Due to simplicity and robustness, classical PID and SMC have been still widely used in practical applications. Performance of these controllers (PID and SMC) depends upon the value of some of the constant controller parameters. To avoid the most commonly used tedious trial and error method, this paper proposes an improved PSO based method for getting the optimized value of these parameters. For validation purpose these improved PSO tuned Proportional Integral Derivative (PID) and Sliding Mode (SMC) classical controllers have been applied for the motion control problem of the robotic manipulator. The chattering problem of SMC has been handled by using pseudo sliding function. Further results have been analyzed by comparing them with the basic conventional controllers. Results and conclusions are based on simulation results.

In this paper, an Evolutionary Optimized Neural Network (EONN) based control scheme is proposed. This control scheme is based on the fact that optimizing values of a few parameters of neural network can enhance its control performance. Radial Biased Neural Network (RBNN) is chosen here and PSO, one of the most emerging global optimizing techniques, is used to optimize the parameters of a RBNN. From hidden to output layer RBNN uses Gaussian function for mapping. Spread factor (s) of this intelligent RBNN is then optimized by a modified PSO to improvise its performance. The proposed controller has been verified by implementing it for position control of a robotic manipulator. For comparison purpose, proposed scheme has been verified with RBNN and the classical PD controller. MATLAB environment has been chosen for simulation study carried out. Robustness of the proposed controller has been checked by applying it to the manipulator for three different paths.