Most of the advanced control systems use sensor-based feedback for robust control. Tilt angle estimation is key feedback for many robotics and mechatronics applications in order to stabilize a system. Tilt angle cannot be directly measured when the system in consideration is not attached to a stationary frame. it is usually estimated through indirect measurements in such systems. The precision of this estimation depends on the measurements; hence it can get expensive and complicated as the precision requirement increases. This research is aimed at developing a novel and economic method to estimate tilt angle with a relatively less sophisticated and complicated system, while maintaining precision in estimating tilt angle. The method is developed to explore a pendulum as an inertial measurement sensor and estimates tilt angle based on dynamics of pendulum and parameter estimation models. Further, algorithms are developed with varying order of complexity and accuracy to have customization for different applications. Furthermore, this study will validate the developed algorithms by experimental testing. This method focuses on developing algorithms to reduce the input measurement error in the Kalman filter.
翻译:大多数先进的控制系统都使用基于传感器的反馈进行稳健的控制。 斜角估计是许多机器人和机械学应用的关键反馈,以便稳定一个系统。 当考虑中的系统不附在固定框架之后, 倾角无法直接测量。 通常通过这些系统中的间接测量来估计。 这一估计的精确度取决于测量结果; 因此随着精确要求的增加, 其成本会变得昂贵和复杂。 这项研究的目的是开发一种新颖和经济的方法, 以相对不那么复杂和复杂的系统来估计倾斜角度, 同时在估计倾斜角度时保持精确性。 该方法旨在探索一个作为惯性测量传感器的支线, 并根据笔数和参数估计模型的动态来估计倾斜角度。 此外, 算法的制定过程复杂性和准确度各不相同, 以便按不同应用的定制。 此外, 这项研究将通过实验性测试来验证已开发的算法。 该方法侧重于开发算法, 以减少Kalman过滤器中的输入测量错误。