User designed manipulators are widely used in industry as a part of automation. The design of lighter robotic arms is required for less energy consumption. Joints, structural features, and payload ...affect the dynamic behavior of manipulators. Even if the arms have sufficient structural rigidity, joints, or payloads further increase their flexibility. These factors should be considered at the design stage. Flexibility causes vibrations, and these vibrations negatively affect robot repeatability and processing speed. Reducing the vibration levels of flexible manipulators is an attractive issue for engineers and researchers. Accurate estimation of the mathematical model of flexible manipulators increases the success of vibration control. In this paper, the modeling and experiments for vibration control of a single-axis flexible curved manipulator with payload are considered. The experimental system is introduced to collect vibration responses synchronously at the tip of the curved manipulator for angular velocity input. The mathematical model of the manipulator is estimated using the continuous-time system identification (CTSI) method with a black-box model based on the experimental input/output (I/O) signals. A five-segment S-curve motion input based on the modal parameters is designed to suppress residual vibrations. Vibration control is successfully performed for different deceleration times of the designed S-curve motion input. The results showed that the residual vibrations from experiments and predicted models matched well for different cases depending on payload, angular position, and motion time.
In this paper, the principal time-domain system identification methods are studied and used to carry out the experimental modal analysis of a single-link flexible manipulator with curved geometry in ...a comparative fashion. The identification process carried out in this study is aimed at deriving first-order state-space dynamical models and second-order configuration-space dynamical models of the single-link flexible manipulator by using an array of fundamental system identification procedures, which includes the ARX (AutoRegressive eXogenous method), SSEST (State-Space ESTimation method), N4SID (Numerical Algorithm for Subspace State-Space System Identification), ERA/OKID (Eigensystem Realization Algorithm combined with the Observer/Kalman Filter Identification method), and TFEST (Transfer Function ESTimation method) methods. The main goal of this paper is, therefore, to perform a comparative study between the most fundamental system identification procedures applied to the numerical and experimental analysis of the structural vibrations of a flexible manipulator having a curved geometric shape. First, this paper poses the theoretical foundations for the development of the numerical study and the experimental testing to be performed for the case study. Subsequently, the flexible manipulator analyzed in this work is modeled with various levels of complexity, which range from a simple lumped parameter model to a linear finite element model developed by integrating the interconnections between the SOLIDWORKS and ANSYS software. By doing so, the computational procedures applied to the time-domain experimental measurements of the dynamic behavior of the flexible manipulator allow for reconstructing first-order and second-order mechanical models of the structural system of interest in the MATLAB simulation environment. The numerical results and the experimental tests reported in this investigation demonstrate the effectiveness of all the time-domain system identification approaches considered in the paper.
This paper is the second part of a two-part research work intended at realizing a systematic computational and experimental analysis of the principal data-driven identification procedures based on ...the Observer/Kalman Filter Identification Methods (OKID) and the Numerical Algorithms for Subspace State-Space System Identification (N4SID). More specifically, this second paper treats the presentation of the numerical analysis and the experimental testing carried out in this study. To perform a systematic comparison, the identification methods mentioned before are implemented in a general-purpose computer program developed in the MATLAB computational environment. To this end, a simple two-degrees-of-freedom dynamical model of a vibrating mechanical system is considered first as a demonstrative example. The demonstrative example is used to carry out a numerical analysis of the performance of the computational methods of interest for this investigation. Subsequently, an experimental analysis is carried out focusing on a three-dimensional structure that vibrates under the effect of an external source of impulsive excitation. To perform a thorough analysis, the flexible structure employed as an experimental case study is modeled starting from its CAD geometric model and assuming different levels of complexity, which range from a simple three-degrees-of-freedom lumped parameter model to a relatively more complex linear finite element model. In the paper, the mechanical models of the structural system considered as illustrative examples are principally employed for comparing the results arising from the modal analysis. The computational and experimental analysis of these structural models turned out to be useful to trace guidelines for evaluating the effectiveness and the efficiency of the numerical and experimental results obtained from the identification process. In this study, a numerical and experimental analysis of the OKID algorithms and the N4SID methods is developed. Both classes of techniques enable the time-domain state-space system identification, that is, they construct an estimation of the state, input influence, output influence, and direct transmission matrices which characterize the dynamic properties of a mechanical system. The present investigation demonstrates that, if properly tuned, both the OKID methods and the N4SID algorithms lead to consistent numerical and experimental results, even in the case when the input-output measurements used for performing the identification procedure are affected by a certain degree of noise. The numerical and experimental results found in this second part of the research work confirmed the efficacy of the time-domain system identification methodologies described in the first part of the paper.
This paper is the first part of a two-part research work aimed at performing a systematic computational and experimental analysis of the principal data-driven identification procedures based on the ...Observer/Kalman Filter Identification Methods (OKID) and the Numerical Algorithms for Subspace State-Space System Identification (N4SID). Considering the approach proposed in this work, the state-space model of a mechanical system can be identified with the OKID and N4SID methods. Additionally, the second-order configuration-space dynamical model of the mechanical system of interest can be estimated with the MKR (Mass, Stiffness, and Damping matrices) and PDC (Proportional Damping Coefficients) techniques. In particular, this first paper concentrates on the description of the fundamental analytical methods and computational algorithms employed in this study. In this investigation, numerical and experimental analyses of two fundamental time-domain system identification techniques are performed. To this end, the main variants of the OKID and the N4SID methods are examined in this study. These two families of numerical methods allow for identifying a first-order state-space model of a given dynamical system by directly starting from the time-domain experimental data measured in input and output to the system of interest. The basic steps of the system identification numerical procedures mentioned before are described in detail in the paper. As discussed in the manuscript, from the identified first-order state-space dynamical models obtained using the OKID and N4SID methods, a second-order configuration-space mechanical model of the dynamic system under consideration can be subsequently obtained by employing another identification algorithm described in this work and referred to as the MKR method. Furthermore, by using the second-order dynamical model obtained from experimental data, and considering the hypothesis of proportional damping, an effective technique referred to as the PDC method is also introduced in this investigation to calculate an improved estimation of the identified damping coefficients. In this investigation, a numerical and experimental comparison between the OKID methods and the N4SID algorithms is proposed. Both families of methodologies allow for performing the time-domain state-space system identification, namely, they lead to an estimation of the state, input influence, output influence, and direct transmission matrices that define the dynamic behavior of a mechanical system. Additionally, a least-square approach based on the PDC method is employed in this work for reconstructing an improved estimation of the damping matrix starting from a triplet of estimated mass, stiffness, and damping matrices of a linear dynamical system obtained using the MKR identification procedure. The mathematical background thoroughly analyzed in this first research work serves to pave the way for the applications presented and discussed in the second research paper.
In this study, it was aimed to determine elbow movements and elbow angles with changes in the EEG signal by thinking voluntarily or involuntaryly. In this study, EEG signals generated during 4 ...different arm movements of the subjects were taken and the EEG data of these 4 cases were discriminated from each other. For this purpose, a common spatial pattern (CSP) and power spectral density (PSD) methods are used to extract the feature, and artificial neural networks (ANN) are used for classification. The average success rate was 85.65% in the first person and 79.93% in the second person under test. In future studies, real-time control of robotic arms or similar prosthetic devices with various arm movements will be possible to implement by using this method.
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