Li Zhijun, Yuan Wang, Chen Yao, Ke Fan, Chu Xiaoli, Chen C L Philip
IEEE Trans Neural Netw Learn Syst. 2018 Dec;29(12):6113-6122. doi: 10.1109/TNNLS.2018.2818127. Epub 2018 Apr 23.
In this paper, a neural-dynamic optimization-based nonlinear model predictive control (NMPC) is developed for the multiple nonholonomic mobile robots formation. First, a model-based monocular vision method is developed to obtain the location information of the leader. Then, a separation-bearing-orientation scheme (SBOS) control strategy is proposed. During the formation motion, the leader robot is controlled to track the desired trajectory and the desired leader-follower relationship can be maintained through the SBOS method. Finally, the model predictive control (MPC) is utilized to maintain the desired leader-follower relationship. To solve the MPC generated constrained quadratic programming problem, the neural-dynamic optimization approach is used to search for the global optimal solution. Compared to other existing formation control approaches, the proposed solution is that the NMPC scheme exploit prime-dual neural network for online optimization. Finally, by using several actual mobile robots, the effectiveness of the proposed approach has been verified through the experimental studies.
本文针对多个非完整移动机器人编队，开发了一种基于神经动力学优化的非线性模型预测控制（NMPC）方法。首先，开发了一种基于模型的单目视觉方法来获取领导者的位置信息。然后，提出了一种分离方位角方案（SBOS）控制策略。在编队运动过程中，通过SBOS方法控制领导者机器人跟踪期望轨迹，并维持期望的领导者-跟随者关系。最后，利用模型预测控制（MPC）来维持期望的领导者-跟随者关系。为解决MPC产生的约束二次规划问题，采用神经动力学优化方法搜索全局最优解。与其他现有编队控制方法相比，所提出的解决方案是NMPC方案利用原对偶神经网络进行在线优化。最后，通过使用多个实际移动机器人，通过实验研究验证了所提方法的有效性。
