| 1. |
- Emami, Reza, et al.
(författare)
-
A robust model-based fuzzy-logic controller for robot manipulators
- 1998
-
Ingår i: 1998 IEEE International Conference on Robotics and Automation, Leuven, Belgium, 16-20 May 1998. - Piscataway, NJ : IEEE Communications Society. ; , s. 2500-2505
-
Konferensbidrag (refereegranskat)abstract
- This paper represents a fuzzy-logic control structure, containing the fuzzy-logic model of the dynamic system and fuzzy control rules that ensure the stability and robust performance. The robust fuzzy rules are designed based on a generalized formulation of sliding mode control for a class of nonlinear multi-input multi-output systems. The proposed fuzzy-logic control scheme was applied to the trajectory control of a four degree-of-freedom robot, and compared with the high-gain PID controllers. A superior tracking performance was achieved.
|
|
| 2. |
- Emami, Reza, et al.
(författare)
-
Development of a systematic methodology of fuzzy logic modeling
- 1998
-
Ingår i: IEEE transactions on fuzzy systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 1063-6706 .- 1941-0034. ; 6:3, s. 346-361
-
Tidskriftsartikel (refereegranskat)abstract
- This paper proposs a systematic methodology of fuzzy logic modeling as a generic tool for modeling of complex systems. The methodology conveys three distinct features: 1) a unified parameterized reasoning formulation; 2) an improved fuzzy clustering algorithm; and 3) an efficient strategy of selecting significant system inputs and their membership functions. The reasoning mechanism introduces four parameters whose variation provides a continuous range of inference operation. As a result, we are no longer restricted to standard extremes in any step of reasoning. Unlike traditional approach of selecting the inference mechanism a priori, the fuzzy model itself can then adjust the reasoning process by optimizing the inference parameters based on input-output data. The fuzzy rules are generated through fuzzy c-means (FCM) clustering algorithm. Major bottle-necks of the algorithm are addressed and analytical solutions are suggested. Furthermore, we also address the classification process in fuzzy modelng to extend the derived fuzzy partition to the entire output space. This issue remains unattained in the current literature. In order to select suitable input variables among a finite number of candidates (unlike traditional approaches) we suggest a new strategy through which dominant input parameters are assigned in one step and no iteration process is required. Furthermore, a clustering technique called fuzzy line clustering is introduced to assign the input membership functions. In order to evaluate the proposed methodology, two examples - a nonlinear function and a gas furnace dynamic procedure - are investigated in detail. The significant improvement of the model is concluded compared to other fuzzy modeling approaches. © 1998 IEEE.
|
|
| 3. |
- Emami, Reza, et al.
(författare)
-
Fuzzy-logic dynamics modeling of robot manipulators
- 1998
-
Ingår i: 1998 IEEE International Conference on Robotics and Automation, Leuven, Belgium, 16-20 May 1998. - Piscataway, NJ : IEEE Communications Society. ; , s. 2512-2517
-
Konferensbidrag (refereegranskat)abstract
- A systematic approach of fuzzy-logic modeling from the system input-output data is applied to modeling dynamics behavior of a four degree-of-freedom robot manipulator. The output of the fuzzy model is compared with the experimental data and with that of an analytical simulation. The simplicity of the fuzzy model and its improved accuracy is significant.
|
|
| 4. |
- Goldenberg, Andrew A., et al.
(författare)
-
Kinematics and Dynamics of Robot Manipulators
- 1999. - 2
-
Ingår i: Handbook of Industrial Robotics. - Hoboken : John Wiley & Sons Ltd. - 9780471177838 - 9780470172506 ; , s. 79-98
-
Bokkapitel (refereegranskat)
|
|
| 5. |
- Melek, William W., et al.
(författare)
-
Improved robust fuzzy clustering algorithm
- 1999
-
Ingår i: 1999 IEEE International Fuzzy Systems Conference Proceedings, Seoul, South Korea, 22-25 August 1999. - Piscataway, NJ : IEEE Communications Society.
-
Konferensbidrag (refereegranskat)abstract
- Fuzzy c-means (FCM) and hard clustering algorithms are the most common tools for data partitioning. However, these clustering algorithms may fail completely in the presence of noise. In this paper, we introduce a robust noise rejection clustering algorithm based on a combination of techniques that treat the FCM weak points with a traditional noise rejection algorithm. Unlike the traditional FCM, the proposed algorithm is a powerful tool for partitioning the data in the presence of noise (outliers).
|
|
| 6. |
- Pitts, David T., et al.
(författare)
-
Simulation study of a mobile manipulator
- 1999
-
Ingår i: 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, Kyongju, South Korea, 17-21 October 1999. - Piscataway, NJ : IEEE Communications Society. ; , s. 1115-1120
-
Konferensbidrag (refereegranskat)abstract
- This paper describes a step by step validation procedure of a mobile manipulator simulation. The procedure includes performing basic model simulations, a sensitivity analysis, and a stochastic output analysis. Static, quasi-static, and dynamic experiments performed on a real system and simulated using DADS are also presented. An algorithm to tune the model parameters, to match the output of the simulations to the output of the real system, was successfully implemented. Finally, an optimization procedure on the design parameters of the system is discussed.
|
|
| 7. |
- Türksen, I. Burhan, et al.
(författare)
-
A unified parameterized formulation of reasoning in fuzzy modeling and control
- 1999
-
Ingår i: Fuzzy sets and systems (Print). - : Elsevier. - 0165-0114 .- 1872-6801. ; 108:1, s. 59-81
-
Tidskriftsartikel (refereegranskat)abstract
- A unified parameterized formulation for the reasoning process in fuzzy modeling and control is developed in this paper. First, by selecting a suitable parameterized family of triangular functions and extending to the n-ary operation, the parameterized form of Mamdani's approximation and formal logical reasoning approaches is introduced. Next, by unifying the inference mechanism for both approaches, a unified parameterized reasoning function is developed. It is also proved that for crisp input variables, the two methods of inference from a set of rules, i.e., first-aggregate-then-infer (FATI) and first-infer-then-aggregate (FITA), generate identical fuzzy outputs. The proposed reasoning formulation introduces four reasoning parameters. Depending on these parameters, the reasoning operation varies continuously among the extreme cases in each step of the inference. In order to reduce the computational effort, a fast algorithm for computing the parameterized family of triangular functions is suggested. Further, a simplified parameterized reasoning formulation is also suggested in which the defuzzified output can be calculated directly from the individual consequent fuzzy sets. This simplified formulation is comparable with Sugeno's and Yager's heuristic simplified reasoning functions. Some examples demonstrate the validity of the results. © 1999 Elsevier Science B.V. All rights reserved.
|
|
