The control of linear and nonlinear systems is of paramount importance when it comes to improvement of the plant performance. Many industries employ different modes of control when it comes to the proportional integral and derivative parameter tuning. Tuning control systems, whether for linear or nonlinear systems, can be a complex and challenging task. The challenges associated with tuning Linear Systems are Model Accuracy, Controller Selection, Parameter Tuning, System Variability while Challenges in Tuning Nonlinear Systems are Model Complexity, Multiple Equilibrium while for Nonlinear Control Strategies the challenges are Traditional linear control strategies may not be directly applicable to nonlinear systems, Input saturation, Uncertainty and Disturbances: Computational Complexity, Experimental Tuning: Safety Concerns: Overall, tuning control systems, whether for linear or nonlinear systems requires advanced tools and techniques such as particle swarm optimization. The effect on changing inertia weight so as to reduce the error was examined. The purpose of using Improved Particle Swarm Optimization is to understand its impact while tuning the Proportional Integral and Derivative parameters. The research used a standard nonlinear system depicting the real life situation, then using improved particle swarm optimization algorithm to analyze and compare the improved behavior on the MATLAB/Simulink tool box as applied to the proportional integral and derivative parameters. Finally it was logically realized that the improved particle swarm optimization algorithm system response was much better in comparison with the standard particle swarm optimization tuned system. It was noted that as the iteration was changed from 10, 50 all the way to 100, there was a significant reduction in integral time absolute error from 0.054806 to a minimum of 0.01900, which was far better than the standard particle swarm optimization algorithm. Standard particle swarm optimization reduced the error from 0.065143 to 0.020476.It is noted that the error is reduced from 5.4% to 1.9% which is significant achievement by the improved particle swarm optimization. The simulated system addressed the problem faced in dealing with the challenges in the conventional tuning of the proportional, integral and derivative parameters. The improved particle swarm optimization was proved to perform better compared to the standard particle swarm optimization and therefore is recommended for application in the industry.