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.

本文提出了一种基于神经网络的模型预测控制（MPC）方法，用于具有模型不确定性和输入约束的机器人机械手。在提出的基于 NN 的 MPC 结构中，考虑了两组径向基函数神经网络 (RBFNN) 进行在线模型估计和有效优化。引入第一组 RBFNN 作为机器人系统的预测模型，具有在线学习策略，用于处理系统不确定性并提高模型估计精度。第二个是为了解决优化问题而开发的。通过考虑具有不同权重和相同激活函数的actor-critic方案，建立自适应学习策略以在最佳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NN的MPC采用非二次成本函数。所有变量的最终一致有界 (UUB) 通过李雅普诺夫方法进行验证。进行仿真研究以解释所提出方法的有效性。