Abstract
In this chapter, an adaptive neural output feedback control scheme is proposed for flexible-joint robotic manipulators. First, the mathematical model of a robotic manipulator is built with considering flexible joints. Then, a Luenberger state observer is employed to estimate the unknown states such that the constriction that all the states should be available for measurements can be relaxed. In order to achieve a satisfactory tracking performance, an adaptive controller is designed by combining neural network control and dynamic surface control techniques to avoid the so-called “explosion of complexity” problem. With the proposed scheme, the tracking error can be guaranteed to converge to a small neighborhood around zero, and simulation results show the effectiveness of the developed method.
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Acknowledgments
This work is supported by National Natural Science Foundation (NNSF) of China under Grant No. 61403343, China Postdoctoral Science Foundation Funded Project Under Grant No. 2015M580521 and 12th Five-Year Plan Construction Project of Emerging University Characteristic Specialty (No. 080601).
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© 2016 Springer Science+Business Media Singapore
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Gao, L., Chen, Q., Shi, L. (2016). Adaptive Neural Output Feedback Control for Flexible-Joint Robotic Manipulators. In: Jia, Y., Du, J., Zhang, W., Li, H. (eds) Proceedings of 2016 Chinese Intelligent Systems Conference. CISC 2016. Lecture Notes in Electrical Engineering, vol 404. Springer, Singapore. https://doi.org/10.1007/978-981-10-2338-5_58
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DOI: https://doi.org/10.1007/978-981-10-2338-5_58
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