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Broad learning control of a two-link flexible manipulator with prescribed performance and actuator faults

Published online by Cambridge University Press:  14 February 2023

Wenkai Niu ,
Linghuan Kong ,
Yifan Wu ,
Haifeng Huang  and
Wei He Open the ORCID record for Wei He [Opens in a new window]
Wenkai Niu
Affiliation:
School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing, China Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China
Linghuan Kong
Affiliation:
School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing, China Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China
Yifan Wu
Affiliation:
School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing, China Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China
Haifeng Huang
Affiliation:
School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing, China Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China
Wei He*
Affiliation:
School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing, China Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China
*
*Corresponding author. E-mail: weihe@ieee.org
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Abstract

In this paper, we present a broad learning control method for a two-link flexible manipulator with prescribed performance (PP) and actuator faults. The trajectory tracking errors are processed through two consecutive error transformations to achieve the constraints in terms of the overshoot, transient error, and steady-state error. And the barrier Lyapunov function is employed to implement constraints on the transition state variable. Then, the improved radial basis function neural networks combined with broad learning theory are constructed to approximate the unknown model dynamics of flexible robotic manipulator. The proposed fault-tolerant PP control cannot only ensure tracking errors converge into a small region near zero within the preset finite time but also address the problem caused by actuator faults. All the closed-loop error signals are uniformly ultimately bounded via the Lyapunov stability theory. Finally, the feasibility of the proposed control is verified by the simulation results.

Keywords

broad learningflexible manipulatorprescribed performanceactuator faultsneural networks

Information

Type
Research Article
Information
Robotica , Volume 41 , Issue 5 , May 2023 , pp. 1371 - 1388
Copyright
© The Author(s), 2023. Published by Cambridge University Press

