Artificial Bee Colony Optimization Algorithm Incorporated With Fuzzy Theory for Real-Time Machine Learning Control of Articulated Robotic Manipulators

Huang, Hsu‐Chih; Chuang, Chun-Chia

doi:10.1109/access.2020.3032715

Cited by 18 publications

(4 citation statements)

References 39 publications

(57 reference statements)

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“…A FOPID controller is an expansion of a traditional PID controller by including a differentiator of order  as well as an integrator of order λ named PIλDµ control. The generalized transfer function of FOPID control law by exploiting the fractional calculus is explained below [7]:…”

Section: Flight Systems Robotics and Vehicle Dynamicsmentioning

confidence: 99%

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Real-time Machine Learning Control for Robotic Manipulator by LNB: Lion Naïve Bayes Algorithm

Tanveer¹,

Roy²

2021

JCMPS

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A real-time Machine Learning Control (MLC) of articulated robotic manipulators is presented in this work by exploiting the Lion Optimization Algorithm (LOA) which is combined with the Naïve Bayes (LNB). Here, the proposed model is used for the real-time MLC of robotic manipulators by exploiting the "Fractional Order Proportional-Integral-Derivative (FOPID)" control scheme. Using the proposed LNB model MLC control gain parameters are tuned. By considering the dynamics of the actuator, to convene significant timing constraints, a "Real-Time Operating System (RTOS)" on a microprocessor collaborates with the LNB-MLC. At last, the Mechatronic design and investigational setup of a 6-degree of freedom (DOF) expressed robotic manipulator are modeled. The performance analysis is presented to show the advantage of the adopted models. While compared with the existing control models, the adopted optimization model has practice and theoretical consequences regarding online parameter tuning, real-time ability, and convergent behavior. In both industry and academia, the adopted MLC approaches are appropriate to design the real-time modern controllers.

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Section: Flight Systems Robotics and Vehicle Dynamicsmentioning

confidence: 99%

“…The IK is exploited by means of reverse coordinates techniques with the forward kinematic formulations. After the analysis of kinematics, the motion planning as well as dynamic plant model is used to model a Lion Naive Bayes robot arm MLC controller with accomplishment [7].…”

Section: Robotic Manipulators For Real-time Mlcmentioning

confidence: 99%

Real-time Machine Learning Control for Robotic Manipulator by LNB: Lion Naïve Bayes Algorithm

Tanveer¹,

Roy²

2021

JCMPS

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“…Batiha et al [10] implemented two optimization algorithms, particle swarm optimization (PSO) and bacteria foraging optimization (BFO) algorithms, for the purpose of tuning the fractional-order PIDcontroller. Huang and Chuang [11] proposed an artificial bee colony optimization (ABC) algorithm incorporating fuzzy theory to optimize the PID controller by introducing fractional-order proportional-integral-derivative (FOPID) control strategy. Panoeiro et al [12] optimized PID controller parameters by bionic optimization technique.…”

Section: Introductionmentioning

confidence: 99%

PID Controller Parameter Optimized by Reformative Artificial Bee Colony Algorithm

Liu

Cui

et al. 2022

Journal of Mathematics

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The PID parameters determine the PID controller performance. A reformative artificial bee colony (RABC) algorithm is proposed for the PID parameter optimization problem. The algorithm balances the exploitation capability and exploration capability of the ABC algorithm by introducing a global optimal solution and improving the food source probability. The proposed algorithm is validated by simulation with six benchmark functions, and the results show that the RABC algorithm has higher search accuracy and faster search speed than other variants of the artificial bee colony algorithm. The RABC algorithm-optimized PID controller has better control with minimum overshoot and fast response, as verified by comparison with PSO-PID, DE-PID, and GA-PID methods in three typical systems.

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“…On the other hand, neural and fuzzy logic system (FLS) -based control approaches have been extensively applied for uncertain applications [14][15][16]. For instance, in [17] an FLS with optimal secondary fuzzy sets is developed for the frequency control of uncertain microgrids.…”

Section: Introduction 1literature Reviewmentioning

confidence: 99%

A Hybrid Predictive Type-3 Fuzzy Control for Time-Delay Multi-Agent Systems

et al. 2021

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In this paper, the consensus problem is addressed for multi-agent systems. The dynamics of each agent contain unknown uncertain/nonlinear terms and unknown time delays. A type-3 fuzzy logic system is developed to tackle the effect of unknown dynamics and design a hybrid controller. The policy scheme involves two control signals for the stabilization of the approximation and consensus error of each agent dynamic. To this end, based on the concept of the model predictive control approach, the constrained control laws are designed and updated at each time step. The simulations results portray the error signals. Feasibility, appropriate convergence, and proper transient response are the main merits of the suggested method.

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Artificial Bee Colony Optimization Algorithm Incorporated With Fuzzy Theory for Real-Time Machine Learning Control of Articulated Robotic Manipulators

Cited by 18 publications

References 39 publications

Real-time Machine Learning Control for Robotic Manipulator by LNB: Lion Naïve Bayes Algorithm

Real-time Machine Learning Control for Robotic Manipulator by LNB: Lion Naïve Bayes Algorithm

PID Controller Parameter Optimized by Reformative Artificial Bee Colony Algorithm

A Hybrid Predictive Type-3 Fuzzy Control for Time-Delay Multi-Agent Systems

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