摘要
由一位新闻记者兼机器人与机器学习的新闻编辑每日新闻-机器人的新研究是一份新报告的主旨。根据NewsRx编辑对Taif大学的新闻报道，研究表明，"模型预测控制(MPC)已经成为控制系统领域的一种首选方法；然而，它面临着明显的挑战。"新闻记者从塔伊夫大学的研究中获得了一句话：“首先，MPC通常取决于是否有一个精确和准确的系统模式L，即使是微小的偏差也会严重影响控制性能。”摘要:本文介绍了一种利用高斯过程(GPs)的概率性质的新方法,提供了一种鲁棒、自适应的方法。我们的方法从收集数据学习最优控制策略开始，然后对这些数据进行GPs离线训练，使这些过程能够准确地掌握系统动力学，建立输入输出关系，关键是识别不确定性，从而为MP C框架提供信息。利用GPs得出的平均值和不确定性估计，我们已经精心设计了一个控制器，能够适应系统偏差，并保持一致的性能，即使面对不可预见的干扰或模型不准确。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on robotics is the subjec t of a new report. According to news reporting out of Taif University by NewsRx editors, research stated, “Model predictive control (MPC) has emerged as a predo minant method in the realm of control systems; yet, it faces distinct challenges .” The news journalists obtained a quote from the research from Taif University: “F irst, MPC often hinges on the availability of a precise and accurate system mode l, where even minor deviations can drastically affect the control performance. S econd, it entails a high computational load due to the need to solve complex opt imization problems in real time. This study introduces an innovative method that harnesses the probabilistic nature of Gaussian processes (GPs), offering a solu tion that is robust, adaptive, and computationally efficient for optimal control . Our methodology commences with the collection of data to learn optimal control policies. We then proceed with offline training of GPs on these data, which ena bles these processes to accurately grasp system dynamics, establish input-output relationships, and, crucially, identify uncertainties, thereby informing the MP C framework. Utilizing the mean and uncertainty estimates derived from GPs, we h ave crafted a controller that is capable of adapting to system deviations and ma intaining consistent performance, even in the face of unforeseen disturbances or model inaccuracies.”
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