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Health-aware LPV-MPC based on a Reliability-based Remaining Useful Life Assessment

机译：基于基于可靠性的剩余使用寿命评估的健康感知LPV-MPC

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One of the relevant information provided by the prognostics and health management algorithms is the estimation of the Remaining Useful Life (RUL). The prediction of the expected RUL is very useful to decrease maintenance cost, operational downtime and safety hazards. This paper proposes a new strategy of health-aware Model Predictive Control (MPC) for a Linear Parameter Varying (LPV) system that includes as an additional goal extending the system RUL via their estimation using reliability tools. In this approach, the RUL maximization is included in the objective function of the LPV-MPC controller. The RUL is included in the MPC model as an extra parameter varying equation that considers the control action as scheduling variable. The proposed control approach allows the controller to accommodate to the parameter changes. Through computing an estimation of the state variables during prediction, the MPC model can be modified to the estimated state evolution at each time instant. Moreover, for solving the optimization problem by using a series of Quadratic Programs (QP) in each time instant, a new iterative approach is exhibited, which improves the computational efficiency. A pasteurization plant control system is used as a case study to illustrate the performance of the proposed approach.
机译：预后和健康管理算法提供的相关信息之一是估计剩余的使用寿命（RUL）。预期RUL的预测对于降低维护成本，运营停机和安全危害非常有用。本文提出了一种用于线性参数变化（LPV）系统的健康感知模型预测控制（MPC）的新策略，该系统包括通过使用可靠性工具通过其估计来扩展系统RUL的额外目标。在这种方法中，RUL最大化包括在LPV-MPC控制器的目标函数中。 RUL包含在MPC模型中，作为额外参数变化方程，其将控制操作视为调度变量。所提出的控制方法允许控制器适应参数变化。通过计算在预测期间估计状态变量，可以在每次瞬间修改到估计的状态演进的MPC模型。此外，为了通过在每次即时使用一系列二次程序（QP）来解决优化问题，展示了一种新的迭代方法，从而提高了计算效率。巴氏杀菌植物控制系统用作案例研究，以说明所提出的方法的性能。

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