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Abstract: In this study, we develop an adaptive neural network based boundary control method for a flexible marine riser system with unknown nonlinear disturbances and output constraints to suppress vibrations. We begin with describing the dynamic behavior of the riser system using a distributed parameter system with partial differential equations. To compensate for the effect of nonlinear disturbances, we construct a neural network based boundary controller using a radial basis neural network to reduce vibrations. Under the proposed boundary controller, the state of the riser is guaranteed to be uniformly bounded based on the Lyapunov method. The proposed methodology provides a way to integrate neural networks into boundary control for other flexible robotic manipulator systems. Finally, numerical simulations are given to demonstrate the effectiveness of the proposed control method.

输出受限的柔性海洋立管自适应神经网络边界控制

[1]Chen Z, Huang FH, Chen WJ, et al., 2020. RBFNN-based adaptive sliding mode control design for delayed nonlinear multilateral telerobotic system with cooperative manipulation. IEEE Trans Ind Inform, 16(2):1236-1247.

[2]Do KD, Pan J, 2008. Boundary control of transverse motion of marine risers with actuator dynamics. J Sound Vib, 318(4-5):768-791.

[3]Gao Y, Wu HN, Wang JW, et al., 2014. Feedback control design with vibration suppression for flexible air-breathing hypersonic vehicles. Sci China Inform Sci, 57(3):1-14.

[4]Ge SS, He W, How BVE, et al., 2010. Boundary control of a coupled nonlinear flexible marine riser. IEEE Trans Contr Syst Technol, 18(5):1080-1091.

[5]Guo F, Liu Y, Wu YL, et al., 2018. Observer-based backstepping boundary control for a flexible riser system. Mech Syst Signal Process, 111:314-330.

[6]Hardy GH, Littlewood JE, Pólya G, 1952. Inequalities. Cambridge University Press, Cambridge, USA.

[7]He W, Ge SS, How BVE, et al., 2011. Robust adaptive boundary control of a flexible marine riser with vessel dynamics. Automatica, 47(4):722-732.

[8]He W, Sun C, Ge SS, 2015. Top tension control of a flexible marine riser by using integral-barrier Lyapunov function. IEEE/ASME Trans Mech, 20(2):497-505.

[9]He W, Ouyang YC, Hong J, 2017. Vibration control of a flexible robotic manipulator in the presence of input deadzone. IEEE Trans Ind Inform, 13(1):48-59.

[10]He W, Mu XX, Chen YN, et al., 2018. Modeling and vibration control of the flapping-wing robotic aircraft with output constraint. J Sound Vib, 423:472-483.

[11]He XY, Zhao ZJ, Su JY, et al., 2021. Adaptive inverse control of a vibrating coupled vessel-riser system with input backlash. IEEE Trans Syst Man Cybern Syst, 51(8):4706-4715.

[12]Herrera CA, McFarland DM, Bergman LA, et al., 2017. Methodology for nonlinear quantification of a flexible beam with a local, strong nonlinearity. J Sound Vib, 388:298-314.

[13]Jiang TT, Liu JK, He W, 2015. Boundary control for a flexible manipulator based on infinite dimensional disturbance observer. J Sound Vib, 348:1-14.

[14]Luo Z, Guo B, Morgül Ö, 1999. Stability and Stabilization of Infinite Dimensional Systems with Applications. Springer-Verlag London, UK.

[15]Ma YF, Lou XY, Wu W, 2021. Sliding mode vibration control of Euler–Bernoulli beam with unknown bounded disturbances. Comput Electr Eng, 96:107504.

[16]Rahn CD, 2001. Mechatronic Control of Distributed Noise and Vibration. Springer, Berlin, Germany.

[17]Smyshlyaev A, Guo BZ, Krstic M, 2009. Arbitrary decay rate for Euler-Bernoulli beam by backstepping boundary feedback. IEEE Trans Autom Contr, 54(5):1134-1140.

[18]Wan M, Yin YX, Liu J, et al., 2021. Active boundary control of vibrating marine riser with constrained input in three-dimensional space. Nonl Dyn, 106(3):2329-2345.

[19]Wang JW, Liu YQ, Sun CY, 2019. Adaptive neural boundary control design for nonlinear flexible distributed parameter systems. IEEE Trans Contr Syst Technol, 27(5):2085-2099.

[20]Wang N, Gao Y, Zhang XF, 2021. Data-driven performance-prescribed reinforcement learning control of an unmanned surface vehicle. IEEE Trans Neur Netw Learn Syst, 32(12):5456-5467.

[21]Xue T, Wang ZW, Zhang T, et al., 2020. Fixed-time constrained acceleration reconstruction scheme for robotic exoskeleton via neural networks. Front Inform Technol Electron Eng, 21(5):705-722.

[22]Yin X, Zhang QC, Wang H, et al., 2020. RBFNN-based minimum entropy filtering for a class of stochastic nonlinear systems. IEEE Trans Autom Contr, 65(1):376-381.

[23]Yu QX, Hou ZS, Bu XH, et al., 2020. RBFNN-based data-driven predictive iterative learning control for nonaffine nonlinear systems. IEEE Trans Neur Netw Learn Syst, 31(4):1170-1182.

[24]Zhang JH, Liu XW, Xia YQ, et al., 2016. Disturbance observer-based integral sliding-mode control for systems with mismatched disturbances. IEEE Trans Ind Electron, 63(11):7040-7048.

[25]Zhang XP, Xu WW, Nair SS, et al., 2005. PDE modeling and control of a flexible two-link manipulator. IEEE Trans Contr Syst Technol, 13(2):301-312.

[26]Zhao ZJ, He XY, Ren ZG, et al., 2019. Boundary adaptive robust control of a flexible riser system with input nonlinearities. IEEE Trans Syst Man Cybern Syst, 49(10):1971-1980.

[27]Zhao ZJ, Lin SM, Zhu DC, et al., 2020. Vibration control of a riser-vessel system subject to input backlash and extraneous disturbances. IEEE Trans Circ Syst II, 67(3):516-520.