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＜会議発表資料＞
OPTIMIZATION FOR REAL-TIME CONTROL WITH LIMITED RESOURCES
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| 概要 | Model predictive control (MPC) has become a popular strategy to implement feedback control loops for a variety of systems. An MPC strategy aims at repeatedly selecting control inputs that yield the be...st outcome among all possible choices. To assess the quality of an input, a cost function is designed that takes into account the desired goals, such as going from point A to point B in short time without wasting fuel. This leads to a problem formulation where the objective is the "minimization" of a cost function, encoding the desired goal, subject to constraints, which instead account for actuators limitations (e.g. maximum speed or power) as well as environmental impediments such as physical obstacles or speed limits. Most systems in nature and science evolve according to "nonlinear" laws, and this leads to the major challenge of nonsmooth and nonconvex problems that need to be solved within sampling time, that is, before a new control input needs to be fed again to the system. In "embedded" applications such as autonomous driving, the resulting problems easily become of large scale and the sampling time can be as low as few milliseconds, thus imposing an imperative demand for algorithmic speed and efficiency. In this talk we show how the scalability properties of "proximal algorithms" can conveniently be employed to design certifiable, fast, and lightweight algorithms perfectly suited for embedded applications.続きを見る |
| 目次 | 1.Optimization: what and why Examples Optimization in control Challenges 2.Problem setting & toolbox Functions, variables, constraints Simplest formulation Speeding up (textbook attempts) 3.Novel speedup Fast directions Globalization 4.Embeddable ms-fast NMPC solvers Handling state constraints Experiments 5.Conclusions続きを見る |
詳細
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| 登録日 | 2023.06.16 |
| 更新日 | 2023.06.16 |