Search

In nowadays industry, most processes are controlled and automated. Interestingly, PID controllers are major contributors to the control process since they were invented and become quite practical. PID controllers are vital component in the industry and enhancing the component will show an echo effect in today’s technology. Their drawbacks are tuning them for an application, and this provides inspiration to develop advanced optimization methods in tuning PID controllers. This survey aims to review metaheuristic optimization methods of PID controller tuning that were published between 2010 and 2018. The paper was constructed based on 22 research papers and found that 8 metaheuristics optimization methods were used with PID tuning on 5 industrial applications. The papers also extensively provided answers to 3 research questions and assessing the quality of the papers based on 6 parameters.

In this research, four topologies have been investigated to characterize substrate/buffer loading effect on GaN HEMT on Si. This effect has been simulated using open de-embedded structure’s Z-parameter measurements for GaN HEMT on Si. An optimization technique based on Grey Wolf Optimization (GWO) with Cuckoo Search (CS) has been developed to extract four models’ elements. The simulated data fitting compared with the measured one has been considered the validation of the process. The models are evaluated in terms of rate of convergence to minimum error and accuracy of the results. The results showed good agreement with previous literature.

In this research, four different equivalent circuit models that characterize the substrate/buffer loading effect for GaN HEMT on Si substrate have been investigated. Z-parameter measurements of an open de-embedded structure have been used to characterize this effect in 16×200 μm GaN HEMT on Si. An optimization technique based on hybridization between two meta-heuristic techniques which are Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) has been developed to extract the models' elements. Each of those techniques has been tested separately to evaluate the performance of the proposed technique. The validation process of the extracted elements is done by comparing the simulated and the measured data of the model. The accuracy and the rate of convergence of the extraction results are considered as evaluation criteria. The proposed PSO-GWO technique shows a very good agreement with previous research results, which accordingly validates the applicability of this technique for small- and large-signal modeling of transistors.