This paper presents the design, implementation, and analysis of a novel adaptive control law that can be deployed in resource constrained environments. The example of a single-rotor helicopter is used to generalize the family of non-minimum phase systems commonly encountered in manufacturing, surveillance, and robotics. Simulation was performed to showcase the effectiveness of feedback control in stabilizing the system. The control algorithm was implemented on an ESP32 microcontroller using the ArduPilot open-source framework for unmanned aerial vehicles. The controller performs well within the boundary of stability of the operating region of ± 150° but shows increased oscillations, settling times, and overshoot beyond this range. This research holds significant implications for the field of soft computing and genetic algorithms. The adaptive control approach leverages these techniques to enhance system stability and performance under dynamic conditions. Additionally, the study is relevant to various aspects of signal processing such as VLSI signal processing. The adaptive controller effectively stabilizes both linear and nonlinear systems with parameter perturbations and tracks reference signals accurately, demonstrating the robustness of this method. This is caused due to the limitations of an individual thruster resulting in a non-minimum phase system. We conclude that the adaptive controller performs better for stabilizing both linear and nonlinear systems with perturbations in parameters. In addition to stabilizing the closed loop system, it also tracks the reference signals closely, which is what was expected. The plant states, after some initial oscillations, follow the reference states closely, and the adaptive gains do not grow unbounded.