Gauss pseudospectral‐based velocity optimization for rail transit trains with running and computation delays

Abstract

With the progress of urban intelligent construction, urban rail transit trains have become one of the most important ways for urban traffic. Optimization operation is a key to further improve the performance of urban rail transit. In this work, an energy saving velocity planning algorithm for rail transit train with running and computation delays is proposed. First, an optimization model with considering both running and computation delays is established for velocity planning. Then, the velocity planning algorithm frame is proposed based on Gauss pseudospectral distribution, where the running delay and computation delay are tackled separately. Meanwhile, the gradient information of transform nonlinear programming (NLP) problem is derived in detail so that NLP solver can be employed to obtain accuracy results. Furthermore, the implementation steps of the proposed algorithm are given. Finally, simulation tests are carried out on a two-station running rail transit train model with three different delay situations and two different slopes railway. Numerical results and comparison show the effectiveness and correctness of the proposed method for rail transit train velocity planning with delays.

Data‐based learning control for optimization of nonlinear systems

“…The seventh group of papers 31‐36 focuses on machine learning, data mining, and practical applications in automation. The performance of a Takagi–Sugeno fuzzy‐model‐based observer is enhanced by proposing a featured multi‐instant united switch‐type observer 31 .…”

“…The reinforcement learning theory with deep Q‐network is applied for the mobile robot to achieve a collision‐free path in an unknown dynamic environment 32 . An energy‐saving velocity planning algorithm is proposed for rail transit train with running and computation delays 33 . A novel COVID‐19 transmission model is established by introducing traditional susceptible–exposed–infected–removed disease transmission models into complex network 34 .…”

“…29 An algorithm called the kernel entropy regression is proposed to enhance the interpretability between the fault and the key performance indicator. 30 The seventh group of papers [31][32][33][34][35][36] focuses on machine learning, data mining, and practical applications in automation. The performance of a Takagi-Sugeno fuzzy-model-based observer is enhanced by proposing a featured multi-instant united switch-type observer.…”

“…32 An energy-saving velocity planning algorithm is proposed for rail transit train with running and computation delays. 33 A novel COVID-19 transmission model is established by introducing traditional susceptible-exposed-infected-removed disease transmission models into complex network. 34 A novel collaborative diagnosis method is presented by combining variational modal decomposition and stochastic configuration network for incipient faults of rolling bearing.…”