IEEE Trans Neural Netw Learn Syst. 2013 Feb;24(2):274-87. doi: 10.1109/TNNLS.2012.2228230.
This paper presents the design and analysis of an intelligent control system that inherits the robust properties of sliding-mode control (SMC) for an n-link robot manipulator, including actuator dynamics in order to achieve a high-precision position tracking with a firm robustness. First, the coupled higher order dynamic model of an n-link robot manipulator is briefy introduced. Then, a conventional SMC scheme is developed for the joint position tracking of robot manipulators. Moreover, a fuzzy-neural-network inherited SMC (FNNISMC) scheme is proposed to relax the requirement of detailed system information and deal with chattering control efforts in the SMC system. In the FNNISMC strategy, the FNN framework is designed to mimic the SMC law, and adaptive tuning algorithms for network parameters are derived in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance. Numerical simulations and experimental results of a two-link robot manipulator actuated by DC servo motors are provided to justify the claims of the proposed FNNISMC system, and the superiority of the proposed FNNISMC scheme is also evaluated by quantitative comparison with previous intelligent control schemes.
本文提出了一种智能控制系统的设计与分析，该系统继承了滑模控制（SMC）的鲁棒特性，用于 n 连杆机器人机械手，包括执行器动态，以实现高精度的位置跟踪和坚定的鲁棒性。首先，简要介绍了 n 连杆机器人机械手的耦合高阶动力学模型。然后，为机器人机械手的关节位置跟踪开发了一种常规的 SMC 方案。此外，提出了一种模糊神经网络继承 SMC（FNNISMC）方案，以放宽对详细系统信息的要求，并处理 SMC 系统中的抖动控制。在 FNNISMC 策略中，设计了 FNN 框架来模拟 SMC 规律，并在投影算法和 Lyapunov 稳定性定理的意义上推导出网络参数的自适应调整算法，以确保网络收敛以及稳定的控制性能。通过直流伺服电机驱动的两连杆机器人机械手的数值模拟和实验结果验证了所提出的 FNNISMC 系统的主张，并且通过与先前的智能控制方案的定量比较评估了所提出的 FNNISMC 方案的优越性。
