Design and implementation of Hybrid TSA PID controller for an Electro Pneumatic systems

Electro ‑ Pneumatic Position Control Systems (EPPCSs) are likewise nonlinear and subject to significant uncertainty because of air compressibility, supply pressure fluctuations, temperature changes, friction and internal leakage. These factors make conventional PID tuning is challenging and often inadequate across all operating ranges. This leads to the development of a hybrid optimization approach that combines Tree-Seed Algorithm (TSA) and Genetic Algorithm (GA) to determine the optimal PID controller parameters for a smart electro-pneumatic positioner. In Hybrid TSA GA approach: TSA contributes an efficient local search through its biologically inspired seed dispersal mechanism, while GA enhances global exploration using selection, crossover and mutation operations. This algorithm synergistically integrates the strengths of both methods to achieve a more robust and globally optimal solution for PID gain tuning. The optimized controller is designed to drive the smart electro pneumatic positioner, thereby regulating airflow to the cylinder and precisely controlling piston displacement. The objective of this optimization is to improve performance criteria over conventional and TSA PID controller. The results underscore the effectiveness of the proposed system for high precision real time Electro ‑ pneumatic systems.