Abstract:
This thesis considers the neural network learning control of a variable-geometry
automotive active suspension system which combines most of the benefits of active
suspension systems with low energy consumption.
Firstly, neural networks are applied to the control of various simplified automotive
active suspensions, in order to understand how a neural network controller can be
integrated with a physical dynamic system model. In each case considered, the
controlled system has a defined objective and the minimisation of a cost function. The
neural network is set up in a learning structure, such that it systematically improves the
system performance via repeated trials and modifications of parameters. The learning
efficiency is demonstrated by the given system performance in agreement with prior
results for both linear and non-linear systems. The above simulation results are
generated by MATLAB and the Neural Network Toolbox.
Secondly, a half-car model, having one axle and an actuator on each side, is developed
via the computer language, AUTOSIM. Each actuator varies the ratio of the
spring/damper unit length change to wheel displacement in order to control each wheel
rate. The neural network controller is joined with the half-car model and learns to
reduce the defined cost function containing a weighted sum of the squares of the body
height change, body roll and actuator displacements. The performances of the neurocontrolled
system are compared with those of passive and proportional-plusdifferential
controlled systems under various conditions. These involve various levels
of lateral force inputs and vehicle body weight changes.
Finally, energy consumption of the variable-geometry system, with either the neurocontrol
or proportional-plus-differential control, is analysed using an actuator model
via the computer simulation package, SIMULINK. The simulation results are
compared with those of other actively-controlled suspension systems taken from the
literature.