Experimental Application of Robust and Converse Dynamic Control for Rotary Flexible Joint Manipulator System

Performance evaluation of trajectory tracking for a rotary flexible joint system is demonstrated in this paper. The robust and converse dynamic (RCD) technique is proposed and implemented for this evaluation. This control methodology is of the left inversion type, i.e., the control inputs are obtained by means of plant output error feedback. RCD control encompasses the baseline inverse (BI) control and sliding mode control-based discontinuous control element. The baseline inverse controller enforces the prescribed servo (virtual) constraints that represent the control objectives. The control objectives of the baseline inverse controller are enclosed in the form of servo (virtual) constraints which are inverted using Moore–Penrose Generalized Inverse (MPGI) to solve for the baseline control law. To boost the robust attributes against parametric uncertainties and disturbances, a discontinuous control function is augmented with baseline controller such that semiglobal practical stability is guaranteed in the sense of Lyapunov. To exhibit the effectiveness of RCD control in terms of tracking performance, computer simulations are conducted in Simulink/Matlab environment. Furthermore, the practical implementation is also investigated through a real-time experiment on Quanser’s rotary flexible joint manipulator system. The experimental results obtained by RCD are compared to the conventional sliding mode and fractional-order control techniques.

[1]  Ubaid M. Al-Saggaf,et al.  State feedback with fractional integral control design based on the Bode’s ideal transfer function , 2016, Int. J. Syst. Sci..

[2]  Jamal Daafouz,et al.  Robust control of a flexible robot arm using the quadratic d-stability approach , 1998, IEEE Trans. Control. Syst. Technol..

[3]  Rachid Mansouri,et al.  Two degrees of freedom fractional controller design: Application to the ball and beam system , 2019 .

[4]  Dilbag Singh,et al.  Metaheuristic-based Deep COVID-19 Screening Model from Chest X-Ray Images , 2021, Journal of healthcare engineering.

[5]  Jinkun Liu,et al.  Advanced Sliding Mode Control for Mechanical Systems , 2011 .

[6]  Kuldip S. Rattan,et al.  Modeling and Control of Single- Link Flexible Arms With Lumped Masses , 1992 .

[7]  Huaicheng Yan,et al.  Improved inequality-based functions approach for stability analysis of time delay system , 2019, Autom..

[8]  Yongming Li,et al.  Adaptive fuzzy output feedback control for a single-link flexible robot manipulator driven DC motor via backstepping ☆ , 2013 .

[9]  J. Jalani,et al.  Design a Fuzzy Logic Controller for a Rotary Flexible Joint Robotic Arm , 2018 .

[10]  Fumitoshi Matsuno,et al.  Augmented Stable Fuzzy Control for Flexible Robotic Arm Using LMI Approach and Neuro-Fuzzy State Space Modeling , 2008, IEEE Transactions on Industrial Electronics.

[11]  Tsu-Tian Lee,et al.  Observer-Based T–S Fuzzy Control for a Class of General Nonaffine Nonlinear Systems Using Generalized Projection-Update Laws , 2011, IEEE Transactions on Fuzzy Systems.

[12]  U. Al-Saggaf,et al.  Three degrees of freedom rotary double inverted pendulum stabilization by using robust generalized dynamic inversion control: Design and experiments , 2020 .

[13]  Yan Peng,et al.  Stability analysis of linear systems with time-varying delay via intermediate polynomial-based functions , 2020, Autom..

[14]  Hak-Keung Lam,et al.  Stability and Stabilization With Additive Freedom for Delayed Takagi–Sugeno Fuzzy Systems by Intermediary-Polynomial-Based Functions , 2020, IEEE Transactions on Fuzzy Systems.

[15]  Vicente Feliu,et al.  Integral Resonant Control for Vibration Damping and Precise Tip-Positioning of a Single-Link Flexible Manipulator , 2011, IEEE/ASME Transactions on Mechatronics.

[16]  Ubaid M. Al-Saggaf,et al.  Rotary flexible joint control by fractional order controllers , 2017 .

[17]  Dong Hwan Kim,et al.  Robust Control Design for Flexible Joint Manipulators , 2006 .

[18]  Rachid Mansouri,et al.  Stabilization of a double inverted rotary pendulum through fractional order integral control scheme , 2019, International Journal of Advanced Robotic Systems.

[19]  Akira Inoue,et al.  VIBRATION CONTROL OF A FLEXIBLE ARM EXPERIMENTAL SYSTEM WITH HYSTERESIS OF PIEZOELECTRIC ACTUATOR , 2010 .

[20]  IbrahimĀ MustafaĀ Mehedi,et al.  State feedback based fractional order control scheme for linear servo cart system , 2018 .

[21]  Huaicheng Yan,et al.  Stability Analysis for Delayed Neural Networks via Improved Auxiliary Polynomial-Based Functions , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[22]  Dong Hwan Kim,et al.  Robust Control Design for Flexible Joint Manipulators : Theory and Experimental Verification , 2006 .

[23]  Manjit Kaur,et al.  Deep Neural Network-Based Screening Model for COVID-19-Infected Patients Using Chest X-Ray Images , 2020, Int. J. Pattern Recognit. Artif. Intell..

[24]  Rachid Mansouri,et al.  Smith Predictor Based Fractional‐Order‐Filter PID Controllers Design for Long Time Delay Systems , 2017 .

[25]  Amir A. Bature,et al.  Vibration and tip deflection control of a single-link flexible manipulator , 2013 .