“…From reported work in [10], the adaptive safe experimentation dynamics (ASED) have shown to be the most effective trajectory-based optimization than other trajectorybased optimization tools due to less number of coefficients, memory-based framework (able to keep the best design parameter during the tuning process) and simple algorithm. On the other hand, there are many types of advanced PID have been proposed by many control researchers, such as PD-PID [11], fuzzy-PID [12,13], PD based fuzzy sliding mode control [14], fractional-PID [15], neural-network-PID (NNPID) [16,17], fractional-fuzzy-PID [18], kinematic model predictive control PID (K-MPC-PID) [19], integral separation PID (IPID) [20], variable structure PID [21] and also sigmoid-PID (SPID) [22,23] controllers. Based on the reported advanced PIDs, the SPID is reported to be effective since the proposed method uses variable PID coefficients depending on error signal behaviour according to the sigmoid function.…”