Comparison of Performance Measures of Speed Control for a DC Motor Using Hybrid Intelligent Controller and Optimal LQR

2014 ◽  
Vol 622 ◽  
pp. 23-31
Author(s):  
T. Velayudham Narmadha ◽  
Chackaravarthy Baskaran ◽  
K. Sivakumar
Keyword(s):  
Rise Time ◽  
Linear Quadratic Regulator ◽  
Speed Control ◽  
Dc Motor ◽  
Pid Controllers ◽  
Rule Base ◽  
Fuzzy Pid ◽  
Linear Quadratic ◽  
Lqr Controller ◽  
Fuzzy Pd

-In this paper , performance of fuzzy PD , fuzzy PI , fuzzy PD+I , fuzzy PID controllers are evaluated and compared. This paper also describes the speed control based on Linear Quadratic Regulator (LQR) technique. The comparison is based on their ability of controlling the speed of DC motor, which merely focuses on performance index of the controllers, and also time domain specifications such as rise time, settling time and peak overshoot. The controller is modelled using MATLAB software, the simulation results shows that the fuzzy PID controllers are the best performing candidates in all aspects but it as higher overshoot and IAE in comparison with optimal LQR. The Fuzzy PI controller exhibited null offset but suffers from poor stability and peak overshoot, whereas the fuzzy PD controller has fast rise time, with no overshoots but the IAE is much greater. Thus, the comparative analysis recommends fuzzy PID controller but it is usually associated with complicated rule base and tedious tuning. To circumvent these problems, the proposed LQR controller gives better performance than the other controllers.

scholarly journals The linear quadratic regular algorithm-based control system of the direct current motor

2019 ◽  
Vol 10 (2) ◽  
pp. 768
Author(s):  
Trong-Thang Nguyen
Keyword(s):  
Control System ◽  
Direct Current ◽  
State Space Model ◽  
Linear Quadratic Regulator ◽  
Response Speed ◽  
Dc Motor ◽  
Linear Quadratic ◽  
Lqr Controller ◽  
Mathematical Equations ◽  
Feed Forward Controller

<span>This research aims to propose an optimal controller for controlling the speed of the Direct Current (DC) motor. Based on the mathematical equations of DC Motor, the author builds the equations of the state space model and builds the linear quadratic regulator (LQR) controller to minimize the error between the set speed and the response speed of DC motor. The results of the proposed controller are compared with the traditional controllers as the PID, the feed-forward controller. The simulation results show that the quality of the control system in the case of LQR controller is much higher than the traditional controllers. The response speed always follows the set speed with the short conversion time, there isn't overshoot. The response speed is almost unaffected when the torque impact on the shaft is changed.</span>

scholarly journals Comparison and Speed Control of DC Motor and DC Servomotor Using IMC Based PID Controller

2021 ◽  
Vol 6 (12) ◽  
pp. 493-501
Author(s):  
Amarapini Divya and Dr.Prasadarao Bobbili
Keyword(s):  
Time Domain ◽  
Rise Time ◽  
Pid Controller ◽  
Speed Control ◽  
Dc Motor ◽  
Settling Time ◽  
Peak Amplitude ◽  
Pid Controllers ◽  
Frequency Method ◽  
Dc Servomotor

IMC based PID controllers are being used to speed control of DC motor and DC servomotor in industry. As this controller offer good performance comparitive to conventional controllers like PI, PID and Ziegler Nichols frequency method controllers. This paper presents the speed control of the DC motor and DC servomotor using PI, PID, Ziegler Nichols method and IMC-PID controllers, to realize the optimization of control action. A mathematical calculation of DC motor and DC servomotor has developed and simulations are carried out in MATLAB/ Simulink environment. From the results, it is observed that time domain parameters like rise time 0.6 secs, settling time 2 secs, speed for peak over shoot 1450, peak amplitude 1, with no oscillations using IMC-PID controller on DC motor. And for DC servomotor its rise time is 0.3 seconds, settling time is 1 second, speed for peak overshoot 1450 rpm, peak amplitude 1 with absence of oscillations by using IMC-PID controller

scholarly journals Analyze and Evaluate the Performance Velocity Control in DC Motor

2020 ◽  
Vol 12 (4) ◽  
pp. 507-516
Author(s):  
Hazim M. Alkargole ◽  
◽  
Abbas S. Hassan ◽  
Raoof T. Hussein ◽  
◽  
...  
Keyword(s):  
Fuzzy Logic Controller ◽  
Linear Quadratic Regulator ◽  
Dc Motor ◽  
Rule Base ◽  
Velocity Control ◽  
Linear Quadratic ◽  
Proportional Integral ◽  
Motor Controller ◽  
Different Types ◽  
Rule Bases

A mathematical model of controlling the DC motor has been applied in this paper. There are many and different types of controllers have been used with purpose of analyzing and evaluating the performance of the of DC motor which are, Fuzzy Logic Controller (FLC), Linear Quadratic Regulator (LQR), Fuzzy Proportional Derivative (FPD) ,Proportional Integral Derivative (PID), Fuzzy Proportional Derivative with integral (FPD plus I) , and Fuzzy Proportional Integral (FPI) with membership functions of 3*3, 5*5, and 7*7 rule bases. The results show that the (FLC) controller with 5*5 rule base provides the best results among all the other controllers to design the DC motor controller.

scholarly journals Optimal Tuning of Linear Quadratic Regulator Controller Using Ant Colony Optimization Algorithm for Position Control of a Permanent Magnet DC Motor

