Abstract
In this paper, a novel active optimal fault-tolerant control (FTC) scheme is designed based on adaptive dynamic programming (ADP) for modular manipulator when sensor and actuator faults are concurrency. Firstly, the sensor fault is transformed into the pseudo-actuator fault by constructing a nonlinear transformation with diffeomorphism theory. Secondly, the faults estimated by the adaptive fault observer are applied to establish an improved performance index function. Next, the online policy iteration (PI) algorithm is used to solve the Hamilton-Jacobi-Bellman (HJB) equation via establishing a critic neural network. The optimal fault-tolerant controller is proved to be uniformly ultimately bounded (UUB) based on Lyapunov stable theory. Finally, the effectiveness of the proposed multi-fault-tolerant control algorithm is verified by simulation results.
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Acknowledgement
This work is supported by the National Natural Science Foundation of China (Grant nos. 61374051, 61773075 and 61703055) and the Scientific Technological Development Plan Project in Jilin Province of China (Grant nos. 20170204067GX, 20160520013JH and 20160414033GH).
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Li, B. et al. (2019). Active Optimal Fault-Tolerant Control Method for Multi-fault Concurrent Modular Manipulator Based on Adaptive Dynamic Programming. In: Lu, H., Tang, H., Wang, Z. (eds) Advances in Neural Networks – ISNN 2019. ISNN 2019. Lecture Notes in Computer Science(), vol 11555. Springer, Cham. https://doi.org/10.1007/978-3-030-22808-8_15
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DOI: https://doi.org/10.1007/978-3-030-22808-8_15
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