非线性伺服系统处理噪声和变载荷的状态和参数估计

Meriç Çetin, S. Beyhan
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引用次数: 0

摘要

有效载荷估计器有许多参数,这些参数是使用记录的位置、速度和已知的有效载荷信息进行训练的。为了在实时应用中使用这些有效载荷估计器,需要精确的系统位置和速度信息。本文首先设计并比较了最近提出的滑模观测器,用于非线性伺服系统的速度估计。其次,设计了一种基于滑模超扭转的参数估计方法,用于估计一类非线性系统的未知和变参数。利用李雅普诺夫稳定性方法研究了观测器的收敛性。在实际应用中,利用所设计的方法对伺服系统的恒载荷和变载荷进行了估计,并与扩展卡尔曼滤波(EKF)进行了比较。最后,对测量信号施加不同信噪比的人工噪声。当噪声信号幅值较小时,二阶SMO比EKF能更好地估计状态和有效载荷。然而,对于大幅度的噪声信号,EKF的估计效果比二阶SMO要好得多。为了推广,二阶SMO在小噪声情况下是一种快速且鲁棒的观测器。此外,对于大噪声情况,EKF的滤波特性仍然具有重要意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
State and Parameter Estimation of a Nonlinear Servo System Handling Noises and Varying Payloads
Payload estimators have many parameters, which are trained using the recorded position, velocity and known payload information. To use these payload estimators in the real-time applications, accurate position and velocity information of the system are required. In this paper, first recently proposed sliding-mode observers (SMOs) are designed and compared for velocity estimation of a nonlinear servo system. Second, a parameter estimation based on sliding-mode super-twisting approach is designed to estimate unknown and varying parameter for a class of nonlinear systems. The convergence property of observers is considered using Lyapunov stability method. In the applications, the constant and varying payloads of the servo system have been estimated using the designed method and compared with Extended-Kalman Filter (EKF). In the final section, artificial noises with different SNR are applied to the measurement signal. When the less amplitude of noise signal is applied, second order SMO estimated the states and the payload better than EKF. However, EKF provides much better estimation results than second order SMO for large amplitude of noise signals. For the sake of generalization, second order SMO is a fast and robust observer for small noise cases. In addition, the filtering property of EKF has still importance for large noise cases.
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