Trajectory tracking in upper limb rehabilitation exercises is utilized for repeatability of joint movement to improve the patient’s recovery in the early stages of rehabilitation. In this article, non-linear active disturbance rejection control as a combination of non-linear extended-state observer and non-linear state error feedback is used for the sinusoidal trajectory tracking control of the two-link model of an upper limb rehabilitation exoskeleton. The two links represent movements like flexion/extension for both the shoulder joint and the elbow joint in the sagittal plane. The Euler–Lagrange method was employed to acquire a dynamic model of an upper limb rehabilitation exoskeleton. To examine the efficacy and robustness of the proposed method, four disturbances cases in simulation studies with 20% parameter variation were applied. It was found that the non-linear active disturbance rejection control is robust against disturbances and achieves better tracking as compared to proportional–integral–derivative and existing conventional active disturbance rejection control method.
In this paper, a combined control strategy with extended state observer (ESO) and finite time stable tracking differentiator (FTSTD) has been proposed to perform flexion and extension motion repetitively and accurately in the sagittal plane for shoulder and elbow joints. The proposed controller improves the tracking accuracy, performs state estimation, and actively rejects disturbance. A sinusoidal trajectory as an input has been given to a two-link multiple-input multiple-output (MIMO) upper limb robotic rehabilitation exoskeleton (ULRRE) for a passive rehabilitation purpose. The efficacy of the controller has been tested with the help of performance indices such as integral time square error (ITSE), integral square error (ISE), integral time absolute error (ITAE), and integral of the absolute magnitude of error (IAE). The system model is obtained through the Euler–Lagrangian method, and the controller’s stability is also given. The proposed controller has been simulated for ±20% parameter variation with constant external disturbances to test the disturbance rejection ability and robustness against parametric uncertainties. The proposed controller has been compared with already developed ESO-based methods such as active disturbance rejection control (ADRC), nonlinear active disturbance rejection control (NLADRC), and improved active disturbance rejection control (I-ADRC). It has been found that the proposed method increases tracking performance, as evidenced by the above performance indices.
Purpose The purpose of this paper is to provide benefits for companies or organizations, which deal with fewer input-outputs and wanted to control their industrial processes remotely with a robust control strategy. Design/methodology/approach In this paper, an active disturbance rejection control (ADRC) strategy is used for the two tank level process plant and it is remotely monitored with the industrial internet of things technology. The disturbances in a primary and secondary loop of the cascade process, which are affecting the overall settling time (ts) of the process, are eliminated by using the proposed, ADRC-ADRC structure in the cascade loop. The stability of the proposed controller is presented with Hurwitz’s stability criteria for selecting gains of observers. The results of the proposed controller are compared with the existing active disturbance rejection control-proportional (ADRC-P) and proportional-integral derivative-proportional (PID-P)-based controller by experimental validation. Findings It is observed that the settling time (ts) in the case of the proposed controller is improved by 60% and 55% in comparison to PID-P and ADRC-P, respectively. The level process is interfaced with an industrial controller and real-time data acquired in matrix laboratory (MATLAB), which acted as a remote monitoring platform for the cascade process. Originality/value The proposed controller is designed to provide robustness against disturbance and parameter uncertainty. This paper provides an alternate way for researchers who are using MATLAB and ThingSpeak cloud server as a tool for the implementation.
False-positive problem (FPP) is a one of the challenging tasks for the researchers. It authenticates the wrong owner to access the multimedia content. To overcome, the FPP problem, this paper introduces an efficient watermarking method based on the selection of highest entropy blocks. In this method, cover and watermark images are initially shuffled through Arnold transform. Then, the encrypted images are further processed by a 2-level discrete wavelet transform followed by singular value decomposition. The proposed method has been evaluated with geometrical, filtering, noise, and contrast adjustment attacks on the standard image datasets against five recently developed watermarking methods. The simulation results reveal that the proposed method outperforms the existing methods. INDEX TERMSColor watermarking, False positive problem, Arnold transform, Discrete wavelet transform, Singular value decomposition.
This paper presents an enhanced generalized extended state observer (EGESO) based sliding mode control (SMC) technique for dealing with the disturbance attenuation problem for a class of non-integral chain systems with mismatched uncertainty. In the proposed control law, the robust SMC with reaching phase elimination is applied in the proposed control law, which uses the estimated states of a system. The stability analysis is thoroughly examined for both EGESO and SMC. The efficacy of the proposed controller is verified using specific examples, and later it is applied on a single-link flexible manipulator. Through simulation and experimentation analysis, it is observed that the proposed controller is giving a robust transient response as compared to existing GESO based controllers.
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