Guo Keyou, Zhang Haoze, Wei Caili, Jiang Haibing, Wang Jiangnan
School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing, 100048, China.
Sci Rep. 2024 Dec 28;14(1):30656. doi: 10.1038/s41598-024-77125-y.
To achieve high-performance trajectory tracking for a manipulator, this study proposes a novel sliding mode control strategy incorporating a nonlinear disturbance observer. The observer is designed to estimate unknown models in real-time, enabling feedforward compensation for various uncertainties such as modeling errors, joint friction, and external torque disturbances. The control law is formulated by integrating the Backstepping method, Lyapunov theory, and global fast terminal sliding mode theory, ensuring global convergence to zero within finite time and enhancing system robustness. To address the inherent chattering issue in sliding mode control, a hybrid reaching law is developed by combining the exponential and power reaching laws. Additionally, the improved-fal (Imp-fal) function replaces the sign function in the switching control law, improving system response speed, preventing overshoot, and optimizing gain beyond the threshold value. Through simulation and comparative experiments conducted using MATLAB/Simulink, the controller model exhibited a 16.4% average reduction in the mean square value of tracking errors compared to existing control strategies, with improvements observed in various performance indicators. When applied to a self-developed three-degree-of-freedom manipulator experimental platform, the controller demonstrated a roughly 55% increase in tracking accuracy and a decrease in response time by approximately 45% compared to existing strategies. The experimental results validate the effectiveness, superiority, and practicality of the control strategy, providing a feasible solution for high-performance trajectory tracking in robotic arm systems.
为实现机械手的高性能轨迹跟踪，本研究提出了一种结合非线性干扰观测器的新型滑模控制策略。该观测器旨在实时估计未知模型，从而对各种不确定性进行前馈补偿，如建模误差、关节摩擦和外部扭矩干扰。控制律通过结合反步法、李雅普诺夫理论和全局快速终端滑模理论来制定，确保在有限时间内全局收敛到零并增强系统鲁棒性。为解决滑模控制中固有的抖振问题，通过结合指数趋近律和幂次趋近律开发了一种混合趋近律。此外，改进的fal（Imp-fal）函数取代了切换控制律中的符号函数，提高了系统响应速度，防止了超调，并优化了阈值之外的增益。通过使用MATLAB/Simulink进行的仿真和对比实验，与现有控制策略相比，控制器模型的跟踪误差均方值平均降低了16.4%，各项性能指标均有改善。当应用于自行开发的三自由度机械手实验平台时，与现有策略相比，该控制器的跟踪精度提高了约55%，响应时间减少了约45%。实验结果验证了该控制策略的有效性、优越性和实用性，为机器人手臂系统的高性能轨迹跟踪提供了一种可行的解决方案。