2020 ◽  
pp. 29-41
Keyword(s):  
Cost Function ◽  
Permanent Magnet ◽  
Ant Colony Optimization ◽  
Position Control ◽  
Particle Swarm ◽  
Linear Quadratic Regulator ◽  
Dc Motor ◽  
Ant Colony ◽  
Linear Quadratic ◽  
Lqr Controller

This paper presents the design of an optimal Linear Quadratic Regulator (LQR) controller using Ant Colony Optimization (ACO) and particle swarm optimization (PSO) methods for position control of a permanent magnet DC (PMDC) motor. In this work, Ant Colony control and particle swarm control algorithms have been utilized to set the optimal elements of the weighting matrices subjected to a proposed cost function. The proposed cost function is a combination of the quadratic performance index and integral square error. The proposed design can overcome the difficulty in setting the weighting matrices with the suitable elements. The simulation results using (Matlab Package) show that the optimal LQR controller using ACO algorithm can give excellent performance in terms of obtaining smooth and unsaturated state voltage control action that will stabilize the DC motor system performance and minimize the position tracking error of the system output. In addition, the rising time and settling time is decreased in comparison with the LQR based PSO controller performance.

Auto PID Tuning of Speed Control of DC Motor Using Particle Swarm Optimization Based on FPGA

2015 ◽  
Vol 776 ◽  
pp. 390-395 ◽  
Author(s):  
Hilal Tayara ◽  
Deok Jin Lee ◽  
Kil To Chong
Keyword(s):  
Particle Swarm Optimization ◽  
Steady State ◽  
Rise Time ◽  
Particle Swarm ◽  
Speed Control ◽  
Dc Motor ◽  
Pid Controllers ◽  
Proportional Integral Derivative ◽  
Swarm Optimization ◽  
Pid Tuning

This paper introduces auto tuning of proportional-integral-derivative (PID) controllers of DC motor using particle swarm optimization (PSO) method. The DC motor was modeled in Simulink and PSO was implanted on FPGA “cyclone IV E” using the soft processor NIOS II. The results were efficient in reducing the steady state error, settling time, rise time and maximum overshoot in speed control of a DC motor.

Anti-windup method for fuzzy PD+I, PI and PID controllers applied in brushless DC motor speed control

2013 ◽  
Author(s):  
J. R. B. A. Monteiro ◽  
W. C. A. Pereira ◽  
M. P. Santana ◽  
T. E. P. Almeida ◽  
G. T. Paula ◽  
...  
Keyword(s):  
Speed Control ◽  
Dc Motor ◽  
Pid Controllers ◽  
Brushless Dc Motor ◽  
Motor Speed ◽  
Brushless Dc ◽  
Fuzzy Pd

scholarly journals Fuzzy PID Controllers Using FPGA Technique for Real Time DC Motor Speed Control

2011 ◽  
Vol 02 (03) ◽  
pp. 233-240 ◽  
Author(s):  
Basil Hamed ◽  
Moayed Almobaied
Keyword(s):  
Real Time ◽  
Speed Control ◽  
Dc Motor ◽  
Pid Controllers ◽  
Fuzzy Pid ◽  
Motor Speed

Interval linear quadratic regulator and its application for speed control of DC motor in the presence of uncertainties

2021 ◽  
Author(s):  
Yuan Zhi ◽  
Wang Weiqing ◽  
Cheng Jing ◽  
Navid Razmjooy
Keyword(s):  
Linear Quadratic Regulator ◽  
Speed Control ◽  
Dc Motor ◽  
Linear Quadratic ◽  
Interval Linear

Robust stabilization control of a spatial inverted pendulum using integral sliding mode controller

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Ishan Chawla ◽  
Vikram Chopra ◽  
Ashish Singla
Keyword(s):  
Inverted Pendulum ◽  
Sliding Mode ◽  
Robust Stabilization ◽  
Linear Quadratic Regulator ◽  
Humanoid Robots ◽  
Sliding Mode Controller ◽  
Linear Quadratic ◽  
Integral Sliding Mode ◽  
Wide Range ◽  
Lqr Controller

AbstractFrom the last few decades, inverted pendulums have become a benchmark problem in dynamics and control theory. Due to their inherit nature of nonlinearity, instability and underactuation, these are widely used to verify and implement emerging control techniques. Moreover, the dynamics of inverted pendulum systems resemble many real-world systems such as segways, humanoid robots etc. In the literature, a wide range of controllers had been tested on this problem, out of which, the most robust being the sliding mode controller while the most optimal being the linear quadratic regulator (LQR) controller. The former has a problem of non-robust reachability phase while the later lacks the property of robustness. To address these issues in both the controllers, this paper presents the novel implementation of integral sliding mode controller (ISMC) for stabilization of a spatial inverted pendulum (SIP), also known as an x-y-z inverted pendulum. The structure has three control inputs and five controlled outputs. Mathematical modeling of the system is done using Euler Lagrange approach. ISMC has an advantage of eliminating non-robust reachability phase along with enhancing the robustness of the nominal controller (LQR Controller). To validate the robustness of ISMC to matched uncertainties, an input disturbance is added to the nonlinear model of the system. Simulation results on two different case studies demonstrate that the proposed controller is more robust as compared to conventional LQR controller. Furthermore, the problem of chattering in the controller is dealt by smoothening the controller inputs to the system with insignificant loss in robustness.

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