数据驱动的故障诊断与容错控制系统设计方法研究
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摘要
动态系统的安全性和可靠性越来越多地受到人们的重视,这些要求不仅局限于核反应堆、化工过程和航天、航空飞行器等安全性要求高的大型工业生产和军用系统,而且己经扩展到诸如车辆控制、人工智能机器人等新型系统。有效的故障诊断与容错控制技术能够为现代控制系统长寿命、可靠运行提供有力保障。因此,深入研究故障诊断与容错控制技术,具有重要的理论意义和实际应用价值。
目前,基于机理建模的故障诊断与容错控制系统设计理论己经比较完善,但是将实际系统抽象为机理模型并且确定模型参数仍然需要工程师花费大量的时间与精力。另一方面,实际系统存在大量离线和在线的输入、输出数据,这启发人们思考如何使用数据取代机理模型,设计故障诊断与容错控制系统。
本论文针对离散线性时不变系统,从理论上深入研究数据驱动的故障诊断与容错控制系统设计方法,主要内容如下：
针对离散线性时不变系统,提出一种数据驱动的鲁棒故障诊断系统设计方法。该方法基于离线数据辨识等价空间,根据H2性能指标,分别针对传感器和执行器故障,设计鲁棒故障检测、降阶鲁棒故障检测、鲁棒故障隔离和鲁棒故障识别的残差生成器。与现有的数据驱动的故障诊断系统设计方法相比,本文方法设计的残差信号对故障灵敏度高,对干扰鲁棒性强,提高了故障诊断系统的有效性。
针对离散线性时不变系统,提出一种数据驱动的参数化控制器设计方法。该方法基于己辨识的等价空间,在数据驱动的残差生成器基础上,设计数据驱动的扩展内模控制器；在降阶等价空间设计算法基础上,设计SISO系统的Youla参数化控制器；在全状态观测器基础上,进一步设计MIMO系统的Youla参数化控制器,实现基本的控制性能。
针对Youla参数化控制器,提出一种数据驱动的控制器优化方法,残差整定法(RbT)。该方法利用残差数据估计二次性能指标的梯度值,采用梯度下降法整定Youla参数,实现扰动抑制。本文理论上给出RbT算法的收敛性条件、全局最优条件和精度分析,工程上设计扩展RbT (ERbT)算法,在线优化控制器参数。
针对二自由度的Youla参数化控制器,提出一种数据驱动的容错控制器设计方法,残差迭代反馈整定法(RIFT)。该方法采用残差数据设计闭环实验并估计二次性能指标的梯度值,基于梯度下降法重构Youla参数矩阵和前馈增益,实现容错控制目的。本文分别给出单入单出(SISO)和多入多出(MIMO)系统的RIFT算法,并进一步讨论了RIFT的实验次数问题。
The increasing demands on safety and reliability of dynamic systems have receivedmuch attention from people. These requirements have extended from those nuclear re-actor, chemical industry process, aerospace vehicle systems, etc. to new systems such asautonomous vehicles or intelligent robots. An efective fault diagnosis and fault-tolerantcontrol is of prime importance for the strong support on safety and reliability. Hence,the study on fault diagnosis and fault-tolerant control technology has both theoretical andpractical importance.
Although the model-based fault diagnosis and fault-tolerant control theory has beenwell-established, it is still difcult to establish mathematical models by means of the frstprinciple for complicated plants. On the other hand, a large amount of historical datafrom regular sensor measurements, event-logs and records are often available. Motivatedby this observation, it is of great interest to design fault diagnosis and fault-tolerant con-trol schemes based on efcient data-driven design methods of fault diagnosis and fault-tolerant schemes instead of model-based methods.
This dissertation focuses on the data-driven design of fault diagnosis and fault-tolerant control systems for discrete linear time-invariant (LTI) systems. The main contri-butions of this dissertation can be summarized as follows:
For discrete LTI systems, a data-driven design method of robust fault diagnosissystems is proposed, in which the well-established parity space and parity vectors areidentifed directly by of-line data. Based on the H2performance index, residual genera-tions for robust fault detection, robust reduced order fault detection, robust fault isolationand robust fault identifcation are constructed for sensor and actuator faults, respectively.Compared with the existing data-driven design method of fault diagnosis, the proposedmethod in this dissertation generates residuals keeping sensitivity to faults and robustnessto disturbances, and thus improves the robustness of fault diagnosis systems.
For discrete LTI systems, this dissertation proposes a data-driven design methodof parametrization controllers. The proposed method studies the data-driven design ofthe extended Internal Model Controllers (EIMC) based on the identifed parity space andthe residual generation, the data-driven design of observer-based Youla parametrization controllers for SISO and MIMO systems based on the reduced order parity space and fullstate observers, respectively.
Based on Youla parametrization, a residual data-based tuning method for Youla pa-rameters, denoted as residual-based tuning (RbT), is proposed. This method exploitsresidual signals for estimating the gradient of the cost function and based on an iterativedescent algorithm to tune Youla parameters for disturbance rejection. In the theoreticalview, the convergence issue, the global optimum condition and the asymptotic accuracyare given. In the view of engineering, an extended RbT (ERbT) algorithm is proposed tooptimize Youla parameters online.
Based on a two degrees-of-freedom (2-DOF) Youla parametrization structure, thisdissertation proposes a novel residual-based iterative feedback tuning method, denotedas residual iterative feedback tuning (RIFT). This method feeds back residuals into theclosed loop, estimates the gradient of the cost function via set of experiments and thenachieves a fault-tolerant control based on the iterative descent algorithm. For single-input-single-output (SISO) and multiple-input-multiple-output (MIMO) plants, two RIFTalgorithms are developed, respectively. The number of experiments of MIMO plants isfurther discussed.