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References

Li, M., Du, Z., Ma, X., Dong, W., Wang, Y., Gao, Y. and Chen, W., “A robot chamfering system for special-shaped and thin-walled workpieces,” Assembly Autom. 41(1), 116–130 (2021).CrossRefGoogle Scholar
Lin, R., Huang, H. and Li, M., “An automated guided logistics robot for pallet transportation,” Assembly Autom. 41(1), 45–54 (2021).CrossRefGoogle Scholar
Sun, T., Chen, L., Hou, Z. and Tan, M., “Novel sliding-mode disturbance observer-based tracking control with applications to robot manipulators,” Sci. China Inf. Sci. 64(7), 172205 (2021).CrossRefGoogle Scholar
Huang, H., He, W., Wang, J., Zhang, L. and Fu, Q., “An all servo-driven bird-like flapping-wing aerial robot capable of autonomous flight,” IEEE/ASME Trans. Mechatron. 27(6), 1–11 (2022).CrossRefGoogle Scholar
He, W., Tang, X., Wang, T. and Liu, Z., “Trajectory tracking control for a three-dimensional flexible wing,” IEEE Trans. Control Syst. Technol. 30(5), 2243–2250 (2022).CrossRefGoogle Scholar
He, W., Mu, X., Zhang, L. and Zou, Y., “Modeling and trajectory tracking control for flapping-wing micro aerial vehicles,” IEEE/CAA J. Autom. Sin. 8(1), 148–156 (2021).CrossRefGoogle Scholar
Chang, W., Li, Y. and Tong, S., “Adaptive fuzzy backstepping tracking control for flexible robotic manipulator,” IEEE/CAA J. Autom. Sin. 8(12), 1923–1930 (2021).CrossRefGoogle Scholar
Pradhan, S. K. and Subudhi, B., “Position control of a flexible manipulator using a new nonlinear self-tuning pid controller,” IEEE/CAA J. Autom. Sin. 7(1), 136–149 (2020).Google Scholar
Fadilah, A., Abdul, R. H., Zaharuddin, M. and Ariffanan, M., “Adaptive pid actuator fault tolerant control of single-link flexible manipulator,” Trans. Inst. Meas. Control 41(4), 1019–1031 (2018).Google Scholar
Sun, W., Su, S., Xia, J. and Nguyen, V., “Adaptive fuzzy tracking control of flexible-joint robots with full-state constraints,” IEEE Trans. Syst. Man Cybern.: Syst. 49(11), 2201–2209 (2019).CrossRefGoogle Scholar
Sun, C., Gao, H., He, W. and Yu, Y., “Fuzzy neural network control of a flexible robotic manipulator using assumed mode method,” IEEE Trans. Neural Netw. Learn. Syst. 29(11), 5214–5227 (2018).CrossRefGoogle ScholarPubMed
Kong, L., He, W., Yang, C., Li, Z. and Sun, C., “Adaptive fuzzy control for coordinated multiple robots with constraint using impedance learning,” IEEE Trans. Cybern. 49(8), 3052–3063 (2019).CrossRefGoogle ScholarPubMed
Li, Z., Li, X., Li, Q., Su, H., Kan, Z. and He, W., “Human-in-the-loop control of soft exosuits using impedance learning on different terrains,” IEEE Trans. Robot. 38(5), 1–10 (2022). doi: 10.1109/TRO.2022.3160052.CrossRefGoogle Scholar
Wang, H. and Kang, S., “Adaptive neural command filtered tracking control for flexible robotic manipulator with input dead-zone,” IEEE Access 7(99), 22675–22683 (2019).CrossRefGoogle Scholar
Kong, L., He, W., Liu, Z., Yu, X. and Silvestre, C., “Adaptive tracking control with global performance for output-constrained MIMO nonlinear systems,” IEEE Trans. Autom. Control, 1–8 (2022). doi: 10.1109/TAC.2022.3201258.Google Scholar
Huang, A.-C. and Chen, Y.-C., “Adaptive sliding control for single-link flexible-joint robot with mismatched uncertainties,” IEEE Trans. Control Syst. Technol. 12(5), 770–775 (2004).CrossRefGoogle Scholar
Salgado, I. and Chairez, I., “Adaptive unknown input estimation by sliding modes and differential neural network observer,” IEEE Trans. Neural Netw. Learn. Syst. 29(8), 3499–3509 (2017).Google ScholarPubMed
Liang, D., Song, Y., Sun, T. and Jin, X., “Dynamic modeling and hierarchical compound control of a novel 2-dof flexible parallel manipulator with multiple actuation modes,” Mech. Syst. Signal Process 103, 413–439 (2018).CrossRefGoogle Scholar
Feng, Y., Shi, W., Cheng, G., Huang, J. and Liu, Z., “Benchmarking framework for command and control mission planning under uncertain environment,” Soft Comput. 24(4), 2463–2478 (2020).CrossRefGoogle Scholar
