基于航行阻力优化的近水面机器人减纵摇控制
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摘要
[目的]水下机器人(AUV)在近水面航行时,不可避免地会受到海浪的干扰,海浪干扰导致的纵摇和升沉运动不仅会影响AUV的航行姿态,同时也会导致其航行阻力增加,加剧能源的消耗。为实现AUV航行姿态和航行阻力的加权最优,[方法]建立AUV的六自由度模型并进行纵平面运动的线性化。对AUV的纵摇增阻情况进行研究,利用势流理论的方法,推导AUV的纵摇增阻模型。以纵摇增阻为性能指标,确定控制器中的Q矩阵和R矩阵,并设计减小AUV纵摇的线性二次型控制系统(LQR)控制器。[结果]仿真结果表明,加入LQR控制器后,减垂荡和减纵摇效果分别达到46.64%和77.62%,纵摇增阻减小到原来的1/6。[结论]研究结果显示,基于能量优化的LQR控制可实现纵摇增阻和航行姿态的加权最优,节约能量消耗,增加AUV的续航力。
When an Autonomous Underwater Vehicle(AUV)sails near the surface of the sea,it willinevitably be subjected to wave disturbance. The heave and pitch motion caused by wave disturbance notonly affects the navigation attitude of the AUV,but also leads to an increase in sailing resistance. As such,its energy consumption is increased. In this paper,the six degrees of freedom model of AUVs isestablished and linearized in order to achieve the weighted optimization of the sailing attitude and theresistance of the AUVs. The drag force model of the AUV is derived using the theory of potential flow. TheQmatrix andRmatrix are determined in the controller based on research into the drag force model. TheLinear Quadratic Regulator(LQR)controller of the AUV is designed using the drag force model as theperformance index. The simulation results show that after adding the LQR controller,the effects ofreducing heave motion and pitch motion are 46.64% and 77.62% respectively,and the increasedresistance caused by the pitch motion is reduced to 1/6 of its original value. The results show that themultiple optimum of attitude and sailing resistance is realized,the energy consumption is decreased andthe endurance of the AUV is increased.
When an Autonomous Underwater Vehicle(AUV)sails near the surface of the sea,it willinevitably be subjected to wave disturbance. The heave and pitch motion caused by wave disturbance notonly affects the navigation attitude of the AUV,but also leads to an increase in sailing resistance. As such,its energy consumption is increased. In this paper,the six degrees of freedom model of AUVs isestablished and linearized in order to achieve the weighted optimization of the sailing attitude and theresistance of the AUVs. The drag force model of the AUV is derived using the theory of potential flow. TheQmatrix andRmatrix are determined in the controller based on research into the drag force model. TheLinear Quadratic Regulator(LQR)controller of the AUV is designed using the drag force model as theperformance index. The simulation results show that after adding the LQR controller,the effects ofreducing heave motion and pitch motion are 46.64% and 77.62% respectively,and the increasedresistance caused by the pitch motion is reduced to 1/6 of its original value. The results show that themultiple optimum of attitude and sailing resistance is realized,the energy consumption is decreased andthe endurance of the AUV is increased.
引文
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[3]LIU Z Q,JIN H Z.Extended radiated energy method and its application to a ship roll stabilisation control system[J].Ocean Engineering,2013,72:25-30.
[4]EVANS J,NAHON M.Dynamics modeling and performance evaluation of an autonomous underwater vehicle[J].Ocean Engineering,2004,31(14/15):1835-1858.
[5]OSTAFICHUK P M.AUV hydrodynamic and modelling for improved control[D].Vancouver,Canada:University of British Columbia,2004.
[6]丁团结.基于LQR的纵向多自由度变稳控制律设计[J].飞行力学,2016,34(3):86-89.DING T J.Design of control law for longitudinal multi-degrees of freedom variable stability based on LQR[J].Flight Dynamics,2016,34(3):86-89(in Chinese).
[7]李一波,陈超,张晓林.改进LQR技术的飞翼式无人机控制算法研究[J].控制工程,2014,21(5):628-633.LI Y B,CHEN C,ZHANG X L.Research on control algorithm for flying wing UAV based on improved LQR technology[J].Control Engineering of China,2014,21(5):628-633(in Chinese).
[8]段镇.无人机鲁棒伺服LQR飞行控制律设计[J].计算机测量与控制,2015,23(8):2713-2715.DUAN Z.Robust servo LQR flight control law design of UAV[J].Computer Measurement&Control,2015,23(8):2713-2715(in Chinese).
[9]BHUSHAN R,CHATTERJEE K,SHANKAR R.Comparison between GA-based LQR and conventional LQR control method of DFIG wind energy system[C]//Proceedings of the 3rd International Conference on Recent Advances in Information Technology(RAIT).Dhanbad,India:RAIT,2016:214-219.
[10]H?USLER A J,SACCON A,AGUIAR A P,et al.Energy-optimal motion planning for multiple robotic vehicles with collision avoidance[J].IEEE Transactions on Control Systems Technology,2016,24(3):867-883.
[11]LIANG X,HUA X J,SU L F,et al.Energy conservation control strategy of autonomous underwater vehicle for ocean search[J].Journal of Coastal Research,2015,73:589-593.
[12]PETRICH J,STILWELL D J.Model simplification for AUV pitch-axis control design[J].Ocean Engineering,2010,37(7):638-651.
[13]WANG H J,WANG L L,PAN L X.Research on roll stabilizing based on energy optimization for autonomous surface vehicle[J].Journal of Applied Mathematics,2014,2014:347589.
[14]徐建安,任立国,杨立平,等.水下机器人广义预测控制算法及能耗问题研究[J].控制理论与应用,2009,26(10):1148-1150.XU J A,REN L G,YANG L P,et al.Generalized predictive control and energy consumption analysis of autonomous underwater vehicle[J].Control Theory&Applications,2009,26(10):1148-1150(in Chinese).
[15]CHYBA M,HABERKORN T,SINGH S B,et al.Increasing underwater vehicle autonomy by reducing energy consumption[J].Ocean Engineering,2009,36(1):62-73.
[16]SARKAR M,NANDY S,VADALI S R K,et al.Modelling and simulation of a robust energy efficient AUV controller[J].Mathematics and Computers in Simulation,2016,121:34-47.
[17]李佳,黄德波,邓锐.载人潜器阻力和有效功率的数值计算与试验[J].哈尔滨工程大学学报,2009,30(7):735-740.LI J,HUANG D B,DENG R.Numerical calculation and model tests for drag and power prediction of a manned submersible[J].Journal of Harbin Engineering University,2009,30(7):735-740(in Chinese).