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Modified Particle Swarm Optimization Based PID for Movement Control of Two-Wheeled Balancing Robot

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@article{IJASEIT9485,
   author = {Nurul Hasanah and - Alrijadjis and Bambang Sumantri},
   title = {Modified Particle Swarm Optimization Based PID for Movement Control of Two-Wheeled Balancing Robot},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {9},
   number = {4},
   year = {2019},
   pages = {1154--1162},
   keywords = {modified PSO; balancing robot; PID; IAE; ISE; benchmark function.},
   abstract = {Two-wheeled balancing robot is a mobile robot that has helped various human’s jobs such as the transportations. To control stability is still be the challenges for researchers. Three equations are obtained by analyzing the dynamics of the robot with the Newton approach. To control three degrees of freedom (DOF) of the robot, PIDs is tuned automatically and optimized by multivariable Modified Particle Swarm Optimization (MPSO). Some parameters of the PSO process are modified to be a nonlinear function. The inertia weight and learning factor variable on PSO are modified to decreasing exponentially and increasing exponentially, respectively. The Integral Absolute Error (IAE) and Integral Square Error (ISE) evaluate the error values. The performances of MPSO and PSO classic are tested by several Benchmark functions. The results of the Benchmark Function show that Modified PSO proposed to produce less error and overshoot. Therefore, the MPSO purposed are implemented to the plant of balancing robot to control the angle, the position, and the heading of the robot. The result of the simulation built shows that the MPSO – PID can make the robot moves to the desired positions and maintain the stability of the angle of the robot. The input of distance and angle of the robot are coupling so MPSO needs six variable to optimize the PID parameters of balancing and distance control.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=9485},
   doi = {10.18517/ijaseit.9.4.9485}
}

EndNote

%A Hasanah, Nurul
%A Alrijadjis, -
%A Sumantri, Bambang
%D 2019
%T Modified Particle Swarm Optimization Based PID for Movement Control of Two-Wheeled Balancing Robot
%B 2019
%9 modified PSO; balancing robot; PID; IAE; ISE; benchmark function.
%! Modified Particle Swarm Optimization Based PID for Movement Control of Two-Wheeled Balancing Robot
%K modified PSO; balancing robot; PID; IAE; ISE; benchmark function.
%X Two-wheeled balancing robot is a mobile robot that has helped various human’s jobs such as the transportations. To control stability is still be the challenges for researchers. Three equations are obtained by analyzing the dynamics of the robot with the Newton approach. To control three degrees of freedom (DOF) of the robot, PIDs is tuned automatically and optimized by multivariable Modified Particle Swarm Optimization (MPSO). Some parameters of the PSO process are modified to be a nonlinear function. The inertia weight and learning factor variable on PSO are modified to decreasing exponentially and increasing exponentially, respectively. The Integral Absolute Error (IAE) and Integral Square Error (ISE) evaluate the error values. The performances of MPSO and PSO classic are tested by several Benchmark functions. The results of the Benchmark Function show that Modified PSO proposed to produce less error and overshoot. Therefore, the MPSO purposed are implemented to the plant of balancing robot to control the angle, the position, and the heading of the robot. The result of the simulation built shows that the MPSO – PID can make the robot moves to the desired positions and maintain the stability of the angle of the robot. The input of distance and angle of the robot are coupling so MPSO needs six variable to optimize the PID parameters of balancing and distance control.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=9485
%R doi:10.18517/ijaseit.9.4.9485
%J International Journal on Advanced Science, Engineering and Information Technology
%V 9
%N 4
%@ 2088-5334

IEEE

Nurul Hasanah,- Alrijadjis and Bambang Sumantri,"Modified Particle Swarm Optimization Based PID for Movement Control of Two-Wheeled Balancing Robot," International Journal on Advanced Science, Engineering and Information Technology, vol. 9, no. 4, pp. 1154-1162, 2019. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.9.4.9485.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Hasanah, Nurul
AU  - Alrijadjis, -
AU  - Sumantri, Bambang
PY  - 2019
TI  - Modified Particle Swarm Optimization Based PID for Movement Control of Two-Wheeled Balancing Robot
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 9 (2019) No. 4
Y2  - 2019
SP  - 1154
EP  - 1162
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - modified PSO; balancing robot; PID; IAE; ISE; benchmark function.
N2  - Two-wheeled balancing robot is a mobile robot that has helped various human’s jobs such as the transportations. To control stability is still be the challenges for researchers. Three equations are obtained by analyzing the dynamics of the robot with the Newton approach. To control three degrees of freedom (DOF) of the robot, PIDs is tuned automatically and optimized by multivariable Modified Particle Swarm Optimization (MPSO). Some parameters of the PSO process are modified to be a nonlinear function. The inertia weight and learning factor variable on PSO are modified to decreasing exponentially and increasing exponentially, respectively. The Integral Absolute Error (IAE) and Integral Square Error (ISE) evaluate the error values. The performances of MPSO and PSO classic are tested by several Benchmark functions. The results of the Benchmark Function show that Modified PSO proposed to produce less error and overshoot. Therefore, the MPSO purposed are implemented to the plant of balancing robot to control the angle, the position, and the heading of the robot. The result of the simulation built shows that the MPSO – PID can make the robot moves to the desired positions and maintain the stability of the angle of the robot. The input of distance and angle of the robot are coupling so MPSO needs six variable to optimize the PID parameters of balancing and distance control.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=9485
DO  - 10.18517/ijaseit.9.4.9485

RefWorks

RT Journal Article
ID 9485
A1 Hasanah, Nurul
A1 Alrijadjis, -
A1 Sumantri, Bambang
T1 Modified Particle Swarm Optimization Based PID for Movement Control of Two-Wheeled Balancing Robot
JF International Journal on Advanced Science, Engineering and Information Technology
VO 9
IS 4
YR 2019
SP 1154
OP 1162
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 modified PSO; balancing robot; PID; IAE; ISE; benchmark function.
AB Two-wheeled balancing robot is a mobile robot that has helped various human’s jobs such as the transportations. To control stability is still be the challenges for researchers. Three equations are obtained by analyzing the dynamics of the robot with the Newton approach. To control three degrees of freedom (DOF) of the robot, PIDs is tuned automatically and optimized by multivariable Modified Particle Swarm Optimization (MPSO). Some parameters of the PSO process are modified to be a nonlinear function. The inertia weight and learning factor variable on PSO are modified to decreasing exponentially and increasing exponentially, respectively. The Integral Absolute Error (IAE) and Integral Square Error (ISE) evaluate the error values. The performances of MPSO and PSO classic are tested by several Benchmark functions. The results of the Benchmark Function show that Modified PSO proposed to produce less error and overshoot. Therefore, the MPSO purposed are implemented to the plant of balancing robot to control the angle, the position, and the heading of the robot. The result of the simulation built shows that the MPSO – PID can make the robot moves to the desired positions and maintain the stability of the angle of the robot. The input of distance and angle of the robot are coupling so MPSO needs six variable to optimize the PID parameters of balancing and distance control.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=9485
DO  - 10.18517/ijaseit.9.4.9485