Akademik Veri Yönetim Sistemi
ANKARA INTERNATIONAL CONGRESS ON SCIENTIFIC RESEARCH-VII, Ankara, Türkiye, 2 - 04 Aralık 2022, ss.853-858
The automotive companies have been using the Eddy Current Dynamometers (ECD) for the
auxiliary braking system, also for engine testing and various internal combustion engine
analyzing operations such as biodiesel, combustion timing and harmful gas emission. Eddy
current dynamometer brings versatile advantages such as contactless braking effect and zero
carbon footprint. However, Eddy Current Dynamometer braking performance varies
unexpectedly divergent rotor speeds and temperatures resulting a nonlinear braking torque.
PID control method which is known as the most popular conventional control method in
industry is inadequate robust for such nonlinear systems. On the other hand, Machine learning
methods are used as artificial intelligence-based techniques that can increase the performance
of conventional control systems in particular by means of improving robustness of the
designed control systems. Therefore, the proposed control system proves changeable Kp
(Proportional gain), Ki (Integral gain) and Kd (Derivative gain) parameters in real-time to
adapt and presents a good capacity to adapt nonlinearities and bring robustness using soft
computing methods. The testing and training dataset is extracted from experimental studies
and its robustness has also been verified with R2
and MAPE. The presented technique is
observed to have a good performance in terms of response time (t) and accuracy of desired
speed value (V) under different parameters such as non-linear dynamics (V, T) of the system
elements and the varying load effects.