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Könyv Vehicle Dynamics and Intelligent Design of Automotive Mechatronic Systems Aydin Azizi

Vehicle Dynamics and Intelligent Design of Automotive Mechatronic Systems

Szerző: Aydin Azizi
Nyelv: Angol
Kötés: Kemény kötésű
Kiadó: Springer, Berlin
Elérhetőség: Könyvújdonság
Küldés 12. 04. 2027
72 362 Ft
This book brings together five contributions that examine that shift, from the design and control of...

Információk a könyvről

Szerző
Aydin Azizi
Nyelv
Angol
Kötés
Könyv - Kemény kötésű
Kiadva
2027
EAN
9789819285624
Enbook ID
54320673
Kiadó
Springer, Berlin
Méretek
155 x 235

Teljes leírás

This book brings together five contributions that examine that shift, from the design and control of the suspension itself through to full-vehicle lap-time simulation and systematic setup optimisation. A modern competition vehicle is a mechatronic system in which the mechanical, electronic and control subsystems can no longer be designed separately. Suspension geometry, damper characteristics, tyre behaviour and control strategy interact continuously, and the tools used to develop them have moved from the test track towards simulation, telemetry and optimisation.

Two chapters address suspension control directly. The first develops and compares classical and artificial intelligence control strategies for active suspension and delivers them through a browser-based toolbox, so that the comparison can be reproduced and extended by the reader rather than merely reported. The second designs an active suspension controller for a racing vehicle, using a linear quadratic regulator tuned by the Artificial Bee Colony algorithm and assessing performance over stepped and sausage kerbs alongside ISO 8608 road profiles.

The remaining chapters work from measured data. One inverts a full-vehicle seven-degree-of-freedom model to recover per-wheel road inputs from damper telemetry alone, then searches the complete damper click space to deliver validated lap-time gains, with a deliberately incorrect damper configuration included as a negative control. Another develops a lap-time simulation model correlated against professional reference data and extends it with fuzzy inference controllers for tyre and fuel management, stress-tested under Monte Carlo sampling. The last builds a structured, repeatable Rally2 setup optimisation workflow spanning tyre pressure, dampers, geometry, differential preload, springs and ride height.