Neeraj Jain - Academia.edu (original) (raw)

Papers by Neeraj Jain

ICTACT Journal on Soft Computing, 2016

In this paper swarm intelligence based PID controller tuning is proposed for a nonlinear ball and... more In this paper swarm intelligence based PID controller tuning is proposed for a nonlinear ball and hoop system. Particle swarm optimization (PSO), Artificial bee colony (ABC), Bacterial foraging optimization (BFO) is some example of swarm intelligence techniques which are focused for PID controller tuning. These algorithms are also tested on perturbed ball and hoop model. Integral square error (ISE) based performance index is used for finding the best possible value of controller parameters. Matlab software is used for designing the ball and hoop model. It is found that these swarm intelligence techniques have easy implementation & lesser settling & rise time compare to conventional methods.

Cogent Engineering, 2018

The present work deals with robustness and comparative analysis of grey wolf optimization (GWO)/p... more The present work deals with robustness and comparative analysis of grey wolf optimization (GWO)/proportional-integral-derivative (PID) approach in control of a ball and hoop (BH) system with different objective functions. The BH system is a device consisting of a ball rolling on the rim of a hoop and can be used to analyse the dynamics of liquid slosh problems. The commonly used objective functions are integral absolute error (IAE), integral square error (ISE), integral of square time multiplied by absolute error (ISTAE) and integral of time multiplied absolute error (ITAE). These objective functions have been minimized with the GWO algorithm to obtain parameters of the PID controller for the control of BH system. The robustness analysis of the proposed GWO/PID approach has also been carried out with ±5% perturbation in locations of the poles. The poles of BH system are perturbed by ±5% of the nominal value and the same PID controller tuned by the GWO algorithm has been applied on the perturbed systems. The comparative analysis of GWO/PID approach is demonstrated with several other existing techniques in terms of the transient response's parameters. The results of the GWO/PID approach outperform certain of the existing techniques in the literature, and the performance of the controller hardly alters with perturbation and objective functions; after it is tuned by GWO.

International Journal of …, Jan 1, 2008

Advances in Intelligent Computing, Jan 1, 2005

WSEAS Transactions on Power Systems, Jan 1, 2008

$Research paper thumbnail of Tuning of fractional PID controllers using PSO algorithm for robot trajectory control$

The aim of the paper is to compare three different controllers tuned with a particle swarm optimi... more The aim of the paper is to compare three different controllers tuned with a particle swarm optimization (PSO) for robot trajectory control. For this reason, Fractional order PID controller (FOPID) tuned with PSO was studied primarily for a 2 DOF planar robot. As the parameters of FOPID controller were optimized by PSO for a given trajectory, three different cost functions were used for better comparison. In order to compare the performance of the optimized FOPID controller with other controllers, the Fuzzy logic controller (FLC) and the PID controller were also tuned with PSO. In order to test the robustness of the tuned controllers, the model parameters and the given trajectory were changed and the white noise was added to the system. All of the trajectory simulation results show that FOPID controller tuned by PSO has good performance and it is better than the FLC and the PID tuned by PSO. Also, the results obtained from robustness test indicate the superior robustness of the FOPID controller on the others.

… and Systems, 2008. …, Jan 1, 2008

This paper proposes a new PID parameter tuning method using particle swarm optimization (PSO) wit... more This paper proposes a new PID parameter tuning method using particle swarm optimization (PSO) without tuning operatorpsilas know-how. The method searches the PID parameter that realizes the expected step response of the plant. The expected response is defined by the overshoot ratio, the rising time, the settling time. The method is implemented into the PID tuning tool on a personal computer. The plant model represented by the transfer function is obtained by system identification on the PID tuning tool. The PID parameter is computed by PSO-based PID tuning method according to the obtained model. The numerical result and the experiment result show the effectiveness of the proposed tuning method and the developed tool.

… Intelligence in Robotics …, Jan 1, 2009

Mechatronics and Its …, Jan 1, 2008

In this paper, a novel method for tuning PID controller of automatic gantry crane control using p... more In this paper, a novel method for tuning PID controller of automatic gantry crane control using particle swarm optimization (PSO) is proposed. PSO is one of the most recent optimization techniques based on evolutionary algorithm. PSO is also known as computationally efficient method. This work presents in detail how to apply PSO method in finding the optimal PID gains of gantry crane system in the fashion of min-max optimization. The simulation results show that with proper tuning a satisfactory PID control performance can be achieved to drive nonlinear plant. The controller is able to effectively move the trolley of the crane in short time while canceling the swing angle of the payload hanging on the trolley at the end position. The robustness of the controller is also tested.

Energy Conversion, IEEE Transactions on, Jan 1, 2004

The PID controller is one of the most popular controllers, due to its remarkable effectiveness, s... more The PID controller is one of the most popular controllers, due to its remarkable effectiveness, simplicity of implementation and broad applicability. However, the conventional approach for parameter optimization in PID controller is easy to produce surge and big overshoot, and therefore heuristics optimization methods such as genetic algorithm (GA), particle swarm optimization (PSO) are employed to enhance the capability of traditional techniques. One major problem of these algorithms is that they may be trapped in the local optima of the objective and lead to poor performance. In this paper, a novel stochastic optimization technique named particle filter optimization (PFO) is proposed to achieve better performance in dealing with local optima while reduce the computation complexity of PID parameter tuning process. Simulation results indicate that the proposed algorithm is effective and efficient, and demonstrate that the proposed algorithm exhibits a significant performance improvement over several other benchmark methods.

The aim of this paper is to study the tuning of a PID controller using soft computing techniques.... more The aim of this paper is to study the tuning of a PID controller using soft computing techniques. The methodology and efficiency of the proposed method are compared with that of traditional methods. Determination or tuning of the PID parameters continues to be important as these parameters have a great influence on the stability and performance of the control system. The results obtained reflect that use of soft computing based controller improves the performance of process interms of time domain specifications, set point tracking, regulatory changes and also provides an optimum stability.