Optimization of Steering Control Parameters of Robot Fish in Variable Flow Field Based on PSO
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摘要: 机器鱼在航行中容易受到非静止流场的干扰而偏离目标航向。文中采用航向角反馈来解决未搭载流场感知相关传感器的机器鱼航向偏离问题。首先, 建立机器鱼的关节动力学模型与水动力学模型, 得到关节角运动与关节力矩之间的关系, 并且获得机器鱼推进力和转向力矩与机器鱼摆动姿态之间的关系。而后, 将中枢模式发生器控制器运用于机器鱼的控制, 与受控对象构成闭环控制系统, 使机器鱼在控制器的调节作用下保持航向稳定。最后, 为获取最佳控制器参数, 采用粒子群优化算法, 以机器鱼从一个设定的航向角偏离量收敛到零的时间长度作为优化指标, 最终实现快速转向。根据建立的机器鱼动力学模型进行了仿真分析, 验证了文中设计方法的有效性和合理性。Abstract: Robotic fish are susceptible to interference from non-stationary flow fields and thus may deviate from the target course during navigation. In this study, course angle feedback is used to solve the problem of course deviation in a robotic fish without flow field sensors. First, a relationship between joint angular motion and joint torque is obtained by establishing the joint dynamics model of a robotic fish. In addition, a relationship between the propulsion and steering torque and swing posture is obtained. Subsequently, to maintain the stability of the robotic fish, a central pattern generator controller is used to adjust the closed-loop control system. Furthermore, this study takes the length of time during which the robot fish converges from a set course angle deviation to zero as an optimization index, and uses the particle swarm optimization algorithm to obtain the best controller parameters that can achieve rapid steering. The simulation analysis is performed based on the established dynamics model of a robotic fish, and the results verify the effectiveness and rationality of the proposed design method.
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表 1 机器鱼参数列表
Table 1. List of robotic fish parameters
名称 参数值 质量(m1~m4)/kg 3.4 1.5 0.4 0.05 长度(l1~l4)/m 0.4 0.2 0.2 0.2 转动惯量(J1
~J2)/(kg·m2)1.556 0.297 0.727 0.281 侧面积(S1~S4)/m2 0.096 0.050 0.026 0.030 表 2 控制器参数初始值列表
Table 2. List of initial values for controller parameters
名称 参数值 偏置系数k1 1.0 偏置系数k2 −1.5 偏置系数k3 1.0 表 3 优化后控制器参数列表
Table 3. Controller parameter list after optimization
名称 参数值 偏置系数k1 1.47 偏置系数k2 −1.83 偏置系数k3 1.32 -
[1] 王耀威, 纪志坚, 翟海川. 仿生机器鱼运动控制方法综述[J]. 智能系统学报, 2014(3): 276-284. doi: 10.3969/j.issn.1673-4785.2014.03.002Wang Yao-wei, Ji Zhi-jian, Zhai Hai-chuan. A Survey on Motion Control of the Biomimetic Robotic Fish[J]. CAAI Transactions on Intelligent Systems, 2014(3): 276-284. doi: 10.3969/j.issn.1673-4785.2014.03.002 [2] Barbera G, Pi L, Deng X. Attitude Control for a Pectoral Fin Actuated Bio-inspired Robotic Fish[C]//IEEE International Conference on Robotics & Automation. Shanghai, China: IEEE, 2011: 526-531. [3] Yuh J. Design and Control of Autonomous Underwater Robots: A Survey[J]. Autonomous Robots, 2000, 8(1): 7-24. doi: 10.1023/A:1008984701078 [4] Wang Rui, Wang Shuo, Wang Yu, et al. Development and Motion Control of Biomimetic Underwater Robots: A Survey[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2020(99): 1-12. [5] Yu J, Su Z, Wang M, et al. Control of Yaw and Pitch Maneuvers of a Multilink Dolphin Robot[J]. IEEE Transactions on Robotics: A publication of the IEEE Robotics and Automation Society, 2012, 28(2): 318-329. [6] Su Z, Yu J, Tan M, et al. Implementing Flexible and Fast Turning Maneuvers of a Multijoint Robotic Fish[J]. IEEE-ASME