Fuzzy logic control of single-link flexible joint manipulator

Akyuz, Ismail H.; Kizir, Selçuk; Bingül, Zafer

doi:10.1109/icit.2011.5754392

Cited by 19 publications

(18 citation statements)

References 24 publications

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“…Over the last two decades, the control of FJMs has also benefited from strategies that seek to reduce the impact of modeling difficulties on controller performance, instead making use such nonmodel intensive strategies as genetic algorithms, particle swarm optimization methods, fuzzy logic, neural networks [11][12][13][14][15][16][17], and more recently, the so-called iPI and iPID strategies [18][19][20]. These pseudomodel based control strategies have enabled a compromise between the cost of real-time controller implementation and the need for highly accurate models in model-based control.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

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 last controller generates control signal of the system using outputs of the other controllers (θ, α). Interval MFs and rules related to all controllers have been designed based on type-1 MFs exist in [17]. All IT2FL-C was designed on our interval type-2 fuzzy logic toolbox as shown in Fig.…”

Section: Design Of the Cascade It2fl-cmentioning

confidence: 99%

Real Time Control of a Flexible Joint Manipulator Using Interval Type-2 Fuzzy Logic Controller

Kelekçi¹,

Kizir²

2018

International Conference of Control, Dynamic Systems, and Robotics

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-Flexible manipulators have several benefits over the rigid manipulators such as light weight with higher payload, better maneuverability, higher operational speed, lower energy consumption. Along these advantages, however, the flexible manipulators cause vibrations and trajectory tracking control problems. In this study, a cascade interval type-2 fuzzy logic controller (IT2FL-C) was proposed for real time trajectory and vibration control of a flexible joint manipulator. The proposed controller was implemented in the system using dSpace DS1103 real-time control board. The cascade control structure includes three separate IT2FL-C. The IT2FL-Cs were designed on the IT2FL-C toolbox, which we developed, with interval triangular membership functions (MFs), Mamdani's fuzzy inference method and Karnik-Mendel (KM) type reduction (TR) algorithm. Several experiments conducted for observation of the proposed IT2FL-C's performance. The step and sinusoidal trajectories were applied to the system changing link length with/without external payload. Additionally, performance of the proposed controllers was compared to conventional type-1 fuzzy logic controller (T1FL-C).

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“…Fuzzy control is a control way of applying expert knowledge to control a plant without having detail information of the plant in passing [10]. A FLC has three main component namely i) Fuzzifier which convert the input signal into fuzzy signal ii) fuzzy inference engine which process the fuzzified signal using decision rules, and iii) Defuzzifier which convert the fuzzy controller output signal to a signal used as the control input signal to the system model.…”

Section: Introductionmentioning

confidence: 99%

“…A FLC has three main component namely i) Fuzzifier which convert the input signal into fuzzy signal ii) fuzzy inference engine which process the fuzzified signal using decision rules, and iii) Defuzzifier which convert the fuzzy controller output signal to a signal used as the control input signal to the system model. Explicitly in [10], three different fuzzy logic controllers (FLCs) are developed to control vibration and end point deflection. A Hybrid fuzzy logic control with genetic optimisation for vibration control of a single-link flexible manipulator is presented in passing [11].…”

Section: Introductionmentioning

confidence: 99%

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Vibration and tip deflection control of a Single-link flexible manipulator

Abdullahi¹,

Mohamed²,

Muhammad³

et al. 2013

IJICS

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In this paper, a hybrid control scheme for vibration and tip deflection control of a single link flexible manipulator system is presented. The purpose of this control is for input tracking, vibration control of hub angle and tip deflection control. The control scheme consists of a resonant controller and a fuzzy logic controller (FLC).The resonant controller is used as the inner loop feedback controller for vibration control using the resonant frequencies at different resonant modes of the system which were determined from experiment. The fuzzy logic controller is designed as the outer loop feedback controller for the tracking control and to achieve zero steady state error. The performance of the proposed control scheme is investigated via simulations and the results show the effectiveness of the control scheme, in addition thecontroller is tested to show it robustness using different values of payload.

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Fuzzy logic control of single-link flexible joint manipulator

Cited by 19 publications

References 24 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

Real Time Control of a Flexible Joint Manipulator Using Interval Type-2 Fuzzy Logic Controller

Vibration and tip deflection control of a Single-link flexible manipulator

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