Optimal parameter tuning for PID controller using accelerated grey Wolf optimisation in smart sensor environments

Abstract

System lifetime is the crucial problem of wireless sensor networks (WSNs), and exploiting environmental energy provides a potential solution. Boost convertor can be employed in WSN to achieve energy efficiency. In order to achieve better performance, PID controller is combined with boost convertor. However, tuning the PID controller is crucial task whenever the input voltage fluctuates. In this work, a novel algorithm namely accelerated grey wolf optimisation (AGWO) is proposed to improve the convergence speed and to eradicate the local optima stagnation. AGWO algorithm utilises a balanced intensification and diversification techniques to eradicate the local optima struck. The observed results conveys that AGWO achieves minimum percentage overshoot (9%), settling time (0.894), rise time (0.50) and peak time (0.57) which is better compared to other comparative algorithms. Additionally, it has been observed that AGWO is able to achieve comparatively better success performance in a complex environment.