Small fault detection method of sampling mechanical arm closed-loop control system

Abstract

The invention discloses a small fault detection method of a sampling mechanical arm closed-loop control system. The method comprises the following steps: designing a self-adaptive neural network controller of a discrete time mechanical arm; constructing a dynamic estimator to approach the unknown dynamic state of the system; calculating a system dynamic residual error caused by the fault and a residual error compensated by a controller, and further obtaining an enhanced total measurable fault residual error; calculating an absolute fault residual accumulated value of weighted recursion; and designing a fault detection decision scheme, comparing a fault residual accumulation value obtained through real-time calculation with a self-adaptive threshold value, and if a certain moment exists and the fault residual accumulation value is larger than the self-adaptive threshold value, judging that the mechanical arm breaks down at the moment. According to the fault detection scheme, it is guaranteed that after the mechanical arm system breaks down, rapid fault detection is achieved, the problems that the faults change frequently and the fault diagnosis speed is low are solved through an absolute residual accumulation mechanism of weighted recursion, and the safety and rapidity of the fault detection system are guaranteed.

Description

technical field [0001] The invention relates to the technical field of robot fault detection, in particular to a small fault detection method for a closed-loop control system of a sampling manipulator. Background technique [0002] Fault detection is an important issue in modern engineering systems and has received extensive attention so far. Modern engineering systems have increasingly high requirements for safety and reliability. The main goal of fault detection is to identify the occurrence of system faults during real-time operation. Timely and accurate fault diagnosis is crucial to the reliable and efficient operation of many engineering systems, especially those safety-critical, such as aero-engines, chemical processes, industrial robots, power networks, etc. [0003] Small faults are usually those that are smaller than the system uncertainty (e.g., unmodeled dynamics or disturbances / noise), and these faults usually appear at an early stage before larger faults occur. ...

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Problems solved by technology

[0004] In order to overcome the defects and deficiencies in the prior art, the present invention provides a small fault detection method for the closed-loop control system of the sampling manipulator. Aiming at the problem that it is difficult to detect small faults in nonlinear uncertain systems, a method based on determination is proposed. The learning dynamic estimation method can effectively and accurately model the uncertain dynamics of the system, and realize the estimation of fault information; for the problem that the closed-loop system is difficult to detect faults due to the compensation of the controller, a control-based The total measurable fault residual detection scheme compensated by the controller can eliminate the compensation effect of the controller on the fault information, realize the enhancement of the detectable fault information, and ensure the rapid detection of faults; at the same time, on this basis, by designing a The weighted and recursive absolute residual accumulation mechanism solves the problems of frequent fault changes and slow fault diagnosis speed, and ensures the safety and rapidity of the fault detection system

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