Azeez Muhammad I, Abdelhaleem A M M, Elnaggar S, Moustafa Kamal A F, Atia Khaled R
Mechanical Design and Production Engineering Department, Zagazig University, Zagazig, 44519, Egypt.
Industrial Engineering Department, Zagazig University, Zagazig, 44519, Egypt.
Sci Rep. 2023 Aug 2;13(1):12518. doi: 10.1038/s41598-023-38855-7.
The aim of this study is to enhance the performance of a nonlinear three-rigid-link maneuver (RLM) in terms of trajectory tracking, disturbance and noise cancellation, and adaptability to joint flexibility. To achieve this, an optimized sliding mode controller with a proportional integral derivative surface (SMC-PID) is employed for maneuver control. An improved artificial bee colony algorithm with multi-elite guidance (MGABC) is utilized to obtain optimal values for the sliding surface and switching mode gain and attain the best performance for the robot maneuver system. The selection of the MGABC algorithm is based on its efficient exploration and exploitation techniques. The performance of the optimized SMC-PID robotic system is compared against other optimization algorithms found in existing literature, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Artificial Bee Colony (ABC), Ant Lion Optimizer (ALO), and Grey Wolf Optimizer (GWO). The implemented controller effectively reduces the tracking error to 0.00691 radians, eliminates chattering phenomena in the control effort, and demonstrates robustness against disturbances and noise. The controller ensures that the objective function (OBJF) is minimized, with 0.954% increase in OBJF under low disturbance and noise conditions and 14.55% under severe disturbance and noise conditions. Moreover, the optimized controller exhibits resilience to variations in payload mass analysis, with the percentage increase in OBJF values ranging from 5.726% under low uncertainty conditions to 18.887% under severe uncertainty conditions. Flexible-link maneuvers (FLM) offer advantages such as improved safety and increased operating speeds in real-world applications. In this study, we investigated the impact of joint flexibility on the performance of the FLM system. Our proposed controller demonstrated superior tracking performance, characterized by minimal vibrations in the movement of the end effector.
本研究的目的是在轨迹跟踪、干扰和噪声消除以及对关节灵活性的适应性方面提高非线性三刚性连杆机动(RLM)的性能。为实现这一目标,采用了具有比例积分微分曲面的优化滑模控制器(SMC-PID)进行机动控制。利用一种改进的具有多精英引导的人工蜂群算法(MGABC)来获取滑模面和切换模式增益的最优值,并使机器人机动系统达到最佳性能。MGABC算法的选择基于其高效的探索和开发技术。将优化后的SMC-PID机器人系统的性能与现有文献中发现的其他优化算法进行了比较,包括粒子群优化(PSO)、遗传算法(GA)、人工蜂群(ABC)、蚁狮优化器(ALO)和灰狼优化器(GWO)。所实现的控制器有效地将跟踪误差降低到0.00691弧度,消除了控制作用中的抖振现象,并表现出对干扰和噪声的鲁棒性。该控制器确保目标函数(OBJF)最小化,在低干扰和噪声条件下OBJF增加0.954%,在严重干扰和噪声条件下增加14.55%。此外,优化后的控制器在有效载荷质量分析变化时具有弹性,OBJF值的增加百分比在低不确定性条件下为5.726%,在严重不确定性条件下为18.887%。在实际应用中,柔性连杆机动(FLM)具有提高安全性和提高运行速度等优点。在本研究中,我们研究了关节灵活性对FLM系统性能的影响。我们提出的控制器表现出卓越的跟踪性能,其特点是末端执行器运动中的振动最小。