Sliding mode fuzzy control

Palm, Rainer

doi:10.1109/fuzzy.1992.258681

Cited by 285 publications

(114 citation statements)

References 6 publications

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“…Apart from numerus positive points to reduce the chattering, this method increases the error and reduce the robustness. To reduce the chattering and error as well, Palm has been developed intelligent-based boundary layer method [7]. However, this method has been solved the challenge of error and chattering as well, but the problem of robustness and tuning the fuzzy coefficients were still remaining.…”

Section: Introductionmentioning

confidence: 99%

“…A sliding mode controller is the next candidate to solve the challenge of the coupling effect concerning system performance. Apart from several advantages, this method contains the problem of highfrequency system oscillation (chattering) and limited robustness [3][4][5][6][7]. Adaptive techniques have been used to solve the challenge of robustness [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…To solve the challenge of chattering, two methods introduced by researchers: functional-based method and intelligent-based method [6][7]. A functional-based linear boundary layer algorithm, has been developed by Slotine [6].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Control of an Uncertain Robot Manipulator Using an Observation-based Modified Fuzzy Sliding Mode Controller

TayebiHaghighi¹,

Piltan²,

Kim³

2018

IJISA

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Abstract-The main contribution of this paper is the design of a robust model reference fuzzy sliding mode observation technique to control multi-input, multi-output (MIMO) nonlinear uncertain dynamical robot manipulators. A fuzzy sliding mode controller was used in this study to control the robot manipulator in the presence of uncertainty and disturbance. To address the challenges of robustness, chattering phenomenon, and error convergence under uncertain conditions, the proposed sliding mode observer was applied to the fuzzy sliding mode controller. This theory was applied to a sixdegrees-of-freedom (DOF) PUMA robot manipulator to verify the power of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Control of an Uncertain Robot Manipulator Using an Observation-based Modified Fuzzy Sliding Mode Controller

TayebiHaghighi¹,

Piltan²,

Kim³

2018

IJISA

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“…However, due to the linguistic expression of fuzzy controller, it is difficult to guarantee the stability of control systems. Nevertheless, by designing sliding mode type fuzzy logic controller, the performance and stability can be ensured and meanwhile the number of fuzzy rules can be reduced [15]. The proposed sliding mode fuzzy controller (SMFC) has the advantages of not requiring knowledge of the cylinder and valve dynamics, and ensures precise and robust performance in the presence of disturbances through adaptation of switching gains and parameters.…”

Section: Introductionmentioning

confidence: 99%

A Sliding Mode Fuzzy Force Tracking Controller for Pneumatic Cylinders

Xu¹,

Hayakawa²,

Pandian³

2005

Proceedings of the JFPS International Symposium on Fluid Power

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The problem of control of force exerted by pneumatic cylinders on the environment is of interest in industrial automation and systems such as legged robots. In this paper, a sliding mode fuzzy force control algorithm is proposed for pneumatic cylinders. The environment is assumed to be "soft" or compliant and modeled as a spring. The advantage of the new controller is that being based on fuzzy logic, knowledge of the cylinder dynamics or disturbances such as friction is not necessary. The control law implementation is based explicitly on contact force measurements. The proposed algorithm is simple, robust, and easy to implement. Results of experimental implementation are presented to illustrate the practical effectiveness of the new method.

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“…Although they are simple and easy to understand, the stability and performance are not guaranteed. To prove the stability, researchers had derived different conditions based on sliding mode control technique [3] and adaptive technique [4]. One significant work [l] proposed the use of a fuzzy model.…”

Section: Introductionmentioning

confidence: 99%

Lyapunov function based design of robust fuzzy controllers for uncertain nonlinear systems: distinct Lyapunov functions

Leung¹,

Lam

Tam

1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98C

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This paper presents the stability and robustness analyses of an uncertain fuzzy control system which is formed by an uncertain fuzzy plant model and a fuzzy controller. The fuzzy plant model with parameter uncertainties describes exactly the behavior of an uncertain nonlinear plant. Three design approaches are introduced to close the feedback loop. Based on the Lyapunov's stabili9 theory, new stability criteria and robust areas are to be derived without resorting to a common Lyapunov function. An application example on stabilizing an uncertain nonlinear mass-spring-damper system will be given to illustrate the merit.

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Sliding mode fuzzy control

Cited by 285 publications

References 6 publications

Control of an Uncertain Robot Manipulator Using an Observation-based Modified Fuzzy Sliding Mode Controller

Control of an Uncertain Robot Manipulator Using an Observation-based Modified Fuzzy Sliding Mode Controller

A Sliding Mode Fuzzy Force Tracking Controller for Pneumatic Cylinders

Lyapunov function based design of robust fuzzy controllers for uncertain nonlinear systems: distinct Lyapunov functions

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