A New Observer-Based Cooperative Fault-Tolerant Tracking Control Method With Application to Networked Multiaxis Motion Control System

In a networked multiaxis motion control task, faults in any motor will cause the performance degradation of cooperative operation, which may considerably affect the whole network and the quality of products. The main objective of this article is to propose an improved observer-based fault-tolerant tracking control approach for industrial multiagent systems. First, a group of new distributed intermediate estimators is presented, where the design structure is modified to enhance the feasibility of the estimation scheme. It is shown that both of the nominal distributed intermediate estimator and the traditional extended state observer are special cases of the proposed estimator. Second, the estimation performance can be improved significantly via an online reinforcement learning estimation strategy, whose core is an adaptive switching mechanism integrated with a function block of source fault mode localization. Benefiting from satisfactory estimation results, good fault-tolerant tracking control performance can be guaranteed despite of multiple faults and disturbances. The application to a networked multiaxis motion control system demonstrates the effectiveness and superiority of the proposed method.

Automated summary

This article introduces an improved observer-based fault-tolerant tracking control approach for industrial multiagent systems, utilizing new distributed intermediate estimators and an online reinforcement learning estimation strategy to enhance estimation performance and ensure good fault-tolerant tracking control performance.