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Method and system for providing an optimized control of a complex dynamical system

机译:用于提供复杂动态系统的优化控制的方法和系统

摘要

A method using machine learned, scenario based control heuristics including: providing a simulation model for predicting a system state vector of the dynamical system in time based on a current scenario parameter vector and a control vector; using a Model Predictive Control, MPC, algorithm to provide the control vector during a simulation of the dynamical system using the simulation model for different scenario parameter vectors and initial system state vectors; calculating a scenario parameter vector and initial system state vector a resulting optimal control value by the MPC algorithm; generating machine learned control heuristics approximating the relationship between the corresponding scenario parameter vector and the initial system state vector for the resulting optimal control value using a machine learning algorithm; and using the generated machine learned control heuristics to control the complex dynamical system modelled by the simulation model.
机译:一种使用机器学习的方法,基于场景的控制启发式:提供了一种基于当前场景参数向量和控制向量的时间仿真模型,用于预测动力系统的系统状态向量;使用模型预测控制,MPC,算法在使用模拟模型的不同方案参数向量和初始系统状态向量的仿真模型期间提供控制向量。计算场景参数向量和初始系统状态矢量通过MPC算法产生最佳控制值;生成机器学习控制启发式逼近相应场景参数矢量与初始系统状态矢量的关系,使用机器学习算法实现所得到的最佳控制值;并使用生成的机器学习控制启发式控制模型模型建模的复杂动态系统。

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