Feng, Y., Yang, X. and Cheng, G., “Stability in mean for multi-dimensional uncertain differential equation,” Soft Comput. 22(17), 5783–5789 (2018).CrossRefGoogle Scholar
Kong, L., He, W., Chen, W., Zhang, H. and Wang, Y., “Dynamic movement primitives based robot skills learning,” Mach. Intell. Res., (2022), in press. doi: 10.1007/s11633-022-1346-z.Google Scholar
Dang, Q., Xu, W. and Yuan, Y., “A dynamic resource allocation strategy with reinforcement learning for multimodal multi-objective optimization,” Mach. Intell. Res. 19(2), 138–152 (2022).CrossRefGoogle Scholar
Yang, Y., Modares, H., Vamvoudakis, K. G., He, W., Xu, C.-Z. and Wunsch, D. C., “Hamiltonian-driven adaptive dynamic programming with approximation errors,” IEEE Trans. Cybern. 52(12), 1–12 (2021). doi: 10.1109/TCYB.2021.3108034.Google Scholar
Yang, Y., Kiumarsi, B., Modares, H. and Xu, C., “Mode l-free $\lambda$ -policy iteration for discrete-time linear quadratic regulation,” IEEE Trans. Neural Netw. Learn. Syst. 34(2), 635–649 (2023). doi: 10.1109/TNNLS.2021.3098985.Google Scholar
Chen, C. L. P. and Liu, Z., “Broad learning system: An effective and efficient incremental learning system without the need for deep architecture,” IEEE Trans. Neural Netw. Learn. Syst. 29(1), 10–24 (2018).CrossRefGoogle ScholarPubMed
Chen, C. L. P., Liu, Z. and Feng, S., “Universal approximation capability of broad learning system and its structural variations,” IEEE Trans. Neural Netw. Learn. Syst. 30(4), 1191–1204 (2019).CrossRefGoogle ScholarPubMed
Huang, H., Zhang, T., Yang, C. and Chen, C. L. P., “Motor learning and generalization using broad learning adaptive neural control,” IEEE Trans. Ind. Electron. 67(10), 8608–8617 (2020).CrossRefGoogle Scholar
Peng, G., Chen, C. L. P., He, W. and Yang, C., “Neural-learning-based force sensorless admittance control for robots with input deadzone,” IEEE Trans. Ind. Electron. 68(6), 5184–5196 (2021).CrossRefGoogle Scholar
Ghaf-Ghanbari, P., Mazare, M. and Taghizadeh, M., “Active fault-tolerant control of a schonflies parallel manipulator based on time delay estimation,” Robotica 39(8), 1518–1535 (2021).CrossRefGoogle Scholar
Liu, Z., Han, Z., Zhao, Z. and He, W., “Modeling and adaptive control for a spatial flexible spacecraft with unknown actuator failures,” Sci. China Inf. Sci. 64(5), 152208 (2021).CrossRefGoogle Scholar
Smaeilzadeh, S. M. and Golestani, M., “Finite-time fault-tolerant adaptive robust control for a class of uncertain non-linear systems with saturation constraints using integral backstepping approach,” IET Control Theory Appl. 12(15), 2109–2117 (2018).CrossRefGoogle Scholar
Van, M., Ge, S. S. and Ren, H., “Finite time fault tolerant control for robot manipulators using time delay estimation and continuous nonsingular fast terminal sliding mode control,” IEEE Trans. Cybern. 47(7), 1681–1693 (2017).CrossRefGoogle Scholar
Shen, Q., Yue, C., Goh, C. H. and Wang, D., “Active fault-tolerant control system design for spacecraft attitude maneuvers with actuator saturation and faults,” IEEE Trans. Ind. Electron. 66(5), 3763–3772 (2018).CrossRefGoogle Scholar
Kong, L., He, W., Yang, W., Li, Q. and Kaynak, O., “Fuzzy approximation-based finite-time control for a robot with actuator saturation under time-varying constraints of work space,” IEEE Trans. Cybern. 51(10), 4873–4884 (2021).CrossRefGoogle ScholarPubMed
Kong, L., He, W., Dong, Y., Cheng, L., Yang, C. and Li, Z., “Asymmetric bounded neural control for an uncertain robot by state feedback and output feedback,” IEEE Trans. Syst. Man Cybern.: Syst. 51(3), 1735–1746 (2021).Google Scholar
Li, H., Zhao, S., He, W. and Lu, R., “Adaptive finite-time tracking control of full state constrained nonlinear systems with dead-zone,” Automatica 100, 99–107 (2019).CrossRefGoogle Scholar
Ilchmann, A., Ryan, E. P. and Trenn, S., “Tracking control: Performance funnels and prescribed transient behaviour,” Syst. Control. Lett. 54(7), 655–670 (2005).CrossRefGoogle Scholar
