<p>This paper presents a comparative analysis between two control algorithms, referred to as A-1 and A-2. The algorithms are intended to control a proposed semi-autonomous Neuronally Controlled Visual Assistive Device (NCVAD) capable of guiding visually challenged persons in conducting reach tasks. A-1 is a bio-inspired algorithm that imitates the procedures followed by the human brain to process information and generate actuation commands. Contrarily, algorithm A-2 adopts a numerical approach to achieve the above-said task. Both algorithms are run with identical input and output parameters, and the study identifies the faster algorithm between the two. The simulation results for the same are validated using a developed 2-DoF robotic arm setup. Further, to generalize the efficacy of the developed A-2 algorithm over the A-1 algorithm, simulation experiments are carried with a 3-DoF anthropomorphic arm for the NCVAD, proposed as a future scope of this study. The simulation results demonstrate that for a learning rate of 2e-03, A-2 algorithm can reach the targeted object coordinates about 1.8 times faster than A-1. Furthermore, A-2 with different learning rates for individual joint actuators is shown to have improved performance.</p>