E. Alizadeh Haghighi, S. Jafarmadar, H. Taghavifar,
Volume 3, Issue 4 (12-2013)
Abstract
Artificial neural network was considered in previous studies for prediction of engine performance and
emissions. ICA methodology was inspired in order to optimize the weights of multilayer perceptron (MLP)
of artificial neural network so that closer estimation of output results can be achieved. Current paper aimed
at prediction of engine power, soot, NOx, CO2, O2, and temperature with the aid of feed forward ANN
optimized by imperialist competitive algorithm. Excess air percent, engine revolution, torque, and fuel
mass were taken into account as elements of input layer in initial neural network. According to obtained
results, the ANN-ICA hybrid approach was well-disposed in prediction of results. NOx revealed the best
prediction performance with the least amount of MSE and the highest correlation coefficient(R) of 0.9902.
Experiments were carried out at 13 mode for four cases, each comprised of amount of plastic waste (0, 2.5,
5, 7.5g) dissolved in base fuel as 95% diesel and 5% biodiesel. ANN-ICA method has proved to be selfsufficient,
reliable and accurate medium of engine characteristics prediction optimization in terms of both
engine efficiency and emission.
P. Bayat, H. Mojallali, A. Baghramian, P. Bayat,
Volume 6, Issue 2 (6-2016)
Abstract
In this paper, a two-surfaces sliding mode controller (TSSMC) is proposed for the voltage tracking control of a two input DC-DC converter in application of electric vehicles (EVs). The imperialist competitive algorithm (ICA) is used for tuning TSSMC parameters. The proposed controller significantly improves the transient response and disturbance rejection of the two input converters while preserving the closed-loop stability. The combination of the proposed controller and ICA, realizes a fast transient response over a wide transient load changes and input voltage disturbances. For modeling the equations governing the system, state-space average modeling technique is used. In order to analyzing the results, the two input converter equipped with the proposed controller, was modeled in MATLAB/SIMULINK environment. Simulation results are reported to validate the theoretical predictions and to confirm the superior performance of the proposed nonlinear controller when it is compared with a conventional pure SMC.
