State Estimation and Fault Tolerant Nonlinear Predictive Control of an Autonomous Hybrid System Using Unscented Kalman Filter
State Estimation and Fault Tolerant Nonlinear Predictive Control of an Autonomous Hybrid System Using Unscented Kalman Filter
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DOI：
10.1007/978-3-642-01094-1_23
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
J. Prakash;A. Deshpande;S. Patwardhan
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文献类型：
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作者：
J. Prakash;A. Deshpande;S. Patwardhan
在这项工作中，我们提出了一种新的容错非线性模型预测控制(FTNMPC)方案来处理与自治非线性混杂系统(NHS)相关的控制问题。首先，我们利用无迹卡尔曼滤波(Unscented Kalman Filter，UKF)这一无导数的非线性状态估值器，提出了一种用于自治NHS的连续状态和离散状态的估计方案，并进一步将其用于建立NMPC方案。NMPC方案的显著特点是扩展了UKF中的Sigma点传播的概念，以进行未来的弹道预测。然后，我们进一步发展了一种非线性版本的广义似然比(GLR)方法，该方法使用UKF来诊断传感器和/或执行器故障。由非线性GLR方法产生的诊断信息用于在线校正测量向量、用于状态估计/预测的模型和NMPC公式中的约束。通过对基准三容混合系统的仿真研究，验证了所提出的状态估计、诊断和控制方案的有效性。对仿真结果的分析表明，FTNMPC方案有利于闭环系统性能的显着恢复，特别是在传感器故障发生时。
In this work, we propose a novel fault tolerant nonlinear model predictive control (FTNMPC) scheme for dealing with control problems associated with an autonomous nonlinear hybrid system (NHS). To begin with, we develop a scheme for state estimation of continuous as well as discrete states for autonomous NHS using unscented Kalman filter (UKF), a derivative free nonlinear state estimator, and further use it for formulating an NMPC scheme. The salient feature of the NMPC scheme is that the concept of sigma point propagation in UKF is extended to carry out the future trajectory predictions. We then proceed to develop a nonlinear version of generalized likelihood ratio (GLR) method that employs UKF for diagnosing sensor and/or actuator faults. The diagnostic information generated by the nonlinear GLR method is used for on-line correction of the measurement vector, the model used for state estimation/prediction and constraints in the NMPC formulation. The efficacy of the proposed state estimation, diagnosis and control schemes is demonstrated by conducting simulation studies on the benchmark three-tank hybrid system. Analysis of the simulation results reveals that the FTNMPC scheme facilitates significant recovery in the closed loop performance particularly on occurrence of sensor faults.