In this work, a simple evolving neuro-fuzzy system with less learning parameters is utilized to develop an intelligent controller namely Reduced Parsimonious Controller (RedPAC). The proposed RedPAC is a simplified version of one of the recently developed intelligent controller called Parsimonious Controller (PAC). In RedPAC, the network parameters are reduced into two steps. Firstly, unlike the conventional fuzzy logic or neuro-fuzzy-based intelligent controller, it has no premise parameters. Secondly, in contrast with PAC, the number of consequent parameters have further reduced to one parameter per rule in RedPAC. The sliding mode control (SMC) technique is utilized to adapt consequent parameters of RedPAC, where the SMC-based auxiliary robustifying control term has guaranteed the uniform asymptotic convergence of tracking error to zero. The proposed controller's performance has been evaluated by implementing it to control a quadcopter unmanned aerial vehicle (UAV) simulator namely Dronekit. In addition, trajectory tracking performance of the quadcopter is compared with three different benchmark controllers namely a linear PID, a nonlinear SMC, and an intelligent controller called PAC. RedPAC outperforms PID and SMC techniques. The results of tracking trajectories are also comparable to PAC; however, RedPAC needs comparatively less learning parameters to obtain a similar or better tracking accuracy.