In the fault diagnosis of the flywheel system, the input information of the system is uncertain. This uncertainty is mainly caused by the interference of environmental factors and the limited cognitive ability of experts. The BRB (belief rule base) shows a good ability for dealing with problems of information uncertainty and small sample data. However, the initialization of the BRB relies on expert knowledge, and it is difficult to obtain the accurate knowledge of flywheel faults when constructing BRB models. Therefore, this paper proposes a new BRB model, called the FFBRB (fuzzy fault tree analysis and belief rule base), which can effectively solve the problems existing in the BRB. The FFBRB uses the Bayesian network as a bridge, uses an FFTA (fuzzy fault tree analysis) mechanism to build the BRB’s expert knowledge, uses ER (evidential reasoning) as its reasoning tool, and uses P-CMA-ES (projection covariance matrix adaptation evolutionary strategies) as its optimization model algorithm. The feasibility and superiority of the proposed method are verified by an example of a flywheel friction torque fault tree.
HighlightsAutomatic navigation technology in autonomous tractors is one of the key technologies in precision agriculture.A path-tracking control algorithm based on lateral deviation and yaw rate feedback is proposed.The modified steering angle was obtained by comparing the ideal yaw rate with the actual yaw rate.The results demonstrate the efficiency and superior accuracy of the proposed algorithm for tractor path-tracking control.Abstract. The performance of path-tracking control systems for autonomous tractors affects the quality and efficiency of farmland operations. The objective of this study was to develop a path-tracking control algorithm based on lateral deviation and yaw rate feedback. The autonomous tractor path lateral dynamics model was developed based on preview theory and a two-degree-of-freedom tractor model. According to the established dynamic model, a path-tracking control algorithm using yaw angular velocity correction was designed, and the ideal steering angle was obtained by lateral deviation and sliding mode control. The modified steering angle was obtained by a proportional-integral-derivative feedback controller after comparing the ideal yaw rate with the actual yaw rate, which was then combined with the ideal steering angle to obtain the desired steering angle. The simulation and experimental results demonstrate the efficiency and superior accuracy of the proposed tractor path-tracking control algorithm, enabling its application in automatic navigation control systems for autonomous tractors. Keywords: Autonomous tractor, Path-tracking control, Sliding mode control, Yaw rate feedback.
As the propulsion part of a space launch vehicle and nuclear weapon missile, the health status of the liquid rocket determines whether the space launch vehicle and nuclear weapon missile can function normally. Therefore, it is of great significance to evaluate the health status of the liquid rocket. As the structure of the liquid rocket is becoming increasingly sophisticated, subjective judgment alone can no longer meet the needs of the actual system. As an expert system and a gray-box model, the belief rule base (BRB) can process both qualitative and quantitative information. The expert knowledge base is used in the safety assessment of a liquid rocket. However, in practical applications, the traditional BRB model still has two problems, which are that (1) when there are too many premise attributes, it easily leads to the explosion of combination rules, and (2) the reliability of rules is not considered in the process of model reasoning. Therefore, this paper proposes the BRB model with intervals (intervals-BRB) on the basis of traditional BRB. The interval-BRB retains the advantage of the traditional BRB, which can handle semi-quantitative information. In addition, the proposed model changes the reference point of the prerequisite attribute to the reference interval and changes the rule combination. This solves the problem of the traditional BRB explosive combination rule. The ER-rule (evidential reasoning rule) is introduced into the reasoning procedure, and the weight of the rule and the reliability of the rule are considered at the same time, which solves the shortcoming of the traditional BRB, which does not consider the reliability of the rule in reasoning. Finally, the CMAES optimization algorithm is used to optimize the initial model to obtain better performance. Finally, the model is verified by the actual data set of a liquid rocket, and the experimental results show that the model can achieve good experimental results.
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