Abstract
This paper presents an adaptive neural control design for an n-link rigid robot with both output constraint and unknown time-varying delays. The main design difficulties caused by both the output constraint and unknown time-varying delayed states. In order to overcome these difficulties, the novel Barrier Lyapunov Functions (BLF) and iterative backstepping procedures are employing to guarantee constraints satisfaction of the position of the robot. The Lyapunov-krasovskii functionals (LKFs) are utilized to eliminate and compensate the effect of unknown functions with time-varying delayed states in communication channels. By using the Lyapunov analysis, the stability of closed-loop systems is proven.
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References
Chen, C.L.P., Wen, G.X., Liu, Y.J., Liu, Z.: Observer-based adaptive backstepping consensus tracking control for high-order nonlinear semi-strict-feedback multiagent systems. IEEE Trans. Cybern. 46, 1591–1601 (2016)
Lai, G.Y., Liu, Z., Zhang, Y., Chen, C.L.P.: Adaptive fuzzy tracking control of nonlinear systems with asymmetric actuator backlash based on a new smooth inverse. IEEE Trans. Cybern. 46, 1250–1262 (2016)
Wen, G.X., Chen, C.L.P., Liu, Y.J., Liu, Z.: Neural-network-based adaptive leader-following consensus control for second-order nonlinear multi-agent systems. IET Control Theory Appl. 9, 1927–1934 (2015)
Liu, Z., Chen, C., Zhang, Y., Chen, C.L.P.: Adaptive neural control for dual-arm coordination of humanoid robot with unknown nonlinearities in output mechanism. IEEE Trans. Cybern. 45, 507–518 (2015)
Chen, M., Chen, W.H., Wu, Q.X.: Adaptive fuzzy tracking control for a class of uncertain MIMO nonlinear systems using disturbance observer. Sci. China Inf. Sci. 57, 1–13 (2014)
Yang, Q.M., Jagannathan, S., Sun, Y.X.: Robust integral of neural network and error sign control of MIMO nonlinear systems. IEEE Trans. Neural Netw. Learn. Syst. 26, 3278–3286 (2015)
Kwan, C., Lewis, F.L., Dawson, D.M.: Robust neural-network control of rigid-link electrically driven robots. IEEE Trans. Neural Netw. 9, 581–588 (1998)
Kwan, C., Lewis, F.L., Kim, Y.H.: Robust neural network control of flexible-joint robots. Asian J. Control 1, 188–197 (1999)
Kwan, C., Lewis, F.L.: Robust backstepping control of nonlinear systems using neural networks. IEEE Trans. Syst. Man Cybern. Part A: Syst. 30, 753–766 (2000)
He, W., Zhang, S., Ge, S.S.: Adaptive control of a flexible crane system with the boundary output constraint. IEEE Trans. Ind. Electron. 64, 4126–4133 (2014)
Li, D.J., Li, D.P.: Adaptive controller design-based neural networks for output constraint continuous stirred tank reactor. Neurocomputing 153, 159–163 (2015)
He, W., Nie, S.X., Meng, T.T., Liu, Y.J.: Modeling and vibration control for a moving beam with application in a drilling riser. IEEE Trans. Control Syst. Technol., to be published. doi:10.1109/TCST.2016.2577001
He, W., Ge, W.L., Li, Y.C., Liu, Y.J., Yang, C.G., Sun, C.Y.: Model identification and control design for a humanoid robot. IEEE Trans. Syst. Man Cybern.: Syst. 47, 45–57 (2017)
Liu, Z., Lai, G.Y., Zhang, Y., Chen, C.L.P.: Adaptive neural output feedback control of output-constrained nonlinear systems with unknown output nonlinearity. IEEE Trans. Neural Netw. Learn. Syst. 26, 1789–1802 (2015)
Tee, K.P., Ge, S.S.: Control of nonlinear systems with partial state constraints using a barrier Lyapunov function. Int. J. Control 84, 2008–2023 (2011)
Liu, Y.J., Tong, S.C.: Barrier Lyapunov functions for Nussbaum gain adaptive control of full state constrained nonlinear systems. Automatica 76, 143–152 (2017)
Liu, Y.J., Tong, S.C., Chen, C.L.P., Li, D.J.: Adaptive NN control using Integral Barrier Lyapunov Functionals for uncertain nonlinear block-triangular constraint systems. IEEE Trans. Cybern., to be published. doi:10.1109/TCYB.2016.2581173
Choi, J.S., Baek, Y.S.: A single dof magnetic levitation system using time delay control and reduced-order observer. J. Mech. Sci. Technol. 16, 1643–1651 (2002)
Mehrkanoon, S., Shardt, Y.A.W., Suykens, J.A.K.: Estimating the unknown time delay in chemical processes. Eng. Appl. Artif. Intell. 55, 219–230 (2016)
Delgado, E., Diaz-Cacho, M., Bustelo, D., Barreiro, A.: Generic approach to stability under time-varying delay in teleoperation: application to the position-error control of a gantry crane. IEEE/ASME Trans. Mechatron. 18, 1581–1591 (2013)
Wang, M., Chen, B., Liu, X.Y., Shi, P.: Adaptive fuzzy tracking control for a class of perturbed strict-feedback nonlinear time-delay systems. Fuzzy Sets Syst. 159, 949–967 (2008)
Chen, B., Liu, X.P., Liu, K.F., Lin, C.: Novel adaptive neural control design for nonlinear MIMO time-delay systems. Automatica 45, 1554–1560 (2009)
Li, D.P., Li, D.J.: Adaptive neural tracking control for nonlinear time-delay systems with full state constraints. IEEE Trans. Syst. Man Cybern.: Syst., to be published. doi:10.1109/TSMC.2016.2637063
Chang, Y.C., Wu, M.F.: Robust tracking control for a class of flexible-joint time-delay robots using only position measurements. Int. J. Syst. Sci. 47, 3336–3349 (2016)
Acknowledgements
The work is supported by the National Natural Science Foundation of China (61473139, 61622303 and 61603164), the Doctoral Scientific Research Staring Fund of Binzhou University under Grant 2016Y14 and the project for Distinguished Professor of Liaoning Province.
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Li, DP., Liu, YJ., Li, DJ., Tong, S., Meng, D., Wen, GX. (2017). Approximation-Based Adaptive Neural Tracking Control of an Uncertain Robot with Output Constraint and Unknown Time-Varying Delays. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10262. Springer, Cham. https://doi.org/10.1007/978-3-319-59081-3_6
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DOI: https://doi.org/10.1007/978-3-319-59081-3_6
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