Intelligent Transportation Systems Research Center, Wuhan University of Technology, 1178 Heping Avenue, Wuhan 430000, China.
Chongqing Research Institute, Wuhan University of Technology, 598 Liangjiang Avenue, Chongqing 400000, China.
Sensors (Basel). 2023 Feb 23;23(5):2501. doi: 10.3390/s23052501.
Mobile robots are widely employed in various fields to perform autonomous tasks. In dynamic scenarios, localization fluctuations are unavoidable and obvious. However, common controllers do not consider the impact of localization fluctuations, resulting in violent jittering or poor trajectory tracking of the mobile robot. For this reason, this paper proposes an adaptive model predictive control (MPC) with an accurate localization fluctuation assessment for mobile robots, which balances the contradiction between precision and calculation efficiency of mobile robot control. The distinctive features of the proposed MPC are three-fold: (1) Integrating variance and entropy-a localization fluctuation estimation relying on fuzzy logic rules is proposed to enhance the accuracy of the fluctuation assessment. (2) By using the Taylor expansion-based linearization method-a modified kinematics model that considers that the external disturbance of localization fluctuation is established to satisfy the iterative solution of the MPC method and reduce the computational burden. (3) An improved MPC with an adaptive adjustment of predictive step size according to localization fluctuation is proposed, which alleviates the disadvantage of a large amount of the MPC calculation and improves the stability of the control system in dynamic scenes. Finally, verification experiments of the real-life mobile robot are offered to verify the effectiveness of the presented MPC method. Additionally, compared with PID, the tracking distance and angle error of the proposed method decrease by 74.3% and 95.3%, respectively.
移动机器人广泛应用于各个领域执行自主任务。在动态场景中，定位波动是不可避免且明显的。然而，常见的控制器并未考虑定位波动的影响，导致移动机器人出现剧烈抖动或轨迹跟踪不良的情况。针对这一问题，本文提出了一种具有精确定位波动评估的自适应模型预测控制（MPC）方法，用于平衡移动机器人控制的精度和计算效率之间的矛盾。所提出的 MPC 具有三个特点：（1）集成方差和熵——提出了一种基于模糊逻辑规则的定位波动估计方法，以提高波动评估的准确性。（2）采用基于泰勒展开的线性化方法——建立了考虑定位波动外部干扰的修正运动学模型，以满足 MPC 方法的迭代求解，并降低计算负担。（3）提出了一种具有自适应预测步长调整的改进 MPC，根据定位波动进行调整，减轻了 MPC 大量计算的缺点，提高了控制系统在动态场景中的稳定性。最后，通过真实移动机器人的验证实验验证了所提出的 MPC 方法的有效性。与 PID 相比，该方法的跟踪距离和角度误差分别降低了 74.3%和 95.3%。
