Parametric modelling approach using bacterial foraging algorithms for modelling of flexible manipulator systems

Supriyono, Heru; Tokhi, M. O.

doi:10.1016/j.engappai.2012.03.004

Cited by 22 publications

(10 citation statements)

References 28 publications

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“…Supriyono and Tokhi [13] proposed an adaptable chemotactic step size BFO in modeling a single-link flexible manipulator system. Based on an experimental single-link flexible manipulator rig, the input–output data have been collected and employed in establishing three single-input single-output models to characterize the system.…”

Section: Bacterial Foraging Optimization (Bfo) Algorithmmentioning

confidence: 99%

Bacterial foraging-optimized PID control of a two-wheeled machine with a two-directional handling mechanism

Goher

Fadlallah

2017

Robot. Biomim.

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This paper presents the performance of utilizing a bacterial foraging optimization algorithm on a PID control scheme for controlling a five DOF two-wheeled robotic machine with two-directional handling mechanism. The system under investigation provides solutions for industrial robotic applications that require a limited-space working environment. The system nonlinear mathematical model, derived using Lagrangian modeling approach, is simulated in MATLAB/Simulink® environment. Bacterial foraging-optimized PID control with decoupled nature is designed and implemented. Various working scenarios with multiple initial conditions are used to test the robustness and the system performance. Simulation results revealed the effectiveness of the bacterial foraging-optimized PID control method in improving the system performance compared to the PID control scheme.

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Section: Bacterial Foraging Optimization (Bfo) Algorithmmentioning

confidence: 99%

Bacterial foraging-optimized PID control of a two-wheeled machine with a two-directional handling mechanism

Goher

Fadlallah

2017

Robot. Biomim.

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“…Several estimations using parametric approaches have been utilized for modeling the flexible manipulators such as the conventional RL [13], RELS [14] and RLS and least mean square (LMS) [15]. Apart from that, parametric approaches using intelligent methods have attracted many researchers to model the system such as, Genetic Algorithm [16] particle swarm optimization (PSO) [17], Bacteria Foraging Algorithm [18], Differential evolutionary (DE) algorithm [19] and Cuckoo Search Algorithm [20]. A number of research works considered parametric model structure because of its simplicity.…”

Section: Introductionmentioning

confidence: 99%

Intelligent modeling of double link flexible robotic manipulator using artificial neural network

Jamali¹,

Darus²,

Samin³

et al. 2018

J. vibroeng.

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The paper investigates the application of the Artificial Neural Network (ANN) in modeling of double-link flexible robotic manipulator (DLFRM). The system was categorized under multi-input multi-output. In this research, the dynamic models of DLFRM were separated into single-input single-output in the modeling stage. Thus, the characteristics of DLFRM were defined separately in each model and the coupling effect was assumed to be minimized. There are four discrete SISO model of double link flexible manipulator were developed from torque input to the hub angle and from torque input to the end point accelerations of each link. An experimental work was established to collect the input-output data pairs and used in developing the system model. Since the system is highly nonlinear, NARX model was chosen as the model structure because of its simplicity. The nonlinear characteristic of the system was estimated using the ANN whereby multi-layer perceptron (MLP) and ELMAN neural network (ENN) structure were utilized. The implementation of the ANN and its' effectiveness in developing the model of DLFRM was emphasized. The performance of the MLP was compared to ENN based on the validation of the mean-squared error (MSE) and correlation tests of the developed models. The results indicated that the identification of the DLFRM system using the MLP outperformed the ENN with lower mean squared prediction error and unbiased results for all the models. Thus, the MLP provides a good approximation of the DLFRM dynamic model compared to the ENN.

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“…Niu et al [ 39 ], Yan et al [ 57 ] and Xu et al [ 56 ] varied the step size within a user-defined range using combination of index and total number of iterations. Supriyono and Tokhi [ 49 ] developed various versions of BFA based on linear and nonlinear mathematical formulations to establish relationship between bacteria step size and their current fitness value. This relationship enables bacteria to have different step sizes in similar iteration as well as through the whole operation.…”

Section: Introductionmentioning

confidence: 99%

Hybrid spiral-dynamic bacteria-chemotaxis algorithm with application to control two-wheeled machines

et al. 2017

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This paper presents the implementation of the hybrid spiral-dynamic bacteria-chemotaxis (HSDBC) approach to control two different configurations of a two-wheeled vehicle. The HSDBC is a combination of bacterial chemotaxis used in bacterial forging algorithm (BFA) and the spiral-dynamic algorithm (SDA). BFA provides a good exploration strategy due to the chemotaxis approach. However, it endures an oscillation problem near the end of the search process when using a large step size. Conversely; for a small step size, it affords better exploitation and accuracy with slower convergence. SDA provides better stability when approaching an optimum point and has faster convergence speed. This may cause the search agents to get trapped into local optima which results in low accurate solution. HSDBC exploits the chemotactic strategy of BFA and fitness accuracy and convergence speed of SDA so as to overcome the problems associated with both the SDA and BFA algorithms alone. The HSDBC thus developed is evaluated in optimizing the performance and energy consumption of two highly nonlinear platforms, namely single and double inverted pendulum-like vehicles with an extended rod. Comparative results with BFA and SDA show that the proposed algorithm is able to result in better performance of the highly nonlinear systems.

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Parametric modelling approach using bacterial foraging algorithms for modelling of flexible manipulator systems

Cited by 22 publications

References 28 publications

Bacterial foraging-optimized PID control of a two-wheeled machine with a two-directional handling mechanism

Bacterial foraging-optimized PID control of a two-wheeled machine with a two-directional handling mechanism

Intelligent modeling of double link flexible robotic manipulator using artificial neural network

Hybrid spiral-dynamic bacteria-chemotaxis algorithm with application to control two-wheeled machines

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