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Article summary:
1. This article proposes a discrete model-free fault-tolerant tracking control (DMFFTC) scheme of redundant manipulator for fault tolerance in the control system of redundant manipulator.
2. The core of the proposed scheme consists of a discrete kinematic estimator and a discrete QP solver, powered by which the fault-tolerant control problem is transformed into a unified computing problem.
3. Extensive simulations and experiments based on a redundant manipulator are performed and analyzed to support the verification of the efficiency and effectiveness of the proposed scheme.
Article analysis:
The article provides an overview of existing methods for fault-tolerant control of robot manipulators, as well as an introduction to the proposed DMFFTC scheme for redundant manipulators. The authors provide evidence from simulations and experiments to support their claims about the efficacy of their proposed method, which is reassuring in terms of trustworthiness and reliability.
However, there are some potential biases that should be noted in this article. For example, there is no discussion or exploration of counterarguments or alternative approaches to solving this problem, nor any mention of possible risks associated with using this method. Additionally, there is no mention of any limitations or drawbacks associated with using this method, which could lead readers to believe that it is perfect without any flaws or drawbacks.
In addition, there is some promotional content in this article that could be seen as biased towards promoting the authors’ own work rather than providing an unbiased overview of existing methods and approaches to solving this problem. This could lead readers to believe that their approach is superior without considering other alternatives or approaches that may be more suitable for certain applications or scenarios.
Finally, it should also be noted that while the authors provide evidence from simulations and experiments to support their claims about the efficacy of their proposed method, they do not provide any evidence from real-world applications or scenarios where their method has been used successfully. This could lead readers to overestimate its effectiveness in real-world scenarios without considering potential issues such as environmental factors or unexpected events that may affect its performance in practice.