Diagnosis and Compensation of Control Program, Sensor and Actuator Failures in Nonlinear Systems Using Hierarchical State Space Checks
Diagnosis and Compensation of Control Program, Sensor and Actuator Failures in Nonlinear Systems Using Hierarchical State Space Checks
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DOI：
10.1007/s10836-020-05914-0
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发表时间：
2020-12
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通讯作者：
Md Imran Momtaz;A. Chatterjee
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作者：
Md Imran Momtaz;A. Chatterjee
具有非线性动态特性的自主系统需要对传感器、执行器和车载电子设备中的错误具有极强的弹性，以确保车辆的整体安全。以前的研究集中在控制理论方法与显着的计算负担，重点是在驱动故障。相比之下，我们建议使用分层机器学习驱动的状态空间检查，以高灵敏度和低延迟检测和诊断控制程序执行，传感器和执行器中的错误。每次检查产生一个随时间变化的误差信号，便于系统的因果诊断，同时允许从每次检查快速参数估计。由于检查是在系统参数的小子集上，估计是快速和准确的。估计的参数，然后用于重新配置系统控制器参数，快速系统恢复。我们使用四轴飞行器系统来演示和验证我们的方法。控制器，传感器和执行器的错误可以检测，诊断和补偿使用一个共同的检查平台，计算开销低。该技术在四轴飞行器硬件测试飞行器上进行了验证。
Autonomous systems with nonlinear dynamics need to be extremely resilient to errors in sensors, actuators and on-board electronics for the purpose of overall vehicle safety. Prior research has focused on control-theoretic methods with significant computational burden with a focus on failures in actuation. In contrast, we propose the use of hierarchical machine learning driven state space checks that detect and diagnose errors in control program execution, sensors and actuators with high sensitivity and low latency. Each check produces a time-varying error signal that facilitates effect-cause diagnosis of the system, while allowing rapid parameter estimation from each check. Since the checks are over small subsets of system parameters, estimation is fast and accurate. The estimated parameters are then used to reconfigure the system controller parameters for rapid system recovery. We use a quadcopter system to demonstrate and validate our approach. Controller, sensor and actuator errors can be detected, diagnosed and compensated using a common checking platform with low computational overhead. The technique is validated on a quadcopter hardware test vehicle.