Motors Synchronization for Mobile Robots Using PID Control

Chen, Chih Liang; Guo, Yi; Zhang, Jun Hao

doi:10.1109/is3c.2014.223

Cited by 1 publication

(4 citation statements)

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“…PID control method is a linear system method. Because DDWMR robots have many states to control, the feedback gains became too many that it would be challenging to select them [10]- [12].…”

Section: Introductionmentioning

confidence: 99%

“…Control systems combine with optimization methods to improve quality and reduce development costs to achieve high-quality output [20]. In [12], PID gain parameters coefficients are tuned manually. Although the control technique is PID, [12] used PDI instead because the D-control term contributes more to the application's accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…In [12], PID gain parameters coefficients are tuned manually. Although the control technique is PID, [12] used PDI instead because the D-control term contributes more to the application's accuracy. The robot in [12] used an infrared sensor as a feedback to avoid obstacles and find cargo manually.…”

Section: Introductionmentioning

confidence: 99%

“…Although the control technique is PID, [12] used PDI instead because the D-control term contributes more to the application's accuracy. The robot in [12] used an infrared sensor as a feedback to avoid obstacles and find cargo manually. Because of the stability of their system, finding the best gain would be a problem.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Swarm Intelligence Autotune For Differential Drive Wheeled Mobile Robot

Auzan

Hujja²,

Fuadin³

et al. 2022

IJEEI

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Differential Drive Wheeled Mobile Robot (DDWMR) is a nonholonomic robot with constrained movement. Such constraint makes robot position control more difficult. A closed-loop control system such as PID can control robot position. However, DDWMR is a Multiple-Input-Multiple-Output system. There will be many feedback gains to be tuned, and the wrong value will make the system unstable. Therefore this research proposes an offline autotune method to choose optimal feedback gain that minimizes a fitness function. The fitness function uses Integral Absolute Error (IAE) and Integral Time Absolute Error (ITAE). These works propose to autotune feedback gain for DDWMR Jetbot, which implements a PI control system with six feedback gains. The methods used to tune the feedback gain are Particle Swarm Optimization (PSO) and Bird Swarm Algorithm (BSA). There are four different scenarios to do the autotune. The autotune result performance shows that those two methods can find an optimal gain to make the robot follow four different continuous trajectories without much trajectory deformation. PSO and BSA can do an autotune PI gain with six variables to minimize the Integral Absolute Error (IAE) and Integral Time Absolute Error (ITAE)

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“…PID control method is a linear system method. Because DDWMR robots have many states to control, the feedback gains became too many that it would be challenging to select them [10]- [12].…”

Section: Introductionmentioning

confidence: 99%