Department of Physics and Electrical Engineering, Universität Bremen, Bremen, Germany.
Department of Aeronautics and Astronautics Engineering, Institute of Space Technology, Islamabad, Pakistan.
PLoS One. 2023 Aug 9;18(8):e0285495. doi: 10.1371/journal.pone.0285495. eCollection 2023.
A comprehensive literature review of self-balancing robot (SBR) provides an insight to the strengths and limitations of the available control techniques for different applications. Most of the researchers have not included the payload and its variations in their investigations. To address this problem comprehensively, it was realized that a rigorous mathematical model of the SBR will help to design an effective control for the targeted system. A robust control for a two-wheeled SBR with unknown payload parameters is considered in these investigations. Although, its mechanical design has the advantage of additional maneuverability, however, the robot's stability is affected by changes in the rider's mass and height, which affect the robot's center of gravity (COG). Conventionally, variations in these parameters impact the performance of the controller that are designed with the assumption to operate under nominal values of the rider's mass and height. The proposed solution includes an extended Kalman filter (EKF) based sliding mode controller (SMC) with an extensive mathematical model describing the dynamics of the robot itself and the payload. The rider's mass and height are estimated using EKF and this information is used to improve the control of SBR. Significance of the proposed method is demonstrated by comparing simulation results with the conventional SMC under different scenarios as well as with other techniques in literature. The proposed method shows zero steady state error and no overshoot. Performance of the conventional SMC is improved with controller parameter estimation. Moreover, the stability issue in the reaching phase of the controller is also solved with the availability of parameter estimates. The proposed method is suitable for a wide range of indoor applications with no disturbance. This investigation provides a comprehensive comparison of available techniques to contextualize the proposed method within the scope of self-balancing robots for indoor applications.
对自平衡机器人 (SBR) 的全面文献综述使人们深入了解了针对不同应用的可用控制技术的优缺点。大多数研究人员在他们的研究中都没有将有效负载及其变化包含在内。为了全面解决这个问题，人们意识到 SBR 的严格数学模型将有助于为目标系统设计有效的控制。这些研究考虑了具有未知有效负载参数的两轮 SBR 的鲁棒控制。尽管其机械设计具有额外机动性的优势，但机器人的稳定性受到骑手质量和身高变化的影响，这会影响机器人的重心 (COG)。通常，这些参数的变化会影响根据骑手质量和身高的标称值设计的控制器的性能。所提出的解决方案包括基于扩展卡尔曼滤波器 (EKF) 的滑模控制器 (SMC)，以及一个广泛的数学模型，用于描述机器人本身和有效负载的动力学。骑手的质量和身高使用 EKF 进行估计，并且该信息用于改善 SBR 的控制。通过将不同场景下的模拟结果与传统 SMC 进行比较，以及与文献中的其他技术进行比较，证明了所提出方法的重要性。所提出的方法显示出零稳态误差和无过冲。通过控制器参数估计，可以改善传统 SMC 的性能。此外，通过可用性参数估计还解决了控制器到达阶段的稳定性问题。所提出的方法适用于无干扰的广泛室内应用。这项研究对可用技术进行了全面比较，将所提出的方法置于室内应用自平衡机器人的范围内。
