To address the issue of reduced positioning accuracy caused by joint friction in Scara robots, an improved glowworm swarm optimization is proposed to identify the parameters of the friction model. Two optimizations are made on the basis of the traditional algorithm: by combining the Levy flight strategy and inertia factor, non-Gaussian random walks and adaptive inertia weights are utilized to randomly initialize fireflies trapped in local optima, enhancing the algorithm’s global search capability; the simulated annealing algorithm is introduced to perform local annealing operations on potential optimal solutions, improving the algorithm’s local optimization ability. Through performance analysis of test functions and parameter identification experiments, the results show that the improved artificial firefly algorithm has better optimization performance compared to other optimization algorithms. Finally, to further verify the effectiveness of the friction model identified through the algorithm, a fuzzy PID controller based on friction compensation is designed for the robot trajectory tracking control experiment. The experimental results indicate that the identified friction model has high accuracy, and the proposed control method can effectively suppress the adverse effects of joint friction on the trajectory tracking control of Scara robots compared to using only the fuzzy PID control method. The position tracking errors of the two joints of the robot are reduced by 76.1% and 81.9% respectively, further improving the positioning accuracy of the robot.