Abstract
Based on the cooperation of neural network and extended state observer (ESO), in this paper, an approach for reinforcement control will be presented for Planar robot. By using the sliding surface as a state variable, the nominal system in quadratic form will be converted to first-order where the total uncertain component is estimated and remove by ESO. Then, a reinforcement algorithm will be added in collaboration to determine the nearly optimal solution of Hamilton-Jacobi-Bellman (HJB) equation. During the determination of the control signal, only one neural network is applied to reduce the computational complexity while still achieving the desired requirements. The simulation results of the algorithm will be examined on the Planar Robot with two degrees of freedom, thereby confirming the effectiveness of proposed control strategy.
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References
Blatnický, M., Dizo, J., Gerlici, J., Sága, M., Lack, T., Kuba, E.: Design of a robotic manipulator for handling products of automotive industry. Int. J. Adv. Robot. Syst. 17(1) (2020)
Li, H., Liu, C.L., Zhang, Y., Chen, Y.Y.: Adaptive neural networks-based fixed-time fault-tolerant consensus tracking for uncertain multiple Euler-Lagrange systems. ISA Trans. 129, 102–113 (2021)
Rahmani, M., Komijani, H., Rahman, M.H.: New sliding mode control of 2-DOF robot manipulator based on extended grey wolf optimizer. Int. J. Control Autom. Syst. 18(6), 1572–1580 (2020). https://doi.org/10.1007/s12555-019-0154-x
Shao, K., Tang, R., Xu, F., Wang, X., Zheng, J.: Adaptive sliding mode control for uncertain Euler-Lagrange systems with input saturation. J. Franklin Inst. 358(16), 8356–8376 (2021)
Ramírez-Neria, M., Madonski, R., Luviano-Juárez, A., Gao, Z., Sira-Ramírez, H.: Design of ADRC for second-order mechanical systems without time-derivatives in the tracking controller. In: 2020 American Control Conference (ACC), (2020)
Ha, W., Back, J.: A disturbance observer-based Robust Tracking Controller for Uncertain Robot Manipulators. Int. J. Control Autom. Syst. 16(2), 417–425 (2018). https://doi.org/10.1007/s12555-017-0188-x
Zhang, Z., Leibold, M., Wollherr, D.: Integral sliding-mode observer-based disturbance estimation for Euler-Lagrangian systems. IEEE Trans. Control Syst. Technol. 28(6), 2377–2389 (2019)
Zhou, Q., Zha, S., Li, R., Lu, R., Wu, C.: Adaptive neural network tracking control for robotic manipulators with dead zone. IEEE Trans. Neural Netw. Learn. Syst. 30(12), 3611 - 3620 (2018)
Jouila, A., Nouri, K.: An adaptive robust nonsingular fast terminal sliding mode controller based on wavelet neural network for a 2-DOF robotic arm. J. Franklin Inst. 357(18), 13259–13282 (2020)
Ioannou, P., Fidan, B.: Advances in Design and Control, Adaptive Control Tutorizal. SIAM, PA (2006)
Tang, L., Liu, Y.J., Tong, S.: Adaptive neural control using reinforcement learning for a class of robot manipulator. Neural Comput. Appl. 25, 135–141 (2014)
Lewis, F.L., Vrabie, D.: Reinforcement learning and adaptive dynamic programming for feedback control. IEEE Circuits Syst. Mag. 9(3), 32–50 (2009)
Dupree, K., Patre, P.M., Wilcox, Z.D., Dixon, W.E.: Asymptotic optimal control of uncertain nonlinear Euler-Lagrange systems. Automatica 1, 99–107 (2011)
Hu, Y., Si, B.: A reinforcement learning neural network for robotic manipulator control. Neural Comput. 30(7), 1983–2004 (2018)
Vamvoudakis, K.G., Lewis, F.L.: Online actor-critic algorithm to solve the continuous-time infinite horizon optimal control problem. Automatica, 46, 878–888, (2010)
Vu, V.T., Dao, P.N., Loc, P.T., Huy, T.Q.: Sliding Variable-based online adaptive reinforcement learning of uncertain/disturbed nonlinear mechanical systems. J. Control Autom. Electr. Syst. 32(2), 281–290 (2021). https://doi.org/10.1007/s40313-020-00674-w
Luy, N.T.: Reinforecement learning-based optimal tracking control for wheeled mobile robot. In: Proceedings of IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), pp. 371-376, May 2012
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Trung, D.N., Van, T.N., Le, H.X., Manh, D.D., Hoang, D. (2023). Reinforcement Control for Planar Robot Based on Neural Network and Extended State Observer. In: Nguyen, T.D.L., Verdú, E., Le, A.N., Ganzha, M. (eds) Intelligent Systems and Networks. ICISN 2023. Lecture Notes in Networks and Systems, vol 752. Springer, Singapore. https://doi.org/10.1007/978-981-99-4725-6_62
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DOI: https://doi.org/10.1007/978-981-99-4725-6_62
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