Adaptive backstepping control of tracked robot running trajectory based on real-time slip parameter estimation
Keywords:
tracked robot, trajectory control, adaptive backstepping control, neural networks, slip parameter, sliding mode observerAbstract
To ensure the stable driving of tracked robots in a complex farmland environment, an adaptive backstepping control method for tracked robots was proposed based on real-time slip parameter estimation. According to the kinematics analysis method, the kinematic model of the tracked robot was established, and then, its pose error differential equation was further obtained. On this basis, the trajectory tracking controller of the tracked robot was designed based on the backstepping control theory. Subsequently, according to the trajectory tracking error of the tracked robot, the back propagation neural network (BPNN) was used to adaptively adjust the control parameters in the backstepping controller, and the inputs of the BPNN are the trajectory tracking error xe, ye, θe. After that, the soft-switching sliding mode observer (SSMO) was designed to identify the slip parameters during the running of the tracked robot. And then the parameters were compensated into the adaptive backstepping controller to reduce the trajectory tracking error. The simulation results show that the proposed adaptive backstepping control method with SSMO can improve the accuracy of the trajectory tracking control of the tracked robot. Additionally, the designed SSMO can accurately estimate the slip parameters. Keywords: tracked robot, trajectory control, adaptive backstepping control, neural networks, slip parameter, sliding mode observer DOI: 10.25165/j.ijabe.20201304.5739 Citation: Lu E, Ma Z, Li Y M, Xu L Z, Tang Z. Adaptive backstepping control of tracked robot running trajectory based on real-time slip parameter estimation. Int J Agric & Biol Eng, 2020; 13(4): 178–187.References
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[2] Guo T Y, Guo J L, Huang B, Peng H. Power consumption of tracked and wheeled small mobile robots on deformable terrains–model and experimental validation. Mechanism & Machine Theory, 2019; 133: 347–364.
[3] Zeng Q H, Ma X J, Yuan D, Liu C G. Motion decoupling and variable structure control of dual-motor electric drive tracked vehicle. Control Theory & Applications, 2015; 32(8): 1080–1089. (in Chinese)
[4] Bian Y M, Yang M, Liu Y C, Yang G. Research on trajectory tracking control of a tracked mobile robot. Chinese Journal of Construction Machinery, 2018; 16(3): 189–193, 206. (in Chinese)
[5] Han J, Ren G Q, Li D W. Trajectory tracking control for tracked mobile robot with moving limitation. Computer Measurement & Control, 2017; 25(12): 86–89. (in Chinese)
[6] Jiao J, Chen J, Qiao Y, Wang W Z, Wang M S, Gu L C, et al. Adaptive sliding mode control of trajectory tracking based on DC motor drive for agricultural tracked robot. Transactions of the CSAE, 2018; 34(4): 64–70. (in Chinese)
[7] Wang B Y, Gong J W, Gao T Y, Zhang R Z, Chen H Y, Xi J Q. Longitudinal and lateral path following coordinated control method of tracked vehicle based on double-layer driver model. Acta Armamentarii, 2018; 39(9): 1675–1682. (in Chinese)
[8] Asif M, Khan M. J, Cai N. Adaptive sliding mode dynamic controller with integrator in the loop for nonholonomic wheeled mobile robot trajectory tracking. International Journal of Control, 2014; 87(5): 964–975.
[9] Yokoyama M, Ikarashi J, Okawa A. Adaptive control of a skid-steer mobile robot with uncertain cornering stiffness. Mechanical Engineering Journal, 2015; 2(4): 15–00040. doi: 10.1299/mej.15-00040.
[10] Wu X, Jin P, Zou T, Qi Z, Xiao H, Lou P. Backstepping trajectory tracking based on fuzzy sliding mode control for differential mobile robots. J Intell Robot Syst, 2019; 96: 109–121.
[11] Xiong G M, Lu H, Guo K H, Chen H Y. Research on trajectory prediction of tracked vehicles based on real time slip estimation. Acta Armamentarii, 2017; 38(3): 600–607. (in Chinese)
[12] Zhu L, Guo J, Liu G F. Slip estimation method of track robot. Journal of Central South University (Science and Technology), 2013; 44(8): 3173–3178. (in Chinese)
[13] Bei X Y, Ping X L, Gao W Y. Trajectory tracking control of wheeled mobile robots under longitudinal slipping conditions. China Mechanical Engineering, 2018; 29(16): 1958–1964. (in Chinese)
[14] Song Z. B, Zweiri Y. H, Seneviratne L. D, Althoefer K. Non-linear observer for slip estimation of skid-steering vehicles. Proceedings 2006 IEEE International Conference on Robotics and Automation, Orlando: IEEE, 2006; pp.1499–1504.
[15] Song Z B, Song X J, Altheoer K, Zweiri Y, Seneviratne L. Non-linear observer for slip parameter estimation of unmanned wheeled vehicles. 2007 IFAC Proceedings Volumes, Harbin: IEEE, 2007; pp.458–463.
[16] Iossaqui J G, Camino J F, Zampieri D E. A nonlinear control design for tracked robots with longitudinal slip. IFAC Proceedings Volumes, 2011; 44(1): 5932–5937.
[17] Zhao Y, Tian Y T, Lian Y F, Hu L L. A sliding mode observer of road condition estimation for four-wheel-independent-drive electric vehicles. Proceeding of the 11th World Congress on Intelligent Control and Automation, Shenyang: IEEE, 2014; pp.4390–4395.
[18] Kanayama Y, Kimura Y, Miyazaki F, Noguchi T. A stable tracking control method for an autonomous mobile robot. Proceedings, IEEE International Conference on Robotics and Automation, Cincinnati: IEEE, 1990; 1: pp.384–389.
[19] Qu Y, Ning D, Lai Z C, Cheng Q, Mu L N. Neural networks based on PID control for greenhouse temperature. Transactions of the CSAE, 2011; 27(2): 307–311.
[20] Liu J K. Advanced PID Control MATLAB simulation (2nd Edition). Beijing: Publishing House of Electronics Industry, 2004; pp.162–164.
[21] Sun S Q, Li S. Application of PID neural network in headbox multivariable decoupling control. 2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet), Yichang: IEEE, 2012; pp.2427–2430.
[22] Zhou B, Dai X Z, Han J D. Online modelling and tracking control of mobile robots with slippage in outdoor environments. Robot, 2011; 33(3): 265–272. (in Chinese)
[23] Wang Z Y, Li Y D, Zhu L. Dual adaptive neural sliding mode control of nonholonomic mobile robot. Journal of Mechanical Engineering, 2010; 46(23): 16–22. (in Chinese)
[24] Lu E, Li W, Yang X F, Xu S Y. Composite sliding mode control of a permanent magnet direct-driven system for a mining scraper conveyor. IEEE Access, 2017; 5: 22399–22408.
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Published
2020-08-07
How to Cite
Lu, E., Ma, Z., Li, Y., Xu, L., & Tang, Z. (2020). Adaptive backstepping control of tracked robot running trajectory based on real-time slip parameter estimation. International Journal of Agricultural and Biological Engineering, 13(4), 178–187. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/5739
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Information Technology, Sensors and Control Systems
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