Velocity Control of DC Motor Based Intelligent Methods and Optimal Integral State Feed Back Controller

Abstract

Velocity control of DC motors is an important issue also shorter settling time is desired. In this paper at first a PID compensator which adjusted by genetic algorithm is designed then another compensator will be designed by combining two methods, Integral controller and optimal State Feedback controller (I&S.F.). In the second compensator, design specifications, depend on choosing weighting matrices Q and R, we use the Genetic Algorithm (GA) to find the proper weighting matrices. Of course Kalman filter is used as a system observer in order to increasing the system robustness. Then the performance of the control techniques is compared in terms of rise time, settling time, tracking error, and robustness with respect to modeling errors and disturbances. The controller design process and implementation requirements are also discussed. Then the comparison between the PID control and the optimal control shows that the optimal controller significantly reduced the overshoot, settling time and has the best performance encountering with system uncertainties. Also we apply noise and 20% parameters variation for DC motor and compare the results. According to the simulation results, the second controller has better performance than the PID controller.

Paper Machine Industrial Analysis on Moisture Control Using BF-PSO Algorithm and Real Time Implementation Setup through Embedded Controller

“…K p = proportional gain, K i = integral constant and K d = derivative constant PID controllers are most common controllers in most of the industry [12][13][14][15]. Schematic diagram for PID control is shown in Fig.…”

PID Controller Optimization By Soft Computing Techniques-A Review

“…The limitations of genetic algorithm in tuning a multivariable system were explored in [10]. GA has been used in position and speed control of a DC motor [11][12]. GA has been used for PID of reverse osmosis and cascade control systems tuning in [13][14][15] .…”

Robust tuning and sensitivity analysis of stochastic integer andfractional‐order PIDcontrol systems: application ofsurrogate‐basedrobustsimulation‐optimization

“…A common and effective tool for solving such an optimal design of controllers is genetic algorithms (GAs) 13,14 . Some other well‐known model‐based methods applied in optimal control design are Grey Wolf Optimizer (GWO), 15 GA, 16‐20 Particle Swarm Optimization (PSO), 18,21 Ant‐Colony Optimization (ACO), 22 and Evolutionary Programming (EP) 18 . The simplicity and global characteristics of such model‐based methods have been the main reasons for their extensive applications in off‐line optimum control system design 5 .…”