Augmented Stable Fuzzy Control for Flexible Robotic Arm Using LMI Approach and Neuro-Fuzzy State Space Modeling

Chatterjee, Amitava; Chatterjee, Ranajit; Matsuno, Fumitoshi; Endo, Takahiro

doi:10.1109/tie.2007.896439

Cited by 115 publications

(44 citation statements)

References 23 publications

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“…Table 1 is robot parameters for experiments. By analyzing the parameters of the robot, the transformation matrix can be calculated by formula (1). By using the D-H method to get the matrix transformation, robot armwith 6 degrees of freedom rotation angle can be calculated, and the robot arm trajectory can be calculated from the angle of rotation, which can achieve the desired objectives.…”

Section: Model Of Robot Arm Control System Based On Adaptive Fuzzy Comentioning

confidence: 99%

“…Most robot tasks require that robot arms interact with the different objects in the work environment because of savings, quality, safety, and production [1][2][3][4][5][6]. Several different control methods (i.e., neural network based adaptive control, robust based adaptive control, model reference based adaptive control, and classical PID control) are used to analyze the control performance.…”

Section: Introductionmentioning

confidence: 99%

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Research on Adaptive Control System of Robot Arm with Six Degrees of Freedom

Yu¹,

Li²

2016

Proceedings of the 2016 International Conference on Sensor Network and Computer Engineering

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Abstract. This paper aims to exploit the adaptive control robot arm with six degrees of freedom, an adaptive fuzzy control system was established. a fuzzy controller, the robot arm trajectory planning and the D-H algorithm were studied based on the fuzzy controller. Firstly, fuzzy controller and control rules are designed according to a series of characteristics of fuzzy control method. Then, based on the analysis of robot arm parameters, robot arm trajectory plan by the D-H algorithm. Finally, Hardware and software design and experiment were complete. Experimental results show that the D-H algorithm can be used to establish the model of the robot arm, and the MATLAB can be used to understand the data processing. The stability of adaptive control of robot arm with 6 degrees of freedom is good, the expansibility is strong, easy to control and so on.

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Section: Model Of Robot Arm Control System Based On Adaptive Fuzzy Comentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Adaptive Control System of Robot Arm with Six Degrees of Freedom

Yu¹,

Li²

2016

Proceedings of the 2016 International Conference on Sensor Network and Computer Engineering

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“…Chen Zhifei presented the adaptive neuro-complex fuzzy inferential system (ANCFIS) [3], providing a good method for the implementation of complex fuzzy rules; the authors have presented the architecture and learning algorithm for ANCFIS. Amitava Chatterjee proposed a control strategy of stable state-feedback fuzzy controller used for flexible robotic arms [1], in which the controller was designed on the basis of a neuro-fuzzy state space model; this strategy has solved the stability condition problems and has been successfully implemented on a real robotic arm. Zhang Wen Ling proposed a fuzzy control system using an iterative feedback tuning algorithm characterized by setting the step size in order to guarantee the stability of the fuzzy control system [14].…”

Section: Introductionmentioning

confidence: 99%

Variable universe fuzzy control for excitation system of HTS machine

Zhang

Cao

2015

IFS

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Abstract.The generator is an important component of the power system and, as a prerequisite for the normal work of the generator; one of the preconditions of excitation of a system running well will directly affect the operation of the motor characteristics and heavily influence the normal operation of the power system. In the excitation system, the control portion of the excitation system is the key to whether a system can effectively resist all kinds of emergencies. Regarding the excitation system of a high temperature superconducting machine, this paper proposes the design of an excitation system control strategy. Combining fuzzy control and conventional PID control strategy, the proposed composition has the advantages of an excitation control strategy, which can achieve a precise control purpose, and provides good adaptive ability and robustness. And on this basis, the proposed composition seeks to combine variable universe fuzzy control, to further improve the control precision of the system. The fuzzy PID control system simulation model and the variable universe fuzzy PID control are investigated. Results are compared and indicate that the variable universe fuzzy PID control demonstrates better dynamic and static performance.

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“…Fuzzy control has the inherent advantage of combining heuristic logic with analytical functions, thereby seamlessly integrating qualitative knowledge with highly nonlinear systems and providing superior control performance on many occasions [13][14][15][16][17][41][42][43][44][45][46][47]. Over the years, fuzzy control has evolved as an extremely popular and viable control alternative for the purpose of modeling and control in a variety of applications, ranging from robotics and mechanical systems, to electrical drives, in process control of highly non-linear chemical processes and in other fields of engineering [30][31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%