Abstract
This paper develops a neuro-dynamic programming based local tracking control (LTC) scheme for unknown interconnected systems. By using the local input-output data and the desired states of coupling subsystems, a local neural network (NN) identifier is established to obtain the local input gain matrix online. By introducing a modified local cost function, the Hamilton-Jacobi-Bellman equation is solved by a local critic NN with asymptotically convergent weight vector, which is obtained by nested update law, and the LTC can be derived with the desired state aided augmented subsystem. The stability of the closed-loop system is shown by Lyapunov’s direct method. The simulation on the parallel inverted pendulum system illustrates that the developed LTC scheme is effective.
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grants 61603387, 61773075, 61533017 and 61773373, and in part by the Early Career Development Award of SKLMCCS under Grant 20180201.
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Zhao, B., Liu, D., Ha, M., Wang, D., Xu, Y., Wei, Q. (2018). Local Tracking Control for Unknown Interconnected Systems via Neuro-Dynamic Programming. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11307. Springer, Cham. https://doi.org/10.1007/978-3-030-04239-4_23
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DOI: https://doi.org/10.1007/978-3-030-04239-4_23
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