Application of learning automata to controller design in slow-active automobile suspensions

Marsh, C., Gordon, T. J. and Wu, Q. H. (1995) Application of learning automata to controller design in slow-active automobile suspensions. Vehicle System Dynamics, 24 (8). pp. 597-616. ISSN 0042-3114

Full text not available from this repository.

Item Type:Article
Item Status:Live Archive


This study considers a new design methodology in the context of active vehicle suspension control. The approach combines concepts from Stochastic Optimal Control with those of Learning Automata. A learning automaton effectively learns optimal control on-line in the vehicle, in an appropriate 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.

Keywords:Accelerometers, Acoustic noise, Automata theory, Computer simulation, Control system synthesis, Feedback, Learning systems, Online systems, Optimal control systems, Performance, Stochastic control systems, Transducers, Learning automata, Slow active automobile suspensions, Automobile suspensions
Subjects:H Engineering > H660 Control Systems
H Engineering > H330 Automotive Engineering
Divisions:College of Science > School of Engineering
Related URLs:
ID Code:11692
Deposited On:01 Oct 2013 17:57

Repository Staff Only: item control page