Improved Sliding Mode Nonlinear Extended State Observer  based Active Disturbance Rejection Control for Uncertain Systems with Unknown Total Disturbance

Abdul-Adheem, Wameedh Riyadh; Ibraheem, Ibraheem Kasim

doi:10.14569/ijacsa.2016.071211

Cited by 17 publications

(13 citation statements)

References 22 publications

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“…For future research in this field, a recommendation would be to adopt one of the control design methods in [21,22] to add more smoothness to the robotic arm movement. Table 7: Comparison results between this study and other studies in offline mode.…”

Section: Experiments Four: Effect Of Number Of Channels On System Accumentioning

confidence: 99%

Teleoperated robotic arm movement using electromyography signal with wearable Myo armband

Hassan

Abou-Loukh

Ibraheem

2020

Journal of King Saud University - Engineering Sciences

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The main purpose of this research is to move the robotic arm (5DoF) in real-time, based on the surface Electromyography (sEMG) signals, as obtained from the wireless Myo gesture armband to distinguish seven hand movements. The sEMG signals are biomedical signals that estimate and record the electrical signals produced in muscles through their contraction and relaxation, representing neuromuscular activities. Therefore, controlling the robotic arm via the muscles of the human arm using sEMG signals is considered to be one of the most significant methods. The wireless Myo gesture armband is used to record sEMG signals from the forearm. In order to analyze these signals, the pattern recognition system is employed, which consists of three main parts: segmentation, feature extraction, and classification. Overlap technique is chosen for segmenting part of the signal. Six time domain features (MAV, WL, RMS, AR, ZC, and SSC) are extracted from each segment. The classifiers (SVM, LDA, and KNN) are employed to enable comparison between them in order to obtain optimum accuracy of the system. The results show that the SVM achieves higher system accuracy at 96.57 %, compared to LDA reaching 96.01 %, and 92.67 % accuracy achieved by KNN.The electrical signal produced through contraction or relaxation of muscles which are ruled by the 3 nervous system are called Electromyography (EMG) signals. This signal depends on the physiological and anatomical characteristic of muscles and is considered to be a complex signal.The surface electromyography (sEMG) are EMG signals that collect the electrical signals of the muscle activity through placing the electrodes on the surface of the skin. Fig. 1 shows the surface electromyography (sEMG) signals that start with the low amplitude, which changes with muscle contraction activity [1].Detection of sEMG signals are useful and improve important methodologies in many applications.Such applications are becoming increasingly in demand, in spheres such as biomedical engineering[2], the robotics arm and automation control systems [3,4].The measurements and precise representations of the sEMG signals depend on the characteristics of the electrodes and their relationship with the skin of the forearm or shoulder, and are affected by the amplifier design, and the transition of the sEMG signals from analogue to digital format [5].A raw sEMG signal has the maximum voltage of (0-2) mV, and a range of frequency approximately between (0-1000) Hz, but the important frequency that contains useful information lies between (20-500) Hz [6]. The sEMG signals can be acquired by positioning surface electrodes on the arm or the shoulder.There are two main types of the electrodes that acquire sEMG signals: needle electrodes (inside the skin) and surface electrodes, with no significant variance between them [7]. There are two types of surface electrodes: wired like Myoware muscle sensor or wireless such as Myo gesture control armband. They differ in features, the most important of which is the sampling rate. All these...

