Open AccessSub‐optimal switching in anti‐lock brake systems using approximate dynamic programming
Author(s) -
Sardarmehni Tohid,
Heydari Ali
Publication year - 2019
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2018.5428
Subject(s) - dynamic programming , control theory (sociology) , computer science , optimal control , brake , artificial neural network , perceptron , lock (firearm) , vehicle dynamics , hamilton–jacobi–bellman equation , scheduling (production processes) , mathematical optimization , mathematics , control (management) , algorithm , engineering , automotive engineering , mechanical engineering , artificial intelligence , machine learning
Optimal scheduling in an anti‐lock brake system of ground vehicles is performed through approximate dynamic programming for reducing the stopping distance in severe braking. The proposed optimal scheduler explicitly incorporates the hybrid nature of the anti‐lock brake system and provides a feedback solution with a negligible computational burden in control calculation. To this goal, an iterative scheme, called the value iteration algorithm, is used to derive the infinite horizon solution to the underlying Hamilton–Jacobi–Bellman equation. Performance of the proposed method in control of the brake system is illustrated using both linear‐in‐parameter neural networks and multi‐layer perceptrons. Simulation results demonstrate potentials of the method.