Adaptive robust control method for unmanned mining electric shovel excavation systems based on the Hamilton–Jacobi inequality

Abstract

The harsh working environment of unmanned mining electric shovels (UMESs) and the considerable inertia changes during the excavation process in the front-end mechanism pose major challenges to excavation trajectory tracking. In this study, an adaptive Hamilton–Jacobi inequality (HJI)-based robust control method for UMES excavation systems with uncertainty was proposed for trajectory tracking control in intelligent mining. First, the excavation system dynamic model was analyzed using the Lagrangian method, and an excavation resistance prediction model and a material quality prediction model were constructed. The optimal excavation trajectory was described. Then, the HJI theorem was used to design an adaptive controller based on the dynamic model of the UMES, and a generalized regression neural network was introduced to fit the interference term in the control object to ensure the convergence of the control system. Subsequently, a Lyapunov function was constructed to demonstrate the stability of the control system to ensure the reliability of the excavation system. Finally, the method proposed in this study was verified under two different working conditions involving a typical material surface and a real material surface. The numerical simulation results demonstrated that the planned position and velocity were effectively tracked in both working conditions. Furthermore, it maintains an improved tracking effect under different uncertain disturbances, thus verifying the feasibility and robustness of the control system designed in this study.

Graphical abstract

Keywords

unmanned mining electric shovel / trajectory tracking control / Hamilton–Jacobi inequality / generalized regression neural network / Lyapunov function / uncertain disturbance

References

[1] Meng D B, Yang S Y, De Jesus A M P, Zhu S P. A novel Kriging-model-assisted reliability-based multidisciplinary design optimization strategy and its application in the offshore wind turbine tower. Renewable Energy, 2023, 203: 407–420

[2] Topno S A, Sahoo L K, Umre B S. Energy efficiency assessment of electric shovel operating in opencast mine. Energy, 2021, 230: 120703

[3] Zhu A M, Zhang Z Q, Pan W. Developing a fast and accurate collision detection strategy for crane-lift path planning in high-rise modular integrated construction. Advanced Engineering Informatics, 2024, 61: 102509

[4] Meng D B, Yang S Y, Yang H F, De Jesus A M P, Correia J, Zhu S P. Intelligent-inspired framework for fatigue reliability evaluation of offshore wind turbine support structures under hybrid uncertainty. Ocean Engineering, 2024, 307: 118213

[5] Chen Z R, Guan W, Guo J B, Xue D M, Liu Z B, Wang G Q, Quan L. Electric shovel trajectory tracking with inversion sliding mode based on Lyapunov functions. Automation in Construction, 2024, 162: 105364

[6] Song X G, Zhang T C, Yuan Y L, Wang X B, Sun W. Multidisciplinary co-design optimization of the structure and control systems for large cable shovel considering cross-disciplinary interaction. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2020, 234(22): 4353–4365

[7] Usman Shoukat M, Yan L R, Deng D, Imtiaz M, Safdar M, Ali Nawaz S. Cognitive robotics: Deep learning approaches for trajectory and motion control in complex environment. Advanced Engineering Informatics, 2024, 60: 102370

[8] Li X H, Sun Z P, Cao D P, Liu D X, He H. Development of a new integrated local trajectory planning and tracking control framework for autonomous ground vehicles. Mechanical Systems and Signal Processing, 2017, 87: 118–137

[9] Mazare M. Adaptive optimal secure wind power generation control for variable speed wind turbine systems via reinforcement learning. Applied Energy, 2024, 353: 122034

[10] Solea R, Nunes U. Trajectory planning and sliding-mode control based trajectory-tracking for cybercars. Integrated Computer-Aided Engineering, 2007, 14(1): 33–47

[11] Ekrem Ö, Aksoy B. Trajectory planning for a 6-axis robotic arm with particle swarm optimization algorithm. Engineering Applications of Artificial Intelligence, 2023, 122: 106099

[12] Fang S, Ma X H, Zhao Y, Zhang Q, Li Y Y.. Trajectory planning for seven-DOF robotic arm based on quintic polynomial. Proceedings of 11th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC 2019), 2019, 198–201

[13] Wu Y H, Yu Z C, Li C Y, He M J, Hua B, Chen Z M. Reinforcement learning in dual-arm trajectory planning for a free-floating space robot. Aerospace Science and Technology, 2020, 98: 105657

[14] Khan A T, Li S, Kadry S, Nam Y. Control framework for trajectory planning of soft manipulator using optimized RRT algorithm. IEEE Access, 2020, 8: 171730–171743

[15] Wang L, Wu Q, Lin F, Li S, Chen D C. A new trajectory-planning beetle swarm optimization algorithm for trajectory planning of robot manipulators. IEEE Access, 2019, 7: 154331–154345

[16] Wang X B, Song X G, Sun W. Surrogate based trajectory planning method for an unmanned electric shovel. Mechanism and Machine Theory, 2021, 158: 104230

[17] Zhang T C, Fu T, Cui Y H, Song X G. Toward autonomous mining: design and development of an unmanned electric shovel via point cloud-based optimal trajectory planning. Frontiers of Mechanical Engineering, 2022, 17(3): 30

