Browsing by Author "Tijani, I.B"

Now showing 1 - 2 of 2
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    Control of an inverted pendulum using mode-based optimized lqr controller
    (IEEE, 2013) Tijani, I.B; Akmeliawati, R; Abdullateef, A. I
    This paper presents an evolutionary optimization based LQR controller design for an inverted pendulum system. The objective is to address the challenges of appropriate design parameters selection in LQR controller while providing optimal performance compromise between the system control objectives with respect to pendulum angle and position response. Hence, a Multiobjective differential evolution algorithm is proposed to design an LQR controller with optimal compromise between the conflicting control objectives. The performance of the MODEbased LQR is benchmarked with an existing controller from the system manufacturer (QANSER). The performance shows the effectiveness of the proposed design algorithm, and in addition provides an efficient solution to conventional trial and error design approach.
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    Modified Narx Network for Low Voltage Consumer Load Prediction
    (Zaria Journal of Electrical Engineering Technology,, 2019) Abdullateef, A. I; Salami, M.J.E; Tijani, I.B; Ajala, M.T
    This paper is a continuation of our previous work on Nonlinear autoregressive with eXogenous input (NARX) for load prediction. Application of NARX network in real-time might be difficult since the tapped delay was selected by trial and error, leading to nine hidden neurons which makes the network complex. This NARX network was modified based on Genetic Algorithm (GA) and Differential Evolution (DE) resulting in a new model coded NARX-DE-GA. GA and DE search for the number of hidden neurons and tapped delay automatically. The NARX-DE-GA was used to predict the consumer load using one month energy data with 8928 data points. The results show that NARX- DE -GA outperformed the NARX network. The training mean square error (MSE) value for NARX- DE -GA is 0.0253 while validation is 0.0612. These values are slightly higher when compared with NARX network in previous study which are 0.0225 and 0.0533 respectively. However, the network structure which is one input and output tapped delay, and one hidden neuron is simple and applicable in real time