besides, an effective state spaceself-turner with a fault tolerant scheme is presented for the unknown multivariable stochastic system. Further, a quantitativecriterion is used to develop a weighting matrix resetting technique by adjusting and resetting the covariance matrices of theparameters estimated by the Kalman filter estimation algorithm, to achieve parameter estimation for the recovery of the faultysystem. Finally, the proposed new anti-windup scheme according to these models set up decentralized trajectory trackers forunknown interconnected large-scale systems with input constraints and state delays. An illustrative example is given todemonstrate the effectiveness of the proposed method.
机译：针对具有输入输出直接传递项的未知非线性随机混合系统，提出了一种基于输入约束的容错容限的基于状态空间自校正器的改进的NARMAX（带有外源输入的非线性自回归移动平均）模型。首先，通过使用离线观察者/卡尔曼滤波器识别方法来增强在线系统识别过程，以获得对修改后的NARMAX模型的良好初始猜测。然后，基于这种系统识别，针对具有系统和测量噪声以及无法访问的系统状态的未知连续时间非线性固有系统，提出了一种相应的自适应数字控制方案。此外，针对未知的多变量随机系统，提出了一种具有容错方案的有效状态空间自翻转器。此外，通过调整和重置由卡尔曼滤波器估计算法估计的参数的协方差矩阵，使用定量准则来开发加权矩阵重置技术，以实现用于故障系统恢复的参数估计。最后，根据这些模型提出的新的抗饱和方案为具有输入约束和状态延迟的未知互连大型系统建立了分散的轨迹跟踪器。给出了一个说明性示例，以证明所提出方法的有效性。

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