[18] FanR J, Li Y H, YangL M. Trajectory planning based on minimum input energy for the electro-hydraulic cable shovel. In: Proceedings of 2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics. Boston: IEEE, 2020: 397–402

[19] Bi Q S, Wang G Q, Wang Y P, Yao Z W, Hall R. Digging trajectory optimization for cable shovel robotic excavation based on a multi-objective genetic algorithm. Energies, 2020, 13(12): 3118

[20] Zhang T C, Fu T, Song X G, Qu F Z. Multi-objective excavation trajectory optimization for unmanned electric shovels based on pseudospectral method. Automation in Construction, 2022, 136: 104176

[21] Fu T, Zhang T C, Lv Y L, Song X G, Li G, Yue H F. Digital twin-based excavation trajectory generation of uncrewed excavators for autonomous mining. Automation in Construction, 2023, 151: 104855

[22] Zhou Q, Zhao S Y, Li H Y, Lu R Q, Wu C W. Adaptive neural network tracking control for robotic manipulators with dead zone. IEEE Transactions on Neural Networks and Learning Systems, 2019, 30(12): 3611–3620

[23] Tong S C, Min X, Li Y X. Observer-based adaptive fuzzy tracking control for strict-feedback nonlinear systems with unknown control gain functions. IEEE Transactions on Cybernetics, 2020, 50(9): 3903–3913

[24] Van M, Mavrovouniotis M, Ge S S. An adaptive backstepping nonsingular fast terminal sliding mode control for robust fault tolerant control of robot manipulators. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019, 49(7): 1448–1458

[25] Song X N, Sun P, Song S, Stojanovic V. Event-driven NN adaptive fixed-time control for nonlinear systems with guaranteed performance. Journal of the Franklin Institute, 2022, 359(9): 4138–4159

[26] Jin X. Adaptive fixed-time control for MIMO nonlinear systems with asymmetric output constraints using universal barrier functions. IEEE Transactions on Automatic Control, 2019, 64(7): 3046–3053

[27] Hewing L, Kabzan J, Zeilinger M N. Cautious model predictive control using gaussian process regression. IEEE Transactions on Control Systems Technology, 2020, 28(6): 2736–2743

[28] Wu G H, Wang G Q, Bi Q S, Wang Y P, Fang Y, Guo G Y, Qu W T. Research on unmanned electric shovel autonomous driving path tracking control based on improved pure tracking and fuzzy control. Journal of Field Robotics, 2023, 40(7): 1739–1753

[29] Fang Y, Wang S, Bi Q S, Wu G H, Guan W, Wang Y P, Yan C L. Research on path planning and trajectory tracking of an unmanned electric shovel based on improved APF and preview deviation fuzzy control. Machines, 2022, 10(8): 707

[30] Cao Y G, Xie Y B. Dynamic modeling of the front structure of an excavator. Nonlinear Dynamics, 2018, 91(1): 233–247

[31] Fu T, Zhang T C, Cui Y H, Song X G. Novel hybrid physics-informed deep neural network for dynamic load prediction of electric cable shovel. Chinese Journal of Mechanical Engineering, 2022, 35: 147

[32] Wang X B, Sun W, Li E Y, Song X G. Energy-minimum optimization of the intelligent excavating process for large cable shovel through trajectory planning. Structural and Multidisciplinary Optimization, 2018, 58(5): 2219–2237

[33] McKyesE. Developments in Agricultural Engineering. Volume 7: Soil Cutting and Tillage. Amsterdam: Elsevier, 1985

[34] Wei B, Gao F, Chen J, He J, Wu S, Song Q. Mechanics performance of three-degree-of-freedom excavating mechanism of an electric shovel. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2011, 225(6): 1443–1457

[35] Rudnick-Cohen E S, Reich G W, Pankonien A M, Beran P S. Robust optimal design and trajectory planning of an aircraft with morphing airfoil sections. Structural and Multidisciplinary Optimization, 2023, 66(10): 214

[36] Hiltunen M, Heikkilä R, Niskanen I, Immonen M. Open InfraBIM for remote and autonomous excavation. Automation in Construction, 2023, 156: 105148

[37] Ding H G, Sang Z W, Li Z P, Shi J J, Wang Y F, Meng D Y. Trajectory planning and control of large robotic excavators based on inclination-displacement mapping. Automation in Construction, 2024, 158: 105209

[38] Kim Y B, Ha J, Kang H, Kim P Y, Park J, Park F C. Dynamically optimal trajectories for earthmoving excavators. Automation in Construction, 2013, 35: 568–578

[39] Madridano Á, Al-Kaff A, Martín D, de la Escalera A. Trajectory planning for multi-robot systems: Methods and applications. Expert Systems with Applications, 2021, 173: 114660

[40] Zhong X Y, Liu J P, Chen K D, Chen J Q, Wang N Y, He C W, Lu Q H, Cheng Z B, Huang W J. A time-optimal wellbore trajectory design for slide drilling systems. Structural and Multidisciplinary Optimization, 2021, 63(2): 881–896

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