Effective Control of a Robotic Manipulator's Trajectory Tracking through the utilization of an Optimal Linear Quadratic Gaussian (LQG) controller enhanced by the Improved Particle Swarm Optimization (IPSO) Algorithm

الملخص

This paper introduces a technique for modeling and effectively controlling the trajectory tracking of a robotic manipulator through an LQG controller optimized by the IPSO algorithm. Extensive simulations have been conducted within the MATLAB simulation environment. The LQG, which combines a Kalman Filter (KF) and a Linear Quadratic Regulator (LQR), is developed to follow the desired input for the robotic manipulator while minimizing the impact of process and measurement noise on its performance. The parameters of the LQG controller, which include the elements of the state and control weighting matrices for the LQR and KF, are optimally adjusted using the IPSO method. The validation of the proposed hybrid LQG-IPSO controller is conducted based on the control criteria parameters. The findings demonstrate that the proposed hybrid LQG-IPSO controller can achieve excellent movement performance. To fully leverage the capabilities of the IPSO algorithm, careful adjustment and determination of IPSO parameters such as inertia weight, iteration count, acceleration constants, and particle quantity are essential. Therefore, The initial focus of this study is to conduct a comparative analysis of various fitness functions; simulation results indicate that the proposed hybrid LQG-IPSO control method achieves commendable fitness outcomes with minimal steady-state error (ess). Following this, a comparison of the performances of a robotic manipulator using the hybrid LQG-IPSO controller, hybrid LQR-IPSO controller, LQG controller, and LQR controller is also presented. Based on the analysis, it can be concluded that superior performance parameters are attained with the hybrid LQG-IPSO controller when compared to its counterparts—the hybrid LQR-IPSO controller, LQG controller, and LQR controller—offering enhanced performance.