目前,基于机理建模的故障诊断与容错控制系统设计理论己经比较完善,但是将实际系统抽象为机理模型并且确定模型参数仍然需要工程师花费大量的时间与精力。另一方面,实际系统存在大量离线和在线的输入、输出数据,这启发人们思考如何使用数据取代机理模型,设计故障诊断与容错控制系统。
本论文针对离散线性时不变系统,从理论上深入研究数据驱动的故障诊断与容错控制系统设计方法,主要内容如下：
针对离散线性时不变系统,提出一种数据驱动的鲁棒故障诊断系统设计方法。该方法基于离线数据辨识等价空间,根据H2性能指标,分别针对传感器和执行器故障,设计鲁棒故障检测、降阶鲁棒故障检测、鲁棒故障隔离和鲁棒故障识别的残差生成器。与现有的数据驱动的故障诊断系统设计方法相比,本文方法设计的残差信号对故障灵敏度高,对干扰鲁棒性强,提高了故障诊断系统的有效性。
针对离散线性时不变系统,提出一种数据驱动的参数化控制器设计方法。该方法基于己辨识的等价空间,在数据驱动的残差生成器基础上,设计数据驱动的扩展内模控制器；在降阶等价空间设计算法基础上,设计SISO系统的Youla参数化控制器；在全状态观测器基础上,进一步设计MIMO系统的Youla参数化控制器,实现基本的控制性能。
针对Youla参数化控制器,提出一种数据驱动的控制器优化方法,残差整定法(RbT)。该方法利用残差数据估计二次性能指标的梯度值,采用梯度下降法整定Youla参数,实现扰动抑制。本文理论上给出RbT算法的收敛性条件、全局最优条件和精度分析,工程上设计扩展RbT (ERbT)算法,在线优化控制器参数。
针对二自由度的Youla参数化控制器,提出一种数据驱动的容错控制器设计方法,残差迭代反馈整定法(RIFT)。该方法采用残差数据设计闭环实验并估计二次性能指标的梯度值,基于梯度下降法重构Youla参数矩阵和前馈增益,实现容错控制目的。本文分别给出单入单出(SISO)和多入多出(MIMO)系统的RIFT算法,并进一步讨论了RIFT的实验次数问题。
The increasing demands on safety and reliability of dynamic systems have receivedmuch attention from people. These requirements have extended from those nuclear re-actor, chemical industry process, aerospace vehicle systems, etc. to new systems such asautonomous vehicles or intelligent robots. An efective fault diagnosis and fault-tolerantcontrol is of prime importance for the strong support on safety and reliability. Hence,the study on fault diagnosis and fault-tolerant control technology has both theoretical andpractical importance.
Although the model-based fault diagnosis and fault-tolerant control theory has beenwell-established, it is still difcult to establish mathematical models by means of the frstprinciple for complicated plants. On the other hand, a large amount of historical datafrom regular sensor measurements, event-logs and records are often available. Motivatedby this observation, it is of great interest to design fault diagnosis and fault-tolerant con-trol schemes based on efcient data-driven design methods of fault diagnosis and fault-tolerant schemes instead of model-based methods.
This dissertation focuses on the data-driven design of fault diagnosis and fault-tolerant control systems for discrete linear time-invariant (LTI) systems. The main contri-butions of this dissertation can be summarized as follows:
For discrete LTI systems, a data-driven design method of robust fault diagnosissystems is proposed, in which the well-established parity space and parity vectors areidentifed directly by of-line data. Based on the H2performance index, residual genera-tions for robust fault detection, robust reduced order fault detection, robust fault isolationand robust fault identifcation are constructed for sensor and actuator faults, respectively.Compared with the existing data-driven design method of fault diagnosis, the proposedmethod in this dissertation generates residuals keeping sensitivity to faults and robustnessto disturbances, and thus improves the robustness of fault diagnosis systems.
For discrete LTI systems, this dissertation proposes a data-driven design methodof parametrization controllers. The proposed method studies the data-driven design ofthe extended Internal Model Controllers (EIMC) based on the identifed parity space andthe residual generation, the data-driven design of observer-based Youla parametrization controllers for SISO and MIMO systems based on the reduced order parity space and fullstate observers, respectively.
Based on Youla parametrization, a residual data-based tuning method for Youla pa-rameters, denoted as residual-based tuning (RbT), is proposed. This method exploitsresidual signals for estimating the gradient of the cost function and based on an iterativedescent algorithm to tune Youla parameters for disturbance rejection. In the theoreticalview, the convergence issue, the global optimum condition and the asymptotic accuracyare given. In the view of engineering, an extended RbT (ERbT) algorithm is proposed tooptimize Youla parameters online.
Based on a two degrees-of-freedom (2-DOF) Youla parametrization structure, thisdissertation proposes a novel residual-based iterative feedback tuning method, denotedas residual iterative feedback tuning (RIFT). This method feeds back residuals into theclosed loop, estimates the gradient of the cost function via set of experiments and thenachieves a fault-tolerant control based on the iterative descent algorithm. For single-input-single-output (SISO) and multiple-input-multiple-output (MIMO) plants, two RIFTalgorithms are developed, respectively. The number of experiments of MIMO plants isfurther discussed.
引文
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