New hybrid SPEA/R-deep learning to predict optimization parameters of cascade FOPID controller according engine speed in powertrain mount system control of half-car dynamic model

摘要

In this article, a new methodology, hybrid genetic algorithm GA, algorithm SPEA/R with Deep Neural Network (HDNN&SPEA/R). This combination gave computing time much faster than computing time when using genetic algorithms SPEA/R. On the other hand, this combination also significantly reduces the number of samples needed for the training of deep artificial neural networks. This is the task of finding out an optimal set that changes with the engine velocity of multi-objective optimization involving 12 simultaneous optimization goals: proportional P, integral I, derivative D, additional integration n and differentiation orders m factor, displacement amplification coefficient K-Dloop, acceleration amplification coefficient K-Aloop in two controllers acceleration and displacement to enhance the ride comfort. This article has provided a control algorithm of a Cascade FOPID controller to control the acceleration and displacement of the mount. Besides, the article also offers solutions to optimize the 12 simultaneous parameters of the two controllers by the new hybrid method HDNN&SPEA/R and suitable for the speed of rotation of the engine. To increase the safety factor in operation, we use magnetorheological dampers (MR) in a powertrain mounting system and a continuous state damper controller that calculates the input voltage to the damper coil. The results of this control method are compared with traditional PID systems, optimal PID parameter adjustment using genetic algorithms (GA) and passive drive system mounts. The results are tested in both time and frequency domains, to verify the success of the proposed Cascade FOPID algorithm. The results show that the proposed Cascade FOPID controller of the MR engine mounting system gives very good results in comfort and softness when riding compared to other controllers. This proposal has reduced 335 hours for optimal computation time and reduce vibration a lot.