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Section: Experiments Four: Effect Of Number Of Channels On System Accumentioning

confidence: 99%

Teleoperated robotic arm movement using electromyography signal with wearable Myo armband

Hassan

Abou-Loukh

Ibraheem

2020

Journal of King Saud University - Engineering Sciences

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Section: Nonlinear Design For Olcmentioning

confidence: 99%

“…unknown function which needs to be identified to analyze the stability of (18). Some studies like [30] proposed an extended state observer to estimate the unknown function F U , while [31] proposed a radial basis function (RBF) to approximate F U and include it in the proposed control law. Since our proposed nonlinear PID controller belongs to a class of passive controllers, where the unknown function cannot be estimated by the controller itself, the stability test is based on the linearized model where the unknown function F U is diminished.…”

Section: Nonlinear Design For Olcmentioning

confidence: 99%

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Nonlinear PID controller design for a 6-DOF UAV quadrotor system

Najm

Ibraheem

2019

Engineering Science and Technology, an International Journal

110

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A Nonlinear PID (NLPID) controller is proposed to stabilize the translational and rotational motion of a 6-DOF UAV quadrotor system and enforce it to track a given trajectory with minimum energy and error. The complete nonlinear model of the 6-DOF quadrotor system are obtained using Euler-Newton formalism and used in the design process, taking into account the velocity and acceleration vectors resulting in a more accurate 6-DOF quadrotor model and closer to the actual system. Six NLPID controllers are designed, each for Roll, Pitch, Yaw, Altitude, and the Position subsystems, where their parameters are tuned using GA to minimize a multi-objective Output Performance Index (OPI). The stability of the 6-DOF UAV subsystems has been analyzed in the sense of Hurwitz stability theorem under certain conditions on the gains of the NLPID controllers. The simulations have been accomplished under MATLAB/SIMULINK environment and included three different trajectories, i.e., circular, helical, and square. The proposed NLPID controller for each of the six subsystems of the 6-DOF UAV quadrotor system has been compared with the Linear PID (LPID) one and the simulations showed the effectiveness of the proposed NLPID controller in terms of speed, control energy, and steady-state error.

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“…A backstepping controller based on the Nussbaum function was designed, and the latent singularities in the attitude extraction process caused by saturation nonlinearities were avoided. In [8]- [12], sliding mode control schemes were proposed to address the problems of underactuation, model uncertainty, actuator failure, and external disturbance. The stability of the closed-loop system was guaranteed with the Lyapunov theory.…”

Section: Introductionmentioning

confidence: 99%

Active Disturbance Rejection Generalized Predictive Control of a Quadrotor UAV via Quantitative Feedback Theory

Cheng

Dai

et al. 2022

IEEE Access

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This paper investigates the design of a robust controller for a quadrotor unmanned aerial vehicle (UAV) system subject to parameter uncertainties and external disturbances. A new double closed-loop active disturbance rejection generalized predictive control (ADRC-GPC) is proposed for the trajectory tracking problem of the UAV. Considering the measurement noise, the ADRC-GPC control strategy can provide better dynamic performance and robustness against uncertainties and external disturbances than active disturbance rejection control (ADRC). Furthermore, a controller parameter tuning method for ADRC-GPC is proposed. In the framework of the classical two-degree-of-freedom equivalent model, the bandwidth of ESO is determined according to the parameter selection criterion. The criterion is a trade-off between the estimation accuracy and the immunity of measurement noise. In addition, the parameters of the ADRC-GPC controller are tuned by quantitative feedback theory to achieve the expected performance specifications. Finally, the proposed method is compared with several traditional control methods in the simulation experiments. The simulation results show that the proposed method has better set-point tracking performance, disturbance rejection performance, and robustness.INDEX TERMS Quadrotor UAVs, uncertainties and disturbances, active disturbance rejection generalized predictive control, quantitative feedback theory, parameter tuning.

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Improved Sliding Mode Nonlinear Extended State Observer based Active Disturbance Rejection Control for Uncertain Systems with Unknown Total Disturbance

Cited by 17 publications

References 22 publications

Teleoperated robotic arm movement using electromyography signal with wearable Myo armband

Teleoperated robotic arm movement using electromyography signal with wearable Myo armband

Nonlinear PID controller design for a 6-DOF UAV quadrotor system

Active Disturbance Rejection Generalized Predictive Control of a Quadrotor UAV via Quantitative Feedback Theory

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