Incremental fuzzy expert PID control

Tzafestas, Spyros G.; Papanikolopoulos, N.

doi:10.1109/41.103431

Cited by 241 publications

(66 citation statements)

References 13 publications

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“…The capability to qualitatively capture the attributes of a control system based on observable phenomena is a main feature of fuzzy control. These aspects of fuzzy control have been demonstrated in the research literature; see [13]- [16], [19], and [20], and commercial products from vendors like Reliance Electric and Omron. The ability of fuzzy logic to capture system dynamics qualitatively, and execute this qualitative idea in a real-time situation, is an attractive feature for temperature control systems.…”

Section: Fuzzy Logic Controlmentioning

confidence: 99%

A stable self-tuning fuzzy logic control system for industrial temperature regulation

Gao

Trautzsch

Dawson³

2002

IEEE Trans. on Ind. Applicat.

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Section: Fuzzy Logic Controlmentioning

confidence: 99%

A stable self-tuning fuzzy logic control system for industrial temperature regulation

Gao

Trautzsch

Dawson³

2002

IEEE Trans. on Ind. Applicat.

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“…The block diagram of the FE-PI for controlling the tip displacement of the microgripper is shown in Figure 12. The design of the FE-PI was to follow the approach of an incremental fuzzy expert PID control [46]. For the FE-PI, the P and I action are given, respectively, by { } 1…”

Section: Gripping Force In Grasping Controlmentioning

confidence: 99%

Self-Biased-SMA Drive PU Microgripper with Force Sensing in Visual Servo

Chang

Shiu

Cheng

2013

International Journal of Advanced Robotic Systems

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An innovative design of a polyurethane microgripper system with force sensor is developed for the measurement of gripping force in vision-based control. A microgripper mechanism integrated with a force sensing arm is fabricated by an excimer laser. The microgripper is actuated by a self-biased-SMA (Shape Memory Alloy) actuator. A computer-vision method through the ERES (Extended Regional Edge Statistics) algorithm is employed to track the motion of gripper. The position information of the gripping point together with the deflection of the force sensing arm is utilized for sensing force. A fuzzy expert with a PI controller in a visual servo is employed to test the performance of sensing the gripping force in grasping of 38μm diameter metal rod. In the performance test, the microgripper system provides a maximum gripping size of 40μm, a maximum force resolution of 1μN and a maximum gripping force of 58μN.

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“…Especially, the most successful FLC applied into industrial plants are designed by control engineers. Defining membership functions of linguistic variables and formulating fuzzy rules by manual operation is time consuming work [4][5][6]. Fuzzy concept was presented by Zadeh in 1965 to process / manipulate data and information affected by probabilistic uncertainty/imprecision [7].…”

Section: Introductionmentioning

confidence: 99%

Design an Innovative Smart Control Scheme to Hybridize Conventional and Expert Controllers

Aboattia¹

2016

ijecs

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Abstract:The purposes of the proposed research present approaches to the design of an Innovative Smart Control Scheme (SCS) methodology mergers the proposed local controllers (conventional PID and the expert controller such as fuzzy Logic controllers (FLC)) in a smart way. This SCS switching and merging mechanism depends on FLC that monitors the process state and act as a supervisory to make hybrid control between the local controllers. A local controller is an analytical controller designed to work around specific process operation .Once the conditions change, the rule-based supervisor decreases the influence of one classical controller (PI) and gives more weight to another intelligent controller (Fuzzy Logic) that has been designed to work in the new conditions. Application of the suggested (SCS) on a simple plant which could be a resonator in proposed system, DC machine is introduced and better response of the new proposed SCS is presented. The performance of SCS will compared to the FLC and PID. MATLAB/SIMULINK program is projected for Comparison testing by applying different disturbances cases.

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Incremental fuzzy expert PID control

Cited by 241 publications

References 13 publications

A stable self-tuning fuzzy logic control system for industrial temperature regulation

A stable self-tuning fuzzy logic control system for industrial temperature regulation

Self-Biased-SMA Drive PU Microgripper with Force Sensing in Visual Servo

Design an Innovative Smart Control Scheme to Hybridize Conventional and Expert Controllers

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