Robust Path Tracking of Mobile Robot Using Fractional Order PID Controller

Ammar, Hossam Hassan; Azar, Ahmad Taher

doi:10.1007/978-3-030-14118-9_37

Cited by 36 publications

(11 citation statements)

References 9 publications

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“…The following is an example of the BOA optimization process. a) Initialization: the balls' agents in space are originally distributed as (10):…”

Section: The Boa Techniquementioning

confidence: 99%

“…Selection of ACO as the performance comparison because ACO had better results in earlier optimization efforts. The initialization process in the BOA-based meta-heuristic technique is initiated randomly because of the population sample selection as in (10), and therefore cannot achieve accurate beginning procedure. Because meta-heuristic algorithms are overly sensitive to initialization states and good beginning states will improve the performance of these algorithms.…”

Section: The C-bo Algorithmmentioning

confidence: 99%

“…This part is replacing the initialization of BOA in steps a) to h) presented in (10) to (17). where rank is a vector of random integers in the range of [0, 1].…”

Section: The C-bo Algorithmmentioning

confidence: 99%

“…Khai et al [9] presented a fuzzy inference system for tuning the parameters of the kinematic controller of differential-drive mobile robot (DDMR) without considering a dynamic model. Ammar and Azar [10] proposed a fractional-order PID (FOPID) for path tracking control of mobile robots. Azizi et al [11] designed a nonlinear model predictive control (NMPC) strategy based on the derived mathematical model for motion planning and control of an omnidirectional mobile robot in dynamic environments.…”

Section: Introductionmentioning

confidence: 99%

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Path tracking control of differential drive mobile robot based on chaotic-billiards optimization algorithm

Mohammed

Dessouki²,

Elnaghi³

2023

IJECE

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<span lang="EN-US">Mobile robots are typically depending only on robot kinematics control. However, when high-speed motions and highly loaded transfer are considered, it is necessary to analyze dynamics of the robot to limit tracking error. The goal of this paper is to present a new algorithm, chaotic-billiards optimizer (C-BO) to optimize internal controller parameters of a differential-drive mobile robot (DDMR)-based dynamic model. The C-BO algorithm is notable for its ease of implementation, minimal number of design parameters, high convergence speed, and low computing burden. In addition, a comparison between the performance of C-BO and ant colony optimization (ACO) to determine the optimum controller coefficient that provides superior performance and convergence of the path tracking. The ISE criterion is selected as a fitness function in a simulation-based optimization strategy. For the point of accuracy, the velocity-based dynamic compensation controller was successfully integrated with the motion controller proposed in this study for the robot's kinematics. Control structure of the model was tested using MATLAB/Simulink. The results demonstrate that the suggested C-BO, with steady state error performance of 0.6 percent compared to ACO's 0.8 percent, is the optimum alternative for parameter optimizing the controller for precise path tracking. Also, it offers advantages of quick response, high tracking precision, and outstanding anti-interference capability.</span>

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“…The following is an example of the BOA optimization process. a) Initialization: the balls' agents in space are originally distributed as (10):…”

Section: The Boa Techniquementioning

confidence: 99%

Section: The C-bo Algorithmmentioning

confidence: 99%

“…This part is replacing the initialization of BOA in steps a) to h) presented in (10) to (17). where rank is a vector of random integers in the range of [0, 1].…”

Section: The C-bo Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Path tracking control of differential drive mobile robot based on chaotic-billiards optimization algorithm

Mohammed

Dessouki²,

Elnaghi³

2023

IJECE

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“…Proportional-integral-derivative (PID) controllers were implemented in the process control and other applications [31][32][33][34][35]. For these reasons, as explained in the previous section, the H-infinity PID loop shaping controller system, given in Figure 4, is obtained by elliptic functions.…”

Section: H ∞ Pid Loop Shaping Controller Synthesis By Elliptic Functionsmentioning

confidence: 99%

Robust H∞ Loop Shaping Controller Synthesis for SISO Systems by Complex Modular Functions

Azar

Serrano

Kamal

2021

MCA

Self Cite

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In this paper, a loop shaping controller design methodology for single input and a single output (SISO) system is proposed. The theoretical background for this approach is based on complex elliptic functions which allow a flexible design of a SISO controller considering that elliptic functions have a double periodicity. The gain and phase margins of the closed-loop system can be selected appropriately with this new loop shaping design procedure. The loop shaping design methodology consists of implementing suitable filters to obtain a desired frequency response of the closed-loop system by selecting appropriate poles and zeros by the Abel theorem that are fundamental in the theory of the elliptic functions. The elliptic function properties are implemented to facilitate the loop shaping controller design along with their fundamental background and contributions from the complex analysis that are very useful in the automatic control field. Finally, apart from the filter design, a PID controller loop shaping synthesis is proposed implementing a similar design procedure as the first part of this study.

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Fuzzy PID Controller for Automatic Generation Control of Interconnected Power System Tuned by Glow-Worm Swarm Optimization

Pilla¹,

Botcha²,

Gorripotu³

et al. 2019

Learning and Analytics in Intelligent Systems

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Robust Path Tracking of Mobile Robot Using Fractional Order PID Controller

Cited by 36 publications

References 9 publications

Path tracking control of differential drive mobile robot based on chaotic-billiards optimization algorithm

Path tracking control of differential drive mobile robot based on chaotic-billiards optimization algorithm

Robust H∞ Loop Shaping Controller Synthesis for SISO Systems by Complex Modular Functions

Fuzzy PID Controller for Automatic Generation Control of Interconnected Power System Tuned by Glow-Worm Swarm Optimization

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