Transactions on Mechatroniocs, 2014, 19(1): 329-338. doi: 10.1109/TMECH.2012.2235853 [7] Gong Z, Cai Y, Bi S, et al. Roll Maneuver Control of Robotic Fish Propelled by Oscillating Pectoral Fins[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 30(11): 2184-2190. [8] Cao Z, Shen F, Zhou C, et al. Heading Control for a Robotic Dolphin Based on a Self-tuning Fuzzy Strategy[J]. International Journal of Advanced Robotic Systems, 2016, 13(1): 349-358. [9] Yuan J, Wu Z, Yu J, et al. Sliding Mode Observer Based Heading Control for a Gliding Robotic Dolphin[J]. IEEE Transactions on Industrial Electronics, 2017, 64(8): 6815-6824. doi: 10.1109/TIE.2017.2674606 [10] 吴正兴, 喻俊志, 苏宗帅, 等. 仿生机器鱼S形起动的控制与实现[J]. 自动化学报, 2013, 39(11): 1914-1922. doi: 10.3724/SP.J.1004.2013.01914Wu Zheng-xing, Yu Jun-zhi, Su Zong-shuai, et al. Control and Implementation of S-start for a Multijoint Biomimetic Robotic Fish[J]. Acta Automatica Sinica, 2013, 39(11): 1914-1922. doi: 10.3724/SP.J.1004.2013.01914 [11] 刘英想, 刘军考, 陈维山. 两关节机器鱼无升潜游动动力学建模与仿真[J]. 机械工程师, 2007(5): 19-22. doi: 10.3969/j.issn.1002-2333.2007.05.011Liu Ying-xiang, Liu Jun-kao, Chen Wei-shan. The Dynamic Mode Building and Simulation of Two-joint Fish Robot in No Up and Down Movement[J]. Mechanical Engineer, 2007(5): 19-22. doi: 10.3969/j.issn.1002-2333.2007.05.011 [12] 吴正兴, 喻俊志, 谭民. 两类仿鲹科机器鱼倒游运动控制方法的对比研究[J]. 自动化学报, 2013, 39(12): 2032-2042.Wu Zheng-xing, Yu Jun-zhi, Tan Min. Comparison of Two Methods to Implement Backward Swimming for a Carangiform Robotic Fish[J]. Acta Automatica Sinica, 2013, 39(12): 2032-2042. [13] 童秉纲, 庄礼贤. 鱼类波状摆动推进的流体力学研究[J]. 力学与实践, 1991, 13(3): 17-26. [14] Yu J, Tan M, Chen J, et al. A Survey on CPG-Inspired Control Models and System Implementation[J]. IEEE Transactions on Neural Networks & Learning Systems, 2017, 25(3): 441-456. [15] 董翔, 王硕, 曹志强, 等. 基于CPG模型的推进器运动控制方法[J]. 华中科技大学学报: 自然科学版, 2008(S1): 1-3.Dong Xiang, Wang Shuo, Cao Zhi-qiang, et al. CPG Model Based Thruster Motion Control Method[J]. Journal of Huazhong University of Science and Technology, 2008(S1): 1-3. [16] 王龙, 谭民, 曹志强, 等. 基于CPG模型的仿生机器鱼运动控制[J]. 控制理论与应用, 2007, 24(5): 749-755. doi: 10.3969/j.issn.1000-8152.2007.05.011Wang Long, Tan Min, Cao Zhi-qiang, et al. CPG Based Motion Control of Biomimetic Robotic Fish[J]. Control Theory & Applications, 2007, 24(5): 749-755. doi: 10.3969/j.issn.1000-8152.2007.05.011 [17] 和岩辉, 胡桥, 王朝晖, 等. 基于CPG和模糊控制的机器鱼定向游动精确控制方法[J]. 水下无人系统学报, 2021, 29(1): 39-47.He Yan-hui, Hu Qiao, Wang Chao-hui, et al. Precise Control Method for Directional Swimming of a Robotic Fish Based on CPG and Fuzzy Control[J]. Journal of Unmanned Undersea Systems, 2021, 29(1): 39-47. [18] Yu J, Wu Z, Wang M, et al. CPG Network Optimization for a Biomimetic Robotic Fish via PSO[J]. IEEE Transactions on Neural Networks and Learning Systems, 2016, 27(9): 1962-1968. doi: 10.1109/TNNLS.2015.2459913 [19] 杨越麒, 王健, 吴正兴, 等. 面向CPG驱动的仿生机器鱼容错控制方法[J]. Engineering, 2018, 4(6): 861-868.Yang Yue-qi ,Wang Jian,Wu Zheng-xing , et al. Fault-Tolerant Control of a CPG-Governed Robotic Fish[J]. Engineering, 2018, 4(6): 861-868. [20] Kennedy J, Eberhart R. Particle Swarm Optimaization[C]// Proceedings of the 4th IEEE International Conference on Neural Networks. Piscataway: IEEE Sevice Center, 1995: 1942-1948.