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Hitting point tracking control of tank projectile based on Chebyshev exterior ballistic polynomial: an adaptive robust feedback approach

基于Chebyshev外弹道多项式的坦克弹丸落点跟踪控制:一种自适应鲁棒反馈方法

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Abstract

In this paper, the tracking control problem of the projectile hitting point of the moving tank is studied. First, a multi-body dynamic model with stability systems is established. Second, the nonlinear coupling dynamic equation of turret-barrel pointing system is established. Third, the trajectory equation of exterior ballistic (EB) projectile in six degree-of-freedom is established, and the pointing problem was transformed into a problem of hitting point tracking through coordinate transformation. Forth, an adaptive robust feedback control method is proposed to make the predicted hitting point tracking the expected position accurately. Finally, Chebyshev surrogate model is used to replace the EB differential equation, which effectively reduces the time required by co-simulation. This paper combines the EB process with the tracking control problem, which effectively ensures the first-round chance of hit for the tank gun.

摘要

本文研究了运动坦克弹丸落点的跟踪控制问题. 首先建立稳定系统的多体动力学模型, 其次建立塔身指向系统的非线性耦合动力学方程. 随后建立6自由度外弹道弹丸的弹道方程, 通过坐标变换将瞄准问题转化为落点跟踪问题. 提出一种自适应鲁棒反馈控制方法, 使预测落点精确跟踪期望位置. 最后采用Chebyshev代理模型代替EB微分方程, 有效减少协同仿真所需的时间. 本文将电子束过程与跟踪控制问题相结合, 有效地保证了坦克炮的首轮命中率.

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References

  1. T. Dursun, F. Büyükcivelek, and Ç. Utlu, A review on the gun barrel vibrations and control for a main battle tank, Defence Tech. 13, 353 (2017).

    Article  Google Scholar 

  2. Y. J. Fang, and J. W. Jiang, Stochastic exterior ballistic model of submunitions and its monte carlo solution (in Chinese), Trans. Beijing Inst. Tech. 29, 850 (2009).

    Google Scholar 

  3. P. Ma, Y. Zhou, X. Shang, and M. Yang, Firing accuracy evaluation of electromagnetic railgun based on multicriteria optimal latin hypercube design, IEEE Trans. Plasma Sci. 45, 1503 (2017).

    Article  Google Scholar 

  4. Y. Xia, F. Pu, M. Fu, and L. Ye, Modeling and compound control for unmanned turret system with coupling, IEEE Trans. Ind. Electron. 63, 5794 (2016).

    Article  Google Scholar 

  5. K. K. Ahn, and T. D. C. Thanh, Nonlinear PID control to improve the control performance of the pneumatic artificial muscle manipulator using neural network, J. Mech. Sci. Technol. 19, 106 (2005).

    Article  Google Scholar 

  6. H. Xian, W. Yang, W. Zhang, X. Duan, and H. Yu, in The application of dual-PID regulation based on sliding mode control in a tank artillery stabilizer: 2011 Fourth International Conference on Intelligent Computation Technology and Automation (Shenzhen, 2011), pp. 731–733.

  7. Q. Gao, J. Chen, L. Wang, S. Xu, and Y. Hou, Multiobjective optimization design of a fractional order PID controller for a gun control system., Sci. World J. 2013(1), 1 (2013).

    Google Scholar 

  8. X. Zhongxiang’, and C. Lixin, New evaluation method for shared firing table of two ammunition types, J. Phys.-Conf. Ser. 1507, 102046 (2020).

    Article  Google Scholar 

  9. X. M. Yan, Y. Wen, and B. He, Compilation of firing tables methods based on statistical theory of Bayes (in Chinese), Fire Control Command Control 42, 121 (2017).

    Google Scholar 

  10. M. Mady, M. Khalil, and M. Yehia, Modelling and production of artillery firing-tables: case-study, J. Phys.-Conf. Ser. 1507, 082043 (2020).

    Article  Google Scholar 

  11. Q. Gao, Z. Sun, G. Yang, R. Hou, L. Wang, and Y. Hou, A novel active disturbance rejection-based control strategy for a gun control system, J. Mech. Sci. Technol. 26, 4141 (2012).

