System Modeling and Identification the Two-Link Pneumatic Artificial Muscle (PAM) Manipulator Optimized with Genetic Algorithms

Ahn, Kyoung Kwan; Ánh, Hồ Phạm Huy

doi:10.1109/sice.2006.314782

Cited by 27 publications

(21 citation statements)

References 15 publications

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“…Furthermore, this disadvantage will also be duplicated because of the complexity of such NNARMAX and NNOE models as well and because of the time cost of iteration calculation, which is considerable too. Finally, all three autoregressive neural networks models, namely, NNARX-NNARMAX and NNOE demonstrate their superb performance in comparison with conventional MLPNN model and linear ARX model as well (see [6]). …”

Section: Results Of Modeling and Identification Of The Pam Manipulatomentioning

confidence: 93%

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Comparative study of modeling and identification of the pneumatic artificial muscle (PAM) manipulator using recurrent neural networks

Ahn

Ánh

2008

J Mech Sci Technol

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The paper deals with the PAM manipulator modeling and identification based on autoregressive recurrent neural networks. For the first time, the most powerful types of neural-network-based nonlinear autoregressive models, namely, NNARMAX, NNOE and NNARX models, will be applied comparatively to the PAM manipulator identification. Furthermore, the evaluation of different nonlinear neural network auto-regressive models of the PAM manipulator with different number of neurons in hidden layer is completely discussed. On this basis, the merits of each identified model of the highly nonlinear PAM manipulator have been analyzed and compared. The results show that the nonlinear NNARX model yields better performance and higher accuracy than the other nonlinear NNARMAX and NNOE model schemes. These results can be applied to model and identify not only the PAM manipulator but also to control other nonlinear and time-varying industrial systems.

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Section: Results Of Modeling and Identification Of The Pam Manipulatomentioning

confidence: 93%

“…In [5], the simulation considers PAM be modeled individually in both bicep and tricep positions. Recently, in [6], the authors applied a modified genetic algorithm for optimizing parameters of linear ARX model of the PAM manipulator. In [7], the authors successfully identified the PAM manipulator based on nonlinear neural NNARX model.…”

Section: Introductionmentioning

confidence: 99%

Comparative study of modeling and identification of the pneumatic artificial muscle (PAM) manipulator using recurrent neural networks

Ahn

Ánh

2008

J Mech Sci Technol

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“…Unfortunately, these fuzzy models are clumsy and have only been tested in simulation studies. (Ahn and Anh, 2006) applied a modified genetic algorithm (MGA) for optimizing the parameters of a linear ARX model of the PAM manipulator which can be modified online with an adaptive self-tuning control algorithm, and then (Ahn and Anh, 2007b) successfully applied recurrent neural networks (RNN) for optimizing the parameters of neural NARX model of the PAM robot arm. Recently, we (Ahn and Anh, 2009) successfully applied the modified genetic algorithm (MGA) for optimizing the parameters of the NARX fuzzy model of the PAM robot arm.…”

Section: Introductionmentioning

confidence: 99%

A New Approach of the Online Tuning Gain Scheduling Nonlinear PID Controller Using Neural Network

Ánh¹,

Nam²

2011

PID Control, Implementation and Tuning

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This chapter presents the design, development and implementation of a novel proposed online-tuning Gain Scheduling Dynamic Neural PID (DNN-PID) Controller using neural network suitable for real-time manipulator control applications. The unique feature of the novel DNN-PID controller is that it has highly simple and dynamic self-organizing structure, fast online-tuning speed, good generalization and flexibility in online-updating. The proposed adaptive algorithm focuses on fast and efficiently optimizing Gain Scheduling and PID weighting parameters of Neural MLPNN model used in DNN-PID controller. This approach is employed to implement the DNN-PID controller with a view of controlling the joint angle position of the highly nonlinear pneumatic artificial muscle (PAM) manipulator in real-time through Real-Time Windows Target run in MATLAB SIMULINK ® environment. The performance of this novel proposed controller was found to be outperforming in comparison with conventional PID controller. These results can be applied to control other highly nonlinear SISO and MIMO systems.

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“…Many potential applications involve some type of exo-skeletal or link segment configuration that attaches to existing anatomical body-segments [3][4][5][6]. Research into the control and the physical and modeling properties of PAM has been undertaken at the INSA (Toulouse, France) [7], the Bio-Robotics Lab at the University of Washington, Seattle, [8], Human Sensory Feedback (HSF) Laboratory at Wright Patterson Air Force Base [9], and Fluid Power and Machine Intelligence Laboratory (FPMI) at Ulsan University [10][11][12] and so on. This paper addresses the modeling, identification and control of a PAM manipulator actuated by a group of antagonistic PAM pair.…”

Section: Introductionmentioning

confidence: 99%

“…In [25], H inf method is used for PAM control. Recently, in [11], it applied thoroughly Modified GA (MGA) for optimizing parameters of pseudo-linear ARX model of the PAM manipulator. In [12], authors identified successfully PAM manipulator based on nonlinear fuzzy NARX model.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Adaptive Self-Tuning MVC Control of PAM Manipulator Using Online Observer Optimized with Modified Genetic Algorithm

Huy¹,

Thanh²

2011

ENG

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In this paper, the application of modified genetic algorithms (MGA) in the optimization of the ARX Modelbased observer of the Pneumatic Artificial Muscle (PAM) manipulator is investigated. The new MGA algorithm is proposed from the genetic algorithm with important additional strategies, and consequently yields a faster convergence and a more accurate search. Firstly, MGA-based identification method is used to identify the parameters of the nonlinear PAM manipulator described by an ARX model in the presence of white noise and this result will be validated by MGA and compared with the simple genetic algorithm (GA) and LMS (Least mean-squares) method. Secondly, the intrinsic features of the hysteresis as well as other nonlinear disturbances existing intuitively in the PAM system are estimated online by a Modified Recursive Least Square (MRLS) method in identification experiment. Finally, a highly efficient self-tuning control algorithm Minimum Variance Control (MVC) is taken for tracking the joint angle position trajectory of this PAM manipulator. Experiment results are included to demonstrate the excellent performance of the MGA algorithm in the NARX model-based MVC control system of the PAM system. These results can be applied to model, identify and control other highly nonlinear systems as well.

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System Modeling and Identification the Two-Link Pneumatic Artificial Muscle (PAM) Manipulator Optimized with Genetic Algorithms

Cited by 27 publications

References 15 publications

Comparative study of modeling and identification of the pneumatic artificial muscle (PAM) manipulator using recurrent neural networks

Comparative study of modeling and identification of the pneumatic artificial muscle (PAM) manipulator using recurrent neural networks

A New Approach of the Online Tuning Gain Scheduling Nonlinear PID Controller Using Neural Network

Modeling and Adaptive Self-Tuning MVC Control of PAM Manipulator Using Online Observer Optimized with Modified Genetic Algorithm

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