Control of Flexible Joint Manipulator via Reduced Rule-Based Fuzzy Control with Experimental Validation

Kandroodi, Mojtaba Rostami; Mansouri, Mohammad; Shoorehdeli, Mahdi Aliyari; Teshnehlab, Mohammad

doi:10.5402/2012/309687

Cited by 15 publications

(7 citation statements)

References 19 publications

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“…5 Step input performances of the classical and distributed HODFC oscillations all damped out in the steady-state. The results reported by Kandroodi et al [22] using fuzzy control in tracking the square-wave trajectory showed that joint vibrations could not be completely damped and steady-state error persists.…”

Section: 1mentioning

confidence: 99%

“…A model of the system based on derivation in Ref. [22] is recollected in this section. An alternative presentation of the system model in terms of a flat output and the use of the flat output to clarify the existence and problems of nonminimum phase dynamics in the manipulator are also included.…”

Section: System Modeling and Experimental Setupmentioning

confidence: 99%

“…Specifically, it has been shown in Ref. [22] that the Lagrangian (L) of the single-link FJM system is given below…”

Section: Mathematical Modeling Of the Flexible-joint Manipulatormentioning

confidence: 99%

“…The Euler-Lagrangian derivation of the system model [22] then produces two second-order linear systems given by…”

Section: Mathematical Modeling Of the Flexible-joint Manipulatormentioning

confidence: 99%

See 3 more Smart Citations

Trajectory and Vibration Control of a Single-Link Flexible-Joint Manipulator Using a Distributed Higher-Order Differential Feedback Controller

Agee

Bingül

Kizir

2017

Journal of Dynamic Systems, Measurement, and Control

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The trajectory tracking in the flexible-joint manipulator (FJM) system becomes complicated since the flexibility of the joint of the FJM superimposes vibrations and nonminimum phase characteristics. In this paper, a distributed higher-order differential feedback controller (DHODFC) using the link and joint position measurement was developed to reduce joint vibration in step input response and to improve tracking behavior in reference trajectory tracking control. In contrast to the classical higher-order differential (HOD), the dynamics of the joint and link are considered separately in DHODFC. In order to validate the performance of the DHODFC, step input, trajectory tracking, and disturbance rejection experiments are conducted. In order to illustrate the differences between classical HOD and DHODFC, the performance of these controllers is compared based on tracking errors and energy of control signal in the tracking experiments and fundamental dynamic characteristics in the step response experiments. DHODFC produces better tracking errors with almost same control effort in the reference tracking experiments and a faster settling time, less or no overshoot, and higher robustness in the step input experiments. Dynamic behavior of DHODFC is examined in continuous and discontinues inputs. The experimental results showed that the DHODFC is successful in the elimination of the nonminimum phase dynamics, reducing overshoots in the tracking of such discontinuous input trajectories as step and square waveforms and the rapid damping of joint vibrations.

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Section: 1mentioning

confidence: 99%

Section: System Modeling and Experimental Setupmentioning

confidence: 99%

“…Specifically, it has been shown in Ref. [22] that the Lagrangian (L) of the single-link FJM system is given below…”

Section: Mathematical Modeling Of the Flexible-joint Manipulatormentioning

confidence: 99%

“…The Euler-Lagrangian derivation of the system model [22] then produces two second-order linear systems given by…”

Section: Mathematical Modeling Of the Flexible-joint Manipulatormentioning

confidence: 99%

See 2 more Smart Citations

Trajectory and Vibration Control of a Single-Link Flexible-Joint Manipulator Using a Distributed Higher-Order Differential Feedback Controller

Agee

Bingül

Kizir

2017

Journal of Dynamic Systems, Measurement, and Control

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show abstract

“…[28] is based on the usage of a multi-level logic. [29][30][31][32] consider natural variable separations based on the specificity of controller design. Coefficients evaluating the rule's redundancy are successfully used in [33].…”

Section: She Initial Trainingmentioning

confidence: 99%

Training, Retraining, and Self-training Procedures for the Fuzzy Logic-Based Intellectualization of IoT&S Environments

Dovydaitis

Jasinevičius

Petrauskas

et al. 2015

Int. J. Fuzzy Syst.

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In the past (few) years a lot of research and development projects were proposed concerning the implementation of smart home environment, sometimes even called an intelligent environment, comforting people who use various adaptations of internet of things and services. This paper advocates an opinion that a set of electronically controlled smart things must be intellectualized using human-type reasoning. A novel approach and new algorithms for the hierarchical fuzzy training, retraining, and selftraining for intellectualized home environments are proposed in this paper. Training algorithms based on fuzzy logic use top-down hierarchical analysis of home situations under consideration to conquer the curse of increasing number of rules. A successful combination of crisp algorithms for the identification of presence/absence of users in the environment with fuzzy logic-based algorithms for corresponding rules subsets development and processing enables the number of necessary rules to decrease significantly. In the paper a case is presented with the starting number of 2500 rules which later diminished approximately 5 times. For the first time, the changes in users' wishes are taken into account during the retraining process. An entirely new ability of the system was investigated, and a fuzzy logic-based algorithm for initiating a self-training process without any a priori information is developed as well. The vitality and efficiency of the proposed methodology was tested and simulated on a specialized virtual software/hardware modeling system. The proposed and simulated algorithms are delivered for use in two industrial projects.

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Design a Fuzzy Logic Controller for a Rotary Flexible Joint Robotic Arm

Jalani

Jayaraman

2018

MATEC Web of Conferences

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The purpose of this research is to design a fuzzy logic feedback controller (FLC) in order to control a desired tip angle position a rotary flexible joint robotic arm. The FLC is also employed to dampen the vibration emanated from a rotary flexible joint robotic arm when reaching a desired tip angle position. The performance of FLC is tested in simulation and experiment. It is found that the FLC is successfully designed, applied and tested. The results show that fuzzy logic controller performed satisfactorily control a desired tip angle position and reduce the oscillations.

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Control of Flexible Joint Manipulator via Reduced Rule-Based Fuzzy Control with Experimental Validation

Cited by 15 publications

References 19 publications

Trajectory and Vibration Control of a Single-Link Flexible-Joint Manipulator Using a Distributed Higher-Order Differential Feedback Controller

Trajectory and Vibration Control of a Single-Link Flexible-Joint Manipulator Using a Distributed Higher-Order Differential Feedback Controller

Training, Retraining, and Self-training Procedures for the Fuzzy Logic-Based Intellectualization of IoT&S Environments

Design a Fuzzy Logic Controller for a Rotary Flexible Joint Robotic Arm

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