Design a Fuzzy PID Controller for Trajectory Tracking of Mobile Robot

Mohamed, Mona; Abbas, Mohamed

doi:10.30684/etj.36.1a.15

Cited by 6 publications

(7 citation statements)

References 10 publications

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“…Fuzzy logic and is a type of Artificial Intelligence. Its goal is to implant human intelligence into the system, allowing it to think intelligently such a human being [20]. In AQM routers, intelligent congestion avoidance control is provided by a fuzzy PID controller that is able to handle a wider range of operational situations than traditional controllers.…”

Section: Fuzzy Like-pid Controllermentioning

confidence: 99%

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Adaptive neuro-fuzzy controller trained by genetic-particle swarm for active queue management in internet congestion

Berbek

Oglah

2022

IJEECS

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Routers are vital during network congestion. All routers have input and output packet buffers. V<span lang="EN-US">Various congestion control strategies have been suggested. Some controller-based proportional-integral derivative (PIDs) have recently been offered as active queue management (AQM) solutions to alleviate the deterioration of transmission control protocol (TCP) congestion management system performance. However, the time delay is large, the data retention decreases, and oscillation occurs, suggesting that the present PID-controller is unable to fulfill quality of service (QoS) criteria. Some research is developed on new control technologies such as neural networks and fuzzy logic. This paper proposes the adaptive neuro-fuzzy inference system (ANFIS) like PID controller for AQM. This model employs genetic algorithms (GAs) and particle swarm optimization (PSO) to learn and optimize all variables for ANFIS like PID controller. Simulations were used to investigate the effects of using fuzzy like PID based on single sign-on (SSO), and (ANFIS like PI, ANFIS like PID with GA-PSO) controllers on the length of the queue for an AQM router, respectively. Then we compared the findings to see which approach should be utilized to manage the queue length for AQM routers. In simulations, ANFIS like PID has superior stability, convergence, resilience, loss ratio, goodput, lowest rising time, overshoot, and settling time.</span>

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Section: Fuzzy Like-pid Controllermentioning

confidence: 99%

“…Layer 4 (de-fuzzification): In each node of this layer, weighted consequent values of rules are calculated as shown in (20).…”

Section: Anfis-structurementioning

confidence: 99%

Adaptive neuro-fuzzy controller trained by genetic-particle swarm for active queue management in internet congestion

Berbek

Oglah

2022

IJEECS

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“…In order to generate this path, the following equation were applied [70]: where the back-stepping parameters are selected as: kx=10, ky = 80 and kθ = 15.…”

Section: Case Of Lemniscatesmentioning

confidence: 99%

Optimal Design and Performance Comparison of a Combined ANFIS-PID with Back Stepping Technique, Using Various Meta-Heuristic Algorithms to Solve Wheeled Mobile Robot Trajectory Tracking Problem

Euldji¹,

Batel²,

Rebhi³

et al. 2022

JESA

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The present investigation suggests a novel control technique that merge the advantage of the adaptive Neuro-Fuzzy inference system (ANFIS) with the proportional integral derivative (PID) controller, abbreviated as (ANFIS-PID), for dealing with the dynamics of the of wheeled mobile robot (WMR). A comparative study of various meta-heuristic optimization algorithms is made. The results revealed the highest efficiency of the suggested ANFIS-PID technique compared to seven designed PID controllers, in terms of settling and rise time, overshoot, and the integral square error (ISE) as a cost function. Various cases, study (circular path, diamond path, zigzag path) have highlighted the over performance of mentioned controller. Moreover, this hybrid technique is fused with backstepping approach for the kinematic control. A lemniscate and a square trajectory were performed to clarify the capability of the mentioned controller.

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“…The proposed hybrid fuzzy gain scheduling algorithm is developed based on the following works [ 7 , 22 – 24 ].…”

Section: Hybrid Fuzzy-pid Gain Scheduling Algorithm Designmentioning

confidence: 99%

“…The rule base look-up tables for calculating and relating the PID gain values are listed as Tables 1 – 3 . To develop the knowledge from the rule base and built inference engine, the applied rule inference method is the Mamdani method, and the used defuzzification approach was the centroid method [ 7 , 24 ]. To express the knowledge levels and represent linguistic variable, triangular membership functions are selected.…”

Section: Hybrid Fuzzy-pid Gain Scheduling Algorithm Designmentioning

confidence: 99%

Artificial Fuzzy‐PID Gain Scheduling Algorithm Design for Motion Control in Differential Drive Mobile Robotic Platforms

Allagui

Salem

Aljuaid

2021

Computational Intelligence and Neuroscience

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Mobile robots are promising devices which are dedicated to human comfort in all areas. However, the control algorithm of the wheels of mobile robot is entirely challenging due to the nonlinearity. Recently, the classical PID (proportional-integral-derivative) controllers are frequently used in robotics for their high accuracy and the smooth determination of their parameters. A robust approach called fuzzy control which is based on the conversion of linguistic inference sets in a suitable control value is a widely used method in industrial system control in our days. A new challenging method to solve the problem of intelligent navigation of nonholonomic mobile robot is suggested. In this work, the presented methodology is based on three hybrid fuzzy logic PID controllers which are adapted to guarantee target achievement and trajectory tracking. A fuzzy-PID control algorithm is designed with 2 inputs and 3 outputs. By the information given by the system response, error and error derivate can be used to extract and adopt the PID controller parameters: proportional, integral, and derivative gains. Besides, a tuning value A is introduced to improve the resulted response in terms of speeding up and reducing error, overshoot, and oscillation, as well as reducing ISE and IAE values. A modelization of a differential drive mobile robot is presented. The developed algorithm is tested and implemented to this mobile robot model via Simulink/MATLAB.

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Design a Fuzzy PID Controller for Trajectory Tracking of Mobile Robot

Cited by 6 publications

References 10 publications

Adaptive neuro-fuzzy controller trained by genetic-particle swarm for active queue management in internet congestion

Adaptive neuro-fuzzy controller trained by genetic-particle swarm for active queue management in internet congestion

Optimal Design and Performance Comparison of a Combined ANFIS-PID with Back Stepping Technique, Using Various Meta-Heuristic Algorithms to Solve Wheeled Mobile Robot Trajectory Tracking Problem

Artificial Fuzzy‐PID Gain Scheduling Algorithm Design for Motion Control in Differential Drive Mobile Robotic Platforms

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