The demands imposed on a braking system of passenger cars, under wide range of operating conditions, are high and manifold. Improvement of automotive braking systems' performance, under different operating conditions, is complicated by the fact that braking process has stochastic nature. The automotive brake's performance primarily affected the braking system's performance because their performance results from the complex interrelated phenomena occurring in the contact of the friction pair. These complex braking phenomena are mostly affected by the physicochemical properties of friction materials ingredients, its manufacturing conditions, and brake's operation regimes.
Analytical models of brakes performance are difficult, even impossible to obtain due to complex and highly nonlinear phenomena involved during braking. That is why, in this paper all relevant influences on the disc brake operation of a passenger car have been integrated by means of artificial neural networks. The influences of the friction material's composition (18 ingredients), its manufacturing conditions (5 parameters), and brake's operation regimes (application pressure, initial speed, and temperature in the contact of friction pair) have been modelled versus changes of the brake factor C i.e. coefficient of the friction. The artificial neural network's model of the disc brake operation has been used for their intelligent controlling. Based on an optimal neural model of the disc brake operation, the neural network's controller of disc brake operation can be developed. The intelligent control of the brake's operation has been related to adjusting of application pressure of the disc brakes. Accordingly, the brake's performance would be adjusted on the level that corresponds to driver's demanded deceleration taking into consideration the history of brake's performance represented by the neural model.