An Artificial Bee Colony Based PID Tuning Algorithm for Inverted Pendulum and Cart System

The Proportional, Integral, Derivative (PID) controller finds widespread use in various control applications, yet accurately tuning it can prove challenging and time-consuming, especially for intricate systems characterized by non-linearities and uncertainties. To address these challenges, an approach rooted in swarm intelligence-inspired optimization techniques, drawn from honeybee foraging behavior, is proposed for PID tuning, specifically employing the Artificial Bee Colony (ABC) algorithm. This algorithm is tailored for the control of an inverted pendulum and cart system. The ABC algorithm offers an efficient and automated avenue to navigate the parameter space and dynamically adjust PID gains according to performance criteria. Within this study, the inverted pendulum and cart model serves as a testing ground to evaluate the effectiveness of the proposed ABC-based PID tuning algorithm. The intricate presence of non-linearities and uncertainties in the system renders achieving precise control a formidable task. By harnessing the potential of the ABC algorithm, the objective is to uncover optimal PID gains that augment control performance, stability, and robustness amidst these complexities. The outcomes underscore the algorithm’s adeptness in exploring the parameter space, tailoring gains based on performance feedback, and adeptly mitigating the system’s non-linearities and uncertainties. The proposed approach bears multiple advantages, including diminished reliance on manual trial-and-error tuning, heightened automation, and improved control performance. By capitalizing on the strengths inherent to the ABC algorithm, engineers can streamline the PID tuning process and realize enhanced control outcomes for intricate systems.