    Article  Google Scholar 

  12. X. Li, and K. Mehran, Model-based control and stability analysis of discrete-time polynomial fuzzy systems with time delay and positivity constraints, IEEE Trans. Fuzzy Syst. 27, 2090 (2019).

    Article  Google Scholar 

  13. Z. Wang, C. Hu, Y. Zhu, S. He, K. Yang, and M. Zhang, Neural network learning adaptive robust control of an industrial linear motor-driven stage with disturbance rejection ability, IEEE Trans. Ind. Inf. 13, 2172 (2017).

    Article  Google Scholar 

  14. H. Yin, Y. H. Chen, D. Yu, and H. Lu, Nash-game-oriented optimal design in controlling fuzzy dynamical systems, IEEE Trans. Fuzzy Syst. 27, 1659 (2019).

    Article  Google Scholar 

  15. H. Yin, Y. H. Chen, and D. Yu, Rendering optimal design in controlling fuzzy dynamical systems: a cooperative game approach, IEEE Trans. Ind. Inf. 15, 4430 (2019).

    Article  Google Scholar 

  16. V. P. Vu, and W. J. Wang, Decentralized observer-based controller synthesis for a large-scale polynomial T-S fuzzy system with nonlinear interconnection terms, IEEE Trans. Cybern. 51, 3312 (2021).

    Article  Google Scholar 

  17. L. B. Wu, and G. H. Yang, Adaptive fault-tolerant control of a class of nonaffine nonlinear systems with mismatched parameter uncertainties and disturbances, NOnlinear Dyn 82, 1281 (2015).

    Article  MathSciNet  Google Scholar 

  18. Y. H. Chen, Constraint-following servo control design for mechanical systems, J. Vib. Control 15, 369 (2009).

    Article  MathSciNet  Google Scholar 

  19. H. Sun, H. Zhao, K. Huang, M. Qiu, S. Zhen, and Y. H. Chen, A fuzzy approach for optimal robust control design of an automotive electronic throttle system, IEEE Trans. Fuzzy Syst. 26, 694 (2018).

    Article  Google Scholar 

  20. H. Sun, H. Zhao, K. Huang, and S. Zhen, A new approach for vehicle lateral velocity and yaw rate control with uncertainty, Asian J. Control 20, 216 (2018).

    Article  MathSciNet  Google Scholar 

  21. H. Sun, Y. H. Chen, and H. Zhao, Adaptive robust control methodology for active roll control system with uncertainty, Nonlinear Dyn. 92, 359 (2018).

    Article  Google Scholar 

  22. F. Xu, G. Yang, L. Wang, and Q. Sun, Interval uncertain optimization for interior ballistics based on Chebyshev surrogate model and affine arithmetic, Eng. Optimization 53, 1331 (2021).

    Article  MathSciNet  Google Scholar 

  23. L. Swiler, R. Slepoy, and A.Giunta, in Evaluation of sampling methods in constructing response surface approximations: 47th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference 14th AIAA/ASME/AHS Adaptive Structures Conference 7th (Newport, 2006), p. 1827.

  24. R. F. Gunst, Response surface methodology: process and product optimization using designed experiments, Technometrics 38, 284 (1996).

    Article  Google Scholar 

  25. S. Shan, and G. G. Wang, Metamodeling for high dimensional simulation-based design problems, J. Mech. Des. 132, 051009 (2010).

    Article  Google Scholar 

  26. T. W. Simpson, T. M. Mauery, J. J. Korte, and F. Mistree, Kriging models for global approximation in simulation-based multidisciplinary design optimization, AIAA J. 39, 2233 (2001).

    Article  Google Scholar 

  27. J. Wu, Y. Zhang, L. Chen, and Z. Luo, A Chebyshev interval method for nonlinear dynamic systems under uncertainty, Appl. Math. Model. 37, 4578 (2013).

    Article  MathSciNet  Google Scholar 

  28. J. Wu, Z. Luo, N. Zhang, and Y. Zhang, A new interval uncertain optimization method for structures using Chebyshev surrogate models, Comput. Struct. 146, 185 (2015).

