Traditional proportional–integral–derivative (PID) controllers are broadly employed in engineering. However, they perform poorly in complex, non-linear systems, and the single-neuron PID (SNPID) has been proposed as an alternative. Few studies have applied SNPID controllers to multi-input multi-output (MIMO) systems, and none has implemented this control technique experimentally on a 2-DOF helicopter—a system that is both complex and nonlinear. In this study, the SNPID settings were optimally computed using the Zebra Optimization Algorithm (ZOA). The proposed SNPID controller was compared with a conventional PID controller, with both controllers tuned by the ZOA to ensure a fair assessment. Real-time results indicate that the SNPID controller achieves greater stability and more accurate reference tracking than the PID controller, with an integral square error (ISE) of 4.7101 × 10⁻⁴ versus 5.7751 × 10⁻⁴ for the PID controller. Furthermore, when disturbances occur, the SNPID controller returns to the reference signal, whereas the PID controller cannot. These findings provide evidence that SNPID controllers outperform conventional PID controllers in MIMO systems.