Automatic design of control systems for robot manipulators using the bees algorithm

Fahmy, Ashraf; Kalyoncu, Mete; Castellani, Marco

doi:10.1177/0959651811425312

Cited by 27 publications

(24 citation statements)

References 24 publications

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“…In the field of robotics, the Bees Algorithm was used to tune the parameters of fuzzy (membership functions and fuzzy gains) [41] [42] and PID (gains) controllers [43] [44] [45], and to train an ANN to model the inverse kinematics of a robotic manipulator [46] [45]. Jevtic, Andina, and their co-workers used the Bees Algorithm for multi-target search and coverage in an unknown area [47], and for task allocation in a robotic swarm [48].…”

Section: Applications Of the Bees Algorithmmentioning

confidence: 99%

Nature-Inspired Intelligent Optimisation Using the Bees Algorithm

Pham

Castellani

Thi

2014

Transactions on Computational Intelligence XIII

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Abstract.The Bees Algorithm models the foraging behaviour of honey bees in order to solve optimisation problems. The algorithm performs a kind of exploitative neighbourhood search combined with random explorative search. This paper describes the Bees Algorithm, and compares its functioning and performance with those of other state-of-the-art nature-inspired intelligent optimisation methods. Two application cases are presented: the minimisation of a set of well-known benchmark functions, and the training of neural networks to reproduce the inverse kinematics of a robot manipulator. In both cases, the Bees Algorithm proved its effectiveness and speed. Compared with other state-ofthe-art methods, the performance of the Bees Algorithm was very competitive.

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Section: Applications Of the Bees Algorithmmentioning

confidence: 99%

Nature-Inspired Intelligent Optimisation Using the Bees Algorithm

Pham

Castellani

Thi

2014

Transactions on Computational Intelligence XIII

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“…Tuning of the PID controller parameters is always not an easy task, especially for complex non-linear system such as robotic manipulators (Fahmy et. al., 2011).…”

Section: Neuro-fuzzy Pid Controller Tuningmentioning

confidence: 99%

“…In this case of neurofuzzy PID controller, the optimal PID parameters represented in the network weights can be found based on certain performance specifications. If the model of the plant is available, the neuro-fuzzy can be initially tuned offline in simulation using the Bees Algorithm (BA) to produce the best fit for the network parameters before actual physical implementation (Fahmy et. al., 2011).…”

Section: Neuro-fuzzy Pid Controller Tuningmentioning

confidence: 99%

“…The stopping criterion depends on the nature of the problem domain, and can be either the location of a solution of fitness above a pre-defined threshold, or the completion of a predefined number of evolution cycles. In the proposed PID controller, stopping is based on meeting certain transient performance specifications within a certain acceptable range (Fahmy et. al., 2011).…”

Section: Neuro-fuzzy Pid Controller Tuningmentioning

confidence: 99%

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Adaptive Functional-Based Neuro-Fuzzy-PID Incremental Controller Structure

Fahmy

Ghany

2014

IJEEI

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This paper presents an adaptive functional-based Neuro-fuzzy-PID incremental (NFPID) controller structure that can be tuned either offline or online according to required controller performance. First, differential membership functions are used to represent the fuzzy membership functions of the inputoutput space of the three term controller. Second, controller rules are generated based on the discrete proportional, derivative, and integral function for the fuzzy space. Finally, a fully differentiable fuzzy neural network is constructed to represent the developed controller for either offline or online controller parameter adaptation. Two different adaptation methods are used for controller tuning, offline method based on controller transient performance cost function optimization using Bees Algorithm, and online method based on tracking error minimization using back-propagation with momentum algorithm. The proposed control system was tested to show the validity of the controller structure over a fixed PID controller gains to control SCARA type robot arm. 25Function-based FPID controllers (F-FPID) [Tang et. al., 2001]. In this paper, the controller functionally performs fuzzy derivative and fuzzy integral functions, so that no calculations are required outside the FLC. The proposed controller employs only two inputs (present and previous errors), so that the design procedure is simpler. Additionally, most fuzzy logic based PID controllers in literature adapt the triangular membership function shape for simplicity of implementation, despite it's linear nature. In this paper, other types of membership functions, such as Gaussian, and sigmoid membership functions are utilized in the design of the controller to allow tuning for the controller membership functions as well as online adaptation of the controller based on the Back-propagation with momentum (BP) learning algorithm.Since the early 1990s, FNN have attracted a great deal of interest because such systems are more flexible and transparent than either NN or FLS alone. Different types of FNN have been presented in the literature [Shing and Jang, 1993]. For tracking control where the reference and dynamics is always changing, the inverse dynamic control is the best to be utilized, despite very difficult to be implemented mathematically. Consequently, researcher tends to use neural network and neuro-fuzzy systems to avoid complex mathematical formulation [Sinthipsomboon et. al., 2011]. In [Anh and Pham, 2010] a gain-scheduling neural PID controller is utilized with 2-axis robotic structure for varying the parameters of the neural PID controller to include information from the robot dynamics. The FNN types can be identified based on the structure of the FNN, the fuzzy model employed and the learning algorithm adopted [Ahn and Anh, 2009]. On the other hand, the most commonly used and successful approach is the feed-forward and recurrent structure model, while using the BP as the learning algorithm [Yuan et. al., 1992, Nauck andKruse 1993]. On the other hand, accord...

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“…The algorithm has been successfully applied to different optimization problems such as continuous function optimization, artificial neural network training, engineering design, multi-objective optimization, data clustering, and job scheduling [9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Optimization of PID controller based on The Bees Algorithm for one leg of a quadruped robot

Bakırcıoğlu

Şen

Kalyoncu

2016

MATEC Web of Conferences

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Abstract.In this paper, we apply The Bees Algorithm to find optimal PID controller gains to control angular positions of robot leg joints with the minimum position error. In order to present more realistic simulation, system modelled in MATLAB/Simulink environment which is close to experimental set up. Solid model of system, which has two degrees of freedom, drawn by using a CAD software. Required physical specifications of robot leg for MATLAB/Simulink modelling is obtained from this CAD model. Controller of the system is designed in MATLAB/Simulink interface. Simulation results derived to show effectiveness of The Bees Algorithm to find optimal PID controller gains.

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Automatic design of control systems for robot manipulators using the bees algorithm

Cited by 27 publications

References 24 publications

Nature-Inspired Intelligent Optimisation Using the Bees Algorithm

Nature-Inspired Intelligent Optimisation Using the Bees Algorithm

Adaptive Functional-Based Neuro-Fuzzy-PID Incremental Controller Structure

Optimization of PID controller based on The Bees Algorithm for one leg of a quadruped robot

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