    Article  Google Scholar 

  29. H. Yin, D. Yu, S. Yin, and B. Xia, Possibility-based robust design optimization for the structural-acoustic system with fuzzy parameters, Mech. Syst. Signal Process. 102, 329 (2018).

    Article  Google Scholar 

  30. W. Qin, H. Yin, D. J. Yu, and W. B. Shangguan, A Chebyshev convex method for mid-frequency analysis of built-up structures with large convex uncertainties, Eng. Computat. 37, 3431 (2020).

    Article  Google Scholar 

  31. Y. Chen, X. Rui, Z. Zhang, and A. Shehzad, Improved incremental transfer matrix method for nonlinear rotor-bearing system, Acta Mech. Sin. 36, 1119 (2020).

    Article  MathSciNet  Google Scholar 

  32. Y. Chen, and G. Yang, Dynamic simulation of tank stabilizer based on adaptive control, P. I. Mech. Eng. C-J. Mech. Eng. Sci. 233, 3038 (2019).

    Article  Google Scholar 

  33. Y. Ma, G. Yang, Q. Sun, X. Wang, and Q. Sun, Adaptive robust control for tank stability: A constraint-following approach, P. I. Mech. Eng. I-J. Syst. Control Eng. 235, 3 (2021).

    Google Scholar 

  34. C. Y. Su, Y. Stepanenko, J. Svoboda, and T. P. Leung, Robust adaptive control of a class of nonlinear systems with unknown backlash-like hysteresis, IEEE Trans. Automat. Contr. 45, 2427 (2000).

    Article  MathSciNet  Google Scholar 

  35. Y. Liu, X. Gao, and X. Yang, Research of control strategy in the large electric cylinder position servo system, Math. Problems Eng. 2015, 1 (2015).

    Google Scholar 

  36. B. Tian, and S. Bhattacharya, Modelling and control of a spatial dynamic cable, Acta Mech. Sin. 35, 866 (2019).

    Article  MathSciNet  Google Scholar 

  37. M. Corless, and G. Leitmann, Continuous state feedback guaranteeing uniform ultimate boundedness for uncertain dynamic systems, IEEE Trans. Automat. Contr. 26, 1139 (1981).

    Article  MathSciNet  Google Scholar 

  38. S. Gutman, and G. Leitmann, in Stabilizing feedback control for dynamical systems with bounded uncertainty: IEEE Conference on Decision and Control including the 15th Symposium on Adaptive Processes (Clearwater, 1976), pp. 94–99.

  39. Q. Sun, X. Wang, G. Yang, Y. H. Chen, and P. Duan, Robust pointing control of marching tank gun with matched and mismatched uncertainty, IEEE Trans. Cybern., 1 (2021).

  40. B. R. Barmish, M. Corless, and G. Leitmann, A new class of stabilizing controllers for uncertain dynamical systems, SIAM J. Control Optim. 21, 246 (1983).

    Article  MathSciNet  Google Scholar 

  41. ISO 8608: 2016 Mechanical vibration-road surface profiles-reporting of measured data (BSI Standards Publication, London, 2016).

  42. M. Abramowitz, I. A. Stegun, and R. H. Romer, Handbook of mathematical functions with formulas, graphs, and mathematical tables (1988).

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Funding

This word was supported by the Natural Science Foundation of Jiangsu Province (Grant No. BK20180474), the National Natural Science Foundation of China (Grant Nos. 51805263, 51705253, and 11572158) and the Natural Science Foundation for Post-doctoral Scientists of China (Grant No. BX2021126).

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Correspondence to Guolai Yang  (杨国来).

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Ma, Y., Yang, G., Sun, Q. et al. Hitting point tracking control of tank projectile based on Chebyshev exterior ballistic polynomial: an adaptive robust feedback approach. Acta Mech. Sin. 38, 521234 (2022). https://doi.org/10.1007/s10409-021-09046-x

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  • DOI: https://doi.org/10.1007/s10409-021-09046-x

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