Trajectory tracking control based on non-singular fractional derivatives for the PUMA 560 robot arm

Lavín-Delgado, J. E.; Solís-Pérez, J.E.; Gómez-Aguilar, J. F.; Escobar-Jiménez, R.F.

doi:10.1007/s11044-020-09752-y

Cited by 21 publications

(10 citation statements)

References 35 publications

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“…Control signal ( 17) is consequently v = diag(λ 1 , λ 2 )•(θ −φ * ). Upon substituting v in (16) and considering (2),û is obtained. Upon substitutingû in (10), considering that θ * = θ in the steady state and operating, the conditions that allow the attainment of the references for the motor angles are yielded The fictitious input μ is obtained from (3) by substituting the real values φ of the tip position and considering that θ * = θ .…”

Section: F Simulated Results Using a High-pass Filtermentioning

confidence: 99%

“…FOCs have been already applied to industrial robots with rigid links [16]. However, their application to flexible robots is rare.…”

Section: Foc To Increase Robustness To Sensor Disturbancesmentioning

confidence: 99%

“…The fourth condition is verified next. By making = 0 in (12), we obtain that A •ẑ = −B •û, while substitutingû for the control law (16) and (17) where it has been considered that x and x c are zero when the closed-loop system exactly follows the reference. By substituting (70) and ( 71) into (21) and considering that μ(μ −1 (a, b), b) = a, P(x) is canceled by P(x +x * ) because x =x −x * and, operating, gives the dynamics of the reduced systemẋ…”

Section: Appendix I Proof Of Theoremmentioning

confidence: 99%

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Control of Very Lightweight 2-DOF Single-Link Flexible Robots Robust to Strain Gauge Sensor Disturbances: A Fractional-Order Approach

Feliu-Talegón

Feliu-Batlle

2022

IEEE Trans. Contr. Syst. Technol.

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This article deals with the control of two degrees of freedom manipulators that have a flexible and very lightweight link. These robots have a single low-frequency and highamplitude vibration mode. Their actuators have high friction, and their vibration sensors are often strain gauges that have offset and high-frequency noise. These problems reduce the robot precision and produce noisy control signals that saturate actuators. An efficient control system is proposed to overcome these drawbacks. Actuator friction effect is nearly removed by closing a high gain position control loop around the actuator. It causes the separation of the robot dynamics into the controlled actuator fast subsystem and the link dynamics slow subsystem. Based on that, an innovative control system is designed to remove vibrations using the singular perturbation theory combined with the input-state linearization technique. This control system includes fractional-order controllers that nearly remove unknown sensor offset and sensor ramp disturbances while reducing the high-frequency component of the control signal caused by sensor noise. Simulated and experimental results show the superior performance of these controllers over other standard integerorder controllers of similar complexity and nominal behavior.

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Section: F Simulated Results Using a High-pass Filtermentioning

confidence: 99%

“…FOCs have been already applied to industrial robots with rigid links [16]. However, their application to flexible robots is rare.…”

Section: Foc To Increase Robustness To Sensor Disturbancesmentioning

confidence: 99%

Section: Appendix I Proof Of Theoremmentioning

confidence: 99%

See 1 more Smart Citation

Control of Very Lightweight 2-DOF Single-Link Flexible Robots Robust to Strain Gauge Sensor Disturbances: A Fractional-Order Approach

Feliu-Talegón

Feliu-Batlle

2022

IEEE Trans. Contr. Syst. Technol.

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“…The dataset is collected in real-time from the test rig as shown in figure 2. A robotic manipulator is designed based on PUMA robotic structure [34]. Different types of DC motors i.e.…”

Section: Experimental Setup 311 Data Collectionmentioning

confidence: 99%

Enhancing robotic manipulator fault detection with advanced machine learning techniques

Khan,

Jamil,

Khan

et al. 2024

Eng. Res. Express

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The optimization of rotating machinery processes is crucial for enhanced industrial productivity. Automatic machine health monitoring systems play a vital role in ensuring smooth operations. This study introduces a novel approach for fault diagnosis in robotic manipulators through motor sound analysis to enhance industrial efficiency and prevent machinery downtime. A unique dataset is generated using a custom robotic manipulator to examine the effectiveness of both deep learning and traditional machine learning in identifying motor anomalies. The investigation includes a two-stage analysis, initially leveraging 2D spectrogram features with neural network architectures, followed by an evaluation of 1D MFCC features using various conventional machine learning algorithms. The results reveal that the proposed custom CNN and 1D-CNN models significantly surpass traditional methods, achieving an F1-score exceeding 92%, highlighting the potential of sound analysis for automated fault detection in robotic systems. Additional experiments were carried out to investigate 1D MFCC features with various machine learning algorithms, including KNN, DT, LR, RF, SVM, MLP, and 1D-CNN. Augmented with additional data collected from the locally designed manipulator, our experimental setup significantly enhances model performance. Particularly, the 1D-CNN stands out as the top-performing model on the augmented dataset.

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“…According to the abovementioned analysis of the dynamic model of the Multi-DoF manipulator arm of the walking machine [10], the controlled object model of the original moving parts of each component of the Multi-DoF manipulator arm of the walking machine is described as the following equation:…”

Section: ⎧ ⎨ ⎩mentioning

confidence: 99%

A Multi-DOF Manipulator Joint Trajectory Tracking and Monitoring Method Based on Decision Tree

Chen

Liu

2022

Mathematical Problems in Engineering

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Understanding trajectory tracking control concerns is crucial for industrial-grade manipulators to provide precise and risk-free operations for the safe environment. Consequently, the robot arms need precise aim for tracking a given target trajectory by the trajectory control input driving torque which can use smart AI-based techniques for precision. Similarly, a decision tree is a soft computing-based method of feature space partitioning which can certainly allow the movement of robots in an accurate manner. The control of robot arms is an important aspect for automating the process of sustainable development. Aiming at the problem of poor tracking accuracy of traditional Multi-Degree-of-Freedom Manipulator Joint Trajectory Monitoring (Multi-DoF MJTM) and long monitoring delay, this article proposes a Multi-DOF manipulator joint trajectory tracking method based on decision tree. The Multi-DoF manipulator is developed for the adaptive control object of the working machine, and it is combined with the output response feature to construct the kinematics model of the Multi-DoF manipulator mechanism of the walking machine. The joint trajectory reconstruction of the running trajectory is used to obtain the joint trajectory deviation of the Multi-DoF manipulator’s running trajectory through the multi-measurement system. Based on this, the Multi-DoF manipulator’s running trajectory joint trajectory tracking control equation is obtained to realize the joint trajectory tracking and monitoring of the manipulator. The features for a safe environment are also integrated. The experimental results show that the proposed method has high accuracy in tracking the trajectory of Multi-DoF manipulator joints, and the time delay for tracking and monitoring the trajectory of manipulator’s joints is also optimized.

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Trajectory tracking control based on non-singular fractional derivatives for the PUMA 560 robot arm

Cited by 21 publications

References 35 publications

Control of Very Lightweight 2-DOF Single-Link Flexible Robots Robust to Strain Gauge Sensor Disturbances: A Fractional-Order Approach

Control of Very Lightweight 2-DOF Single-Link Flexible Robots Robust to Strain Gauge Sensor Disturbances: A Fractional-Order Approach

Enhancing robotic manipulator fault detection with advanced machine learning techniques

A Multi-DOF Manipulator Joint Trajectory Tracking and Monitoring Method Based on Decision Tree

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