A response time reduction for DC motor controller using SISO technique

Abstract

In an industrial controller, over five decades there are many attempts had been proposed to improve a method of tuning proportional gains of PID controller. Where in the review there is a very little attention have been paid to use satisfactory tuning to get maximum performance. This paper proposes an alternative solution to maximize optimization for a controller-based DC motor. The novel methodology relies on merge proper tuning with optimization using SISO-Optimization technique-based tune ). The comparative study has been done by utilizing classical tuning methods Z N, SIMC, CHR, and AMIGO, to obtain suitable tuning to be joined with . The proposed PID controller was examined in term of response time characteristics. This strategy provides a superior reduction in peak overshoot Pos, dead time td, rise time tr, settling time ts, and peak time tp, that could be utilized to improve the responses of a DC motor controller. Based on comparison results, it was founded that a CHR based SISO_optimization playing a superior role over others in term of Pos 0%, td 0.1811µsec., tr 17.2 µsec, ts30.7 µsec, tp 80 µsec, and the number of iterations iter No 9.Ultimatly,this work overcome the majority of previouse work that related with this approach.

Optimal backstepping controller for controlling DC motor speed

“…The optimal backstepping controller is simulated using Matlab/Simulink environment and the parameters values of the DC motor system are given in Table 1 [26]. The SSA tuning algorithm are used for finding the optimal gains of the controller to obtain the desired output for the system and the parameters used for achieve suitable control action by reducing the tracking error with a minimum value of objective function are listed in Table 2.…”

Perspective Chapter: Fabulous Design Speed Industrial Robotic Arm

“…Therefore, it is necessary to define input signals such as torque and drive input voltage to achieve the desired operation [22,23]. For several decades, the Proportional Integral Derivative (PID) algorithm is one of the most widely used in the industry for controlling closedloop feedback systems, because of the less complexity with the ability to meet desired controller's functions for wide scope plant models [24][25][26]. It stands for three proportional gains: proportional (Kp), integral (Ki), and derivative (Kd), where they should be jointly tuned to get better performance [27,28].…”

A Novel HSPICS for Industrial Robotic Controller Based on FPGA_SoC: Modelling and Fabrication

“…Thus, most industrial processes are non-linear, and some process is challenging to establish the estimation of TF form. Consequently, the classical PID controller cannot achieve desired closed-loop response if the mathematical form of a TF of such plant system not modelled accurately [30,31], thus, without achieving an accurate TF, the implementation of compensator design and stability analysis is rendered insufficient [32,33], and this is the second challenge to get higher performance controller design . There are several studies for estimation TF used digital devices such as oscilloscope to collect data in data out to be imported into system identification application (Sys Ident).However, there is a drawback in this method that comes from lousy collection data with limited sampling, causing the lowest fitting between injected signal and angular speed (θ), leads to lousy estimation TF form [34,35].…”