Wang, S., Ren, X., Jing, N. and Zeng, T., “Extended-state-observer-based funnel control for nonlinear servomechanisms with prescribed tracking performance,” IEEE Trans. Autom. Sci. Eng. 14(1), 98–108 (2017).CrossRefGoogle Scholar
Bechlioulis, C. P. and Rovithakis, G. A., “Robust adaptive control of feedback linearizable mimo nonlinear systems with prescribed performance,” IEEE Trans. Autom. Control 53(9), 2090–2099 (2008).CrossRefGoogle Scholar
Wang, P., Zhang, X. and Zhu, J., “Online performance-based adaptive fuzzy dynamic surface control for nonlinear uncertain systems under input saturation,” IEEE Trans. Fuzzy Syst. 27(2), 209–220 (2018).CrossRefGoogle Scholar
Sun, W., Wu, Y.-Q. and Sun, Z.-Y., “Command filter-based finite-time adaptive fuzzy control for uncertain nonlinear systems with prescribed performance,” IEEE Trans. Fuzzy Syst. 28(12), 3161–3170 (2020).CrossRefGoogle Scholar
Guo, D., Li, A., Cai, J., Feng, Q. and Shi, Y., “Inverse kinematics of redundant manipulators with guaranteed performance,” Robotica 40(1), 170–190 (2022).CrossRefGoogle Scholar
Gul, S., Zergeroglu, E., Tatlicioglu, E. and Kilinc, M. V., “Desired model compensation-based position constrained control of robotic manipulators,” Robotica 40(2), 279–293 (2022).CrossRefGoogle Scholar
Guo, Q., Zhang, Y., Celler, B. G. and Su, S. W., “Neural adaptive backstepping control of a robotic manipulator with prescribed performance constraint,” IEEE Trans. Neural Netw. Learn. Syst. 30(12), 3572–3583 (2018).CrossRefGoogle ScholarPubMed
Chen, Z., Wang, M. and Zou, Y., “Dynamic learning from adaptive neural control for flexible joint robot with tracking error constraints using high-gain observer,” Syst. Sci. Control Eng. Open Access J. 6(3), 177–190 (2018).CrossRefGoogle Scholar
Zhou, B. and Zhang, K.-K., “A linear time-varying inequality approach for prescribed time stability and stabilization,” IEEE Trans. Cybern., 1–10 (2022). doi: 10.1109/TCYB.2022.3164658.Google Scholar
Espitia, N. and Perruquetti, W., “Predictor-feedback prescribed-time stabilization of lti systems with input delay,” IEEE Trans. Autom. Control 67(6), 2784–2799 (2022). doi: 10.1109/TAC.2021.3093527.CrossRefGoogle Scholar
Zhang, S., Liu, R., Peng, K. and He, W., “Boundary output feedback control for a flexible two-link manipulator system with high-gain observers,” IEEE Trans. Control Syst. Technol. 29(2), 835–840 (2019).CrossRefGoogle Scholar
Huang, X., Song, Y. and Lai, J., “Neuro-adaptive control with given performance specifications for strict feedback systems under full-state constraints,” IEEE Trans. Neural Netw. Learn. Syst. 30(1), 25–34 (2018).CrossRefGoogle ScholarPubMed
Ahanda, J. J.-B. M., Mbede, J. B., Melingui, A. and Zobo, B. E., “Robust adaptive command filtered control of a robotic manipulator with uncertain dynamic and joint space constraints,” Robotica 36(5), 767–786 (2018).CrossRefGoogle Scholar
Gao, H., He, W., Zhou, C. and Sun, C., “Neural network control of a two-link flexible robotic manipulator using assumed mode method,” IEEE Trans. Ind. Inform. 15(2), 755–765 (2018).CrossRefGoogle Scholar
Chen, G., Song, Y. and Lewis, F. L., “Distributed fault-tolerant control of networked uncertain Euler-Lagrange systems under actuator faults,” IEEE Trans. Cybern. 47(7), 1706–1718 (2017).CrossRefGoogle Scholar
Zhang, S., Dong, Y., Ouyang, Y., Yin, Z. and Peng, K., “Adaptive neural control for robotic manipulators with output constraints and uncertainties,” IEEE Trans. Neural Netw. Learn. Syst. 29(11), 5554–5564 (2018).CrossRefGoogle ScholarPubMed
Ren, B., Ge, S. S., Tee, K. P. and Lee, T. H., “Adaptive neural control for output feedback nonlinear systems using a barrier Lyapunov function,” IEEE Trans. Neural Netw. 21(8), 1339–1345 (2010).Google ScholarPubMed
Karayiannidis, Y. and Doulgeri, Z., “Model-free robot joint position regulation and tracking with prescribed performance guarantees,” Robot. Auton. Syst. 60(2), 214–226 (2012).CrossRefGoogle Scholar