Performance improvement of flexible robot using combined observer-controller and particle swarm optimization

ABSTRACT. The aim of this study is to examine the robust control design based on coefficient diagram method with backstepping control combined with an observer for position control of the flexible joint manipulator. A simulation model with stability analysis was established where the parameters of the observer-controller are tuned by means of particle swarm optimization. Through this study, it was found that the proposed control scheme is effective, and the results indicate that ours approach ensures the asymptotic convergence of the actual joints positions to theirs desired trajectory, and robustness where the system is subjected to external disturbance and parameters uncertainties.

[1]  D. Ouoba,et al.  Non-linear command of wind turbine based on doubly-field induction generator , 2016, 2016 International Conference on Electrical Sciences and Technologies in Maghreb (CISTEM).

[2]  Shaoming He,et al.  Robust backstepping control for a class of nonlinear systems using generalized disturbance observer , 2016 .

[3]  Won-Sook Lee,et al.  Multi-objective design of state feedback controllers using reinforced quantum-behaved particle swarm optimization , 2016, Appl. Soft Comput..

[4]  Aziz Derouich,et al.  Observer backstepping control of DFIG-Generators for wind turbines variable-speed: FPGA-based implementation , 2015 .

[5]  Yasunori Mitani,et al.  Decentralized load frequency control in an interconnected power system using Coefficient Diagram Method , 2014 .

[6]  D. Wagg,et al.  Nonlinear robust observer design using an invariant manifold approach , 2016 .

[7]  Ahmed A. Zaki Diab,et al.  Application of Linear Quadratic Gaussian and Coefficient Diagram Techniques to Distributed Load Frequency Control of Power Systems , 2015 .

[8]  Kyoung Kwan Ahn,et al.  Integrated model-based backstepping control for an electro-hydraulic system , 2016 .

[9]  Yushu Bian,et al.  Parameter optimization of controllable local degree of freedom for reducing vibration of flexible manipulator , 2013 .

[10]  Ekhlas Hameed Karam,et al.  Design and Implementation of Fuzzy PID Controller for Single Link Flexible Joint Robotic System using FPGA , 2014 .

[11]  Yukinori Kobayashi,et al.  Approaches based on particle swarm optimization for problems of vibration reduction of suspended mobile robot with a manipulator , 2014 .

[12]  Igor Furtat,et al.  Modified backstepping algorithm for nonlinear systems , 2016, Autom. Remote. Control..

[13]  Kiyoshi Ohishi,et al.  Design for Sensorless Force Control of Flexible Robot by Using Resonance Ratio Control Based on Coefficient Diagram Method , 2013 .

[14]  Gang Tao,et al.  A novel nonlinear resilient control for a quadrotor UAV via backstepping control and nonlinear disturbance observer , 2016 .

[15]  Hossam Ali,et al.  Distributed load frequency control in an interconnected power system using ecological technique and coefficient diagram method , 2016 .

[16]  Chih-Chiang Cheng,et al.  Block backstepping control of multi-input nonlinear systems with mismatched perturbations for asymptotic stability , 2010, Int. J. Control.

[17]  Xingjian Jing,et al.  Adaptive fuzzy backstepping dynamic surface control for nonlinear Input-delay systems , 2016, Neurocomputing.

[18]  Mostafa A. El-Hosseini,et al.  Design of optimal PID controller using hybrid differential evolution and particle swarm optimization with an aging leader and challengers , 2016, Appl. Soft Comput..

[19]  N. K. Jain,et al.  A Review of Particle Swarm Optimization , 2018, Journal of The Institution of Engineers (India): Series B.

[20]  Tzuu-Hseng S. Li,et al.  Adaptive neural network based tracking control for electrically driven flexible-joint robots without velocity measurements , 2012, Comput. Math. Appl..