Sánchez G, Murillo M, Giovanini L
Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH-UNL/CONICET, Ciudad Universitaria UNL, 4° piso FICH, S3000 Santa Fe, Argentina.
ISA Trans. 2017 May;68:54-62. doi: 10.1016/j.isatra.2017.02.012. Epub 2017 Mar 1.
Moving horizon estimation is an efficient technique to estimate states and parameters of constrained dynamical systems. It relies on the solution of a finite horizon optimization problem to compute the estimates, providing a natural framework to handle bounds and constraints on estimates, noises and parameters. However, the approximation of the arrival cost and its updating mechanism are an active research topic. The arrival cost is very important because it provides a mean to incorporate information from previous measurements to the current estimates and it is difficult to estimate its true value. In this work, we exploit the features of adaptive estimation methods to update the parameters of the arrival cost. We show that, having a better approximation of the arrival cost, the size of the optimization problem can be significantly reduced guaranteeing the stability and convergence of the estimates. These properties are illustrated through simulation studies.
移动时域估计是一种用于估计受约束动态系统状态和参数的有效技术。它依靠求解有限时域优化问题来计算估计值，为处理估计值、噪声和参数的边界及约束提供了一个自然的框架。然而，到达代价的近似及其更新机制是一个活跃的研究课题。到达代价非常重要，因为它提供了一种将先前测量信息纳入当前估计值的手段，并且难以估计其真实值。在这项工作中，我们利用自适应估计方法的特点来更新到达代价的参数。我们表明，通过对到达代价有更好的近似，可以显著减小优化问题的规模，同时保证估计值的稳定性和收敛性。这些特性通过仿真研究得到了说明。
