A Nesterov-Accelerated Hybrid PSO Optimization Approach for PID-Based Control of a Twin Rotor MIMO System

This study proposes a hybrid optimization algorithm that combines Particle Swarm Optimization (PSO) with the Nesterov Accelerated Gradient method for efficient PID controller tuning in a Twin Rotor MIMO System (TRMS). The proposed approach employs PSO to perform a global search over the six-dimensional PID parameter space and identify promising candidate solutions. When the optimization process stagnates during successive PSO iterations, a Nesterov momentum-based local search is selectively activated to refine the current best solution, reduce the risk of local minima, and accelerate convergence. The improved solution is then reintegrated into the PSO population, enabling the global search to continue from a higher-quality candidate. PID parameters are optimized using a multi-objective function that considers tracking error, overshoot, steady-state error, and control effort. The proposed hybrid method is evaluated through MATLAB 2019 simulations and compared with conventional PSO-based PID tuning. The results show that the hybrid PSO–Nesterov approach achieves a lower objective function value, improves convergence behavior, and enhances the transient response of the control system, demonstrating its effectiveness for TRMS PID parameter optimization.