College of Intelligent Systems Science and Engineering, Harbin Engineering University, Nantong street, Harbin 150001, China.
School of Electrical, Electronic, and Computer Engineering, The University of Western Australia, Crawley, WA 6009, Australia.
Math Biosci Eng. 2023 Apr 10;20(6):10495-10513. doi: 10.3934/mbe.2023463.
For constrained image-based visual servoing (IBVS) of robot manipulators, a model predictive control (MPC) strategy tuned by reinforcement learning (RL) is proposed in this study. First, model predictive control is used to transform the image-based visual servo task into a nonlinear optimization problem while taking system constraints into consideration. In the design of the model predictive controller, a depth-independent visual servo model is presented as the predictive model. Next, a suitable model predictive control objective function weight matrix is trained and obtained by a deep-deterministic-policy-gradient-based (DDPG) RL algorithm. Then, the proposed controller gives the sequential joint signals, so that the robot manipulator can respond to the desired state quickly. Finally, appropriate comparative simulation experiments are developed to illustrate the efficacy and stability of the suggested strategy.
针对机器人的基于图像的视觉伺服（IBVS）的约束问题，本研究提出了一种通过强化学习（RL）调整的模型预测控制（MPC）策略。首先，模型预测控制用于将基于图像的视觉伺服任务转换为非线性优化问题，同时考虑系统约束。在模型预测控制器的设计中，提出了一种深度独立的视觉伺服模型作为预测模型。接下来，通过基于深度确定性策略梯度（DDPG）的 RL 算法，训练和获得合适的模型预测控制目标函数权重矩阵。然后，所提出的控制器给出了连续的关节信号，以便机器人能够快速响应期望状态。最后，进行了适当的对比仿真实验，以说明所提出策略的有效性和稳定性。
