Numerical Modelling and Simulation-Based Optimization of NPID Controller for Robotic Manipulator System Using Genetic Algorithm

Abstract

Robotic manipulation has always posed a challenge for researchers and scientists, especially when dealing with the nonlinearity of manipulators. To address this issue, this paper proposed a robust non-linear proportional integral derivative (NPID) control structure for regulating a non-linear, coupled, two-link stiff robotic manipulator system. The NPID controller employs an error-dependent non-linear factor to enhance its performance. The gains of the controllers were optimized using the meta-heuristic optimization technique Genetic Algorithm, with the objective function defined as the integral of the absolute error change in controller output. The paper compared the performance of PID and NPI controllers with the NPID Controller for reference trajectory tracking, noise suppression, disturbance rejection, and model uncertainty. The simulation results showed that the proposed NPID controller outperformed the other controllers. The NPID controller's improved performance is because of its ability to handle the nonlinearity of the manipulators more effectively than the PID and NPI controllers. This study is a significant contribution to robotic manipulation, as it provides a viable solution to improve the performance of robotic manipulators in various applications.