Braking torque control using recurrent neural networks

Abstract

The basic problem in the operation of automotive brakes is the unpredictable nature of the tribological processes that occur at the contact of the friction pair. The stochastic nature of the tribological contact of the disc brake is affected differently by the complex interaction between the brake disc and the friction material under different conditions because of the influences of the applied pressure, the speed and the brake interface temperature. Owing to the highly dynamic non-linear change in the braking torque induced by the complex situation at the contact of the disc brake, the braking torque could not be modelled, predicted and controlled using classical mathematical methods. This is related, in particular, to the dynamic change in the braking torque in a braking cycle. Dynamic modelling and prediction of the braking torque is very important for further improvement in the performance of the brakes of motor vehicles through more precise control of their performance with respect to the driver demands and the change in the adhesion between the tyre and the road. Recurrent dynamic neural networks were employed in this paper for modelling, prediction and control of the dynamic change in the braking torque during a braking cycle. The dynamic functional relationship between the changes in the applied pressure, the sliding speed, the brake interface temperature and the braking torque of the disc brake was established. The dynamic model developed was used to predict and control the braking torque during a braking cycle under different disc brake operation conditions.

Regenerative Intelligent Brake Control for Electric Motorcycles

“…These systems have evolved from their origin, using increasingly sophisticated algorithms and complex control architectures. Fuzzy logic [5,6], sliding control [7][8][9], control by artificial neural networks [10,11] and nonlinear control [12,13] are examples of the most used control methods. These systems try to optimize the longitudinal and lateral force in the tire, obtaining the maximum available force in the wheel-road contact during braking and traction processes.…”

Neuro-genetic optimization of disc brake performance at elevated temperatures

“…Due to very complex and highly non-linear phenomena involved into the braking process, an analytical model of dynamic change of the brake performance versus its operation conditions changes are almost impossible to be obtained [11][12][13]. This significantly complicates the process of control of automotive brakes performance [14,15].…”

The potentional application of artificial intelligence in motor vehicles braking system performance

“…U [86,87] se posebno ističe korisnost NARX rekurentne neuronske mreže za upravljanje složenim dinamičkim sistemima. Ovaj tip dinamičke neuronske mreže je iskorišćen i u [88,89]. U [88] su istraživane mogućnosti razvoja metode za predviđanje uticaja radnih režima disk kočnice na pojavu tzv.…”

“…Korišćenjem dinamičkih neuronskih mreža, razvijen je dinamički model uticaja radnih uslova disk kočnice na pojavu kontaktnih fenomena i način promene momenta kočenja. U [89] je pokazano da je dinamičko modeliranje i predviđanje momenta kočenja veoma važno za dalje unapređenje performansi kočnica motornih vozila putem preciznijeg upravljanja njihovim performansama u zavisnosti od zahteva vozača i promene uslova u kontaktu pneumatika i puta. Rekurentne neuronske mreže su pokazale izuzetne sposobnosti u modeliranju, predviđanju i upravljanju dinamičkom promenom momenta kočenja u toku ciklusa kočenja u zavisnosti od različitih radnih režima kočnice.…”

“…Prema [82,83,85,[87][88][89], NARX mreža se široko koristi u mnogim oblastima, a posebno za modeliranje nelinearnih dinamičkih sistema. Još jedan razlog zbog koga je ona iskorišćena za razvoj dinamičkog modela procesa kočenja leži u činjenici da je moguće formirati tzv.…”