This study considers a new design methodology in the context of active vehicle suspensions control. The approach combines concepts from Stochastic Optimal Control with those of Learning Automate stochastic 'test-track' environment. For practical application, the overwhelming advantage of this approach is that no explicit modelling is required, and considerable time savings may be expected in system development. This simulation study considers the on-line learning of optimal control in a low-bandwidth active suspension system, where control feedback is confined to a body-mounted accelerometer at each corner of the vehicle. It is shown that learning can successfully take place under a range of conditions, including the case when there is substantial transducer noise. The performance of the resulting control system is shown to depend heavily on the nature of the learning environment.
Application of learning automata to controller design in slow-active automobile suspensions
Anwendung von Lernautomaten zum Regleraufbau bei langsamen aktiven Automobilfederungen
Vehicle System Dynamics ; 24 , 8 ; 597-616
1995
20 Seiten, 7 Bilder, 9 Tabellen, 9 Quellen
Article (Journal)
English
Application of learning automata to controller design on slow-active automobile suspensions
Automotive engineering | 1995
|Application of Learning Automata to Controller Design in Slow-Active Automobile Suspensions
Taylor & Francis Verlag | 1995
|Application of Learning Automata to Controller Design in Slow-Active Automobile Suspensions
Online Contents | 1995
|Automotive engineering | 1996
|Stochastic optimal control of active vehicle suspensions using learning automata
Tema Archive | 1993
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