文章基本信息

标题：Fast Gradient Method for Model Predictive Control with Input Rate and Amplitude Constraints ⁎
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作者：Idris Kempf ; Paul Goulart ; Stephen Duncan 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：6542-6547
DOI：10.1016/j.ifacol.2020.12.070
语种：English
出版社：Elsevier
摘要：AbstractThis paper is concerned with the computing efficiency of model predictive control (MPC) problems for dynamical systems with both rate and amplitude constraints on the inputs. Instead of augmenting the decision variables of the underlying finite-horizon optimal control problem to accommodate the input rate constraints, we propose to solve this problem using the fast gradient method, where the projection step is solved using Dykstra’s algorithm. We show that, relative to the Alternating Direction of Method Multipliers (ADMM), this approach greatly reduces the computation time while halving the memory usage. Our algorithm is implemented in C and its performance demonstrated using several examples.
关键词：KeywordsModel Predictive Control (MPC)Fast Gradient MethodDykstra’s MethodAlternating Direction of Multipliers Method (ADMM)ProjectionRate Constraints