Low-cost geared DC motors are widely used in mobile robotics and embedded mechatronic systems; however, their control remains challenging due to dead-zone nonlinearities, friction, low encoder resolution, motor asymmetry, and supply voltage variations. Biological organisms routinely perform motor control in the presence of similar uncertainties by relying on approximate reasoning and adaptive responses rather than precise mathematical models. This paper develops and experimentally validates a practical bio-inspired control architecture for low-cost geared DC motors operating under severe sensing and actuator limitations. The proposed controller combines fuzzy inference, dead-zone compensation, and a ramp-start mechanism to emulate the gradual and adaptive nature of biological motor responses. Instead of relying on an accurate plant model, control actions are generated through linguistic rules that mimic human-like decision-making based on speed error and error variation. The controller is implemented on an Arduino-based differential-drive robotic platform equipped with low-resolution optical encoders. Experimental results demonstrate that the proposed bio-inspired approach effectively mitigates startup stall, reduces oscillatory behavior caused by measurement quantization, and maintains stable speed regulation despite actuator variability and battery voltage fluctuations. The study shows that biologically inspired fuzzy control provides a practical and computationally efficient alternative to conventional PID methods for low-cost robotic systems characterized by significant uncertainty and nonlinear behavior.