SUMMARYThe flexible motion of the inchworm makes the locomotion mechanism as the prominent one than other limbless animals. Recently, the application of engineering greatly assists the inchworm locomotion to be applicable in the robotic mechanism. Due to the outstanding robustness, sliding mode control (SMC) has been validated as a robust control strategy for diverse types of systems. Even though the SMC techniques have made numerous achievements in several fields, some systems cannot be comfortably accepted as the general SMC approaches. Accordingly, this paper develops the Grey Wolf-Second order sliding mode control (GW-SoSMC) to control the manipulator of the inchworm robot. The GW-SoSMC reduces the chattering phenomenon of SMC and improves the controlling ability of SoSMC by weightage function. Subsequently, it compares the performance of the proposed method with several conventional techniques like Grey Wolf-SMC (GW-SMC), FireFly-SoSMC (FF-SoSMC), Artificial Bee Colony-SoSMC (ABC-SoSMC), Group Searching-SoSMC (GS-SoSMC), and Genetic Algorithm-SoSMC (GA-SoSMC). It portrays the valuable comparative analysis by measuring the accomplished joint angles, error, and response of the controller. Thus the proposed method discovers the supervisory controller for the inchworm robot that is immensely better than conventional controllers mentioned earlier.

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基于灰狼优化的尺蠖机器人二阶滑模控制

摘要尺蠖的灵活运动使其运动机制比其他无肢动物更为突出。近来，工程的应用极大地辅助尺蠖运动在机器人机构中的应用。由于出色的鲁棒性，滑模控制 (SMC) 已被验证为适用于各种类型系统的鲁棒控制策略。尽管 SMC 技术在多个领域取得了许多成就，但某些系统不能作为一般 SMC 方法而被接受。据此，本文开发了灰狼-二阶滑模控制（GW-SoSMC）来控制尺蠖机器人的机械臂。GW-SoSMC通过权重函数减少了SMC的颤振现象，提高了SoSMC的控制能力。随后，它将所提出方法的性能与灰狼-SMC (GW-SMC)、萤火虫-SoSMC (FF-SoSMC)、人工蜂群-SoSMC (ABC-SoSMC)、群搜索-SoSMC (GS- SoSMC) 和遗传算法-SoSMC (GA-SoSMC)。它通过测量控制器完成的关节角度、误差和响应来描述有价值的比较分析。因此，所提出的方法发现尺蠖机器人的监督控制器比前面提到的传统控制器要好得多。错误和控制器的响应。因此，所提出的方法发现尺蠖机器人的监督控制器比前面提到的传统控制器要好得多。错误和控制器的响应。因此，所提出的方法发现尺蠖机器人的监督控制器比前面提到的传统控制器要好得多。