Kang Erlong, Qiao Hong, Gao Jie, Yang Wenjing
The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; Beijing Key Laboratory of Research and Application for Robotic Intelligence of Hand-Eye-Brain Interaction, Beijing 100190, China.
The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai 200031, China.
ISA Trans. 2021 Mar;109:89-101. doi: 10.1016/j.isatra.2020.10.009. Epub 2020 Oct 8.
This paper proposes a neural network-based model predictive control (MPC) method for robotic manipulators with model uncertainty and input constraints. In the presented NN-based MPC structure, two groups of radial basis function neural networks (RBFNNs) are considered for online model estimation and effective optimization. The first group of RBFNNs is introduced as a predictive model for the robotic system with online learning strategies for handling the system uncertainty and improving the model estimation accuracy. The second one is developed for solving the optimization problem. By taking into account an actor-critic scheme with different weights and the same activation function, adaptive learning strategies are established for balancing between optimal tracking performance and predictive system stability. In addition, aiming at guaranteeing the input constraints, a nonquadratic cost function is adopted for the NN-based MPC. The ultimately uniformly boundedness (UUB) of all variables is verified through the Lyapunov approach. Simulation studies are conducted to explain the effectiveness of the proposed method.
本文针对具有模型不确定性和输入约束的机器人机械手，提出了一种基于神经网络的模型预测控制（MPC）方法。在所提出的基于神经网络的MPC结构中，考虑了两组径向基函数神经网络（RBFNN）用于在线模型估计和有效优化。第一组RBFNN被用作机器人系统的预测模型，并采用在线学习策略来处理系统不确定性并提高模型估计精度。第二组RBFNN则用于解决优化问题。通过考虑具有不同权重和相同激活函数的行为-评判方案，建立了自适应学习策略，以在最优跟踪性能和预测系统稳定性之间取得平衡。此外，为了保证输入约束，基于神经网络的MPC采用了非二次成本函数。通过李雅普诺夫方法验证了所有变量的最终一致有界性（UUB）。进行了仿真研究以说明所提方法的有效性。
