Nonlinear Disturbance Attenuation Control of Hydraulic Robotics

This paper presents a novel nonlinear disturbance rejection control for hydraulic robots. This method requires two third-order filters as well as inverse dynamics in order to estimate the disturbances. All the parameters for the third-order filters are pre-defined. The proposed method is nonlinear, which does not require the linearization of the rigid body dynamics. The estimated disturbances are used by the nonlinear controller in order to achieve disturbance attenuation. The performance of the proposed approach is compared with existing approaches. Finally, the tracking performance and robustness of the proposed approach is validated extensively on real hardware by performing different tasks under either internal or both internal and external disturbances. The experimental results demonstrate the robustness and superior tracking performance of the proposed approach.

[1]  V. Utkin Variable structure systems with sliding modes , 1977 .

[2]  Oussama Khatib,et al.  A unified approach for motion and force control of robot manipulators: The operational space formulation , 1987, IEEE J. Robotics Autom..

[3]  Weiping Li,et al.  Applied Nonlinear Control , 1991 .

[4]  Phillip J. McKerrow,et al.  Introduction to robotics , 1991 .

[5]  Lakmal Seneviratne,et al.  Adaptive Control Of Robot Manipulators , 1992, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems.

[6]  Peter J. Gawthrop,et al.  A nonlinear disturbance observer for robotic manipulators , 2000, IEEE Trans. Ind. Electron..

[7]  Jon Rigelsford,et al.  Modelling and Control of Robot Manipulators , 2000 .

[8]  Roy Featherstone,et al.  Rigid Body Dynamics Algorithms , 2007 .

[9]  Jun Nakanishi,et al.  Operational Space Control: A Theoretical and Empirical Comparison , 2008, Int. J. Robotics Res..

[10]  Jian Chen,et al.  Robust Feedback Control for a Class of Uncertain MIMO Nonlinear Systems , 2008, IEEE Transactions on Automatic Control.

[11]  Long Cheng,et al.  Adaptive neural network tracking control for manipulators with uncertain kinematics, dynamics and actuator model , 2009, Autom..

[12]  Stefan Schaal,et al.  Inverse dynamics control of floating base systems using orthogonal decomposition , 2010, 2010 IEEE International Conference on Robotics and Automation.

[13]  Darwin G. Caldwell,et al.  Dynamic torque control of a hydraulic quadruped robot , 2012, 2012 IEEE International Conference on Robotics and Automation.

[14]  Mahdi Tavakoli,et al.  Nonlinear Disturbance Observer Design For Robotic Manipulators , 2013 .

[15]  Sarah Spurgeon Sliding mode control: a tutorial , 2014, 2014 European Control Conference (ECC).

[16]  Xinghuo Yu,et al.  Chattering free full-order sliding-mode control , 2014, Autom..

[17]  E. Kampen,et al.  Selective-Reinitialization Multiple-Model Adaptive Estimation for Fault Detection and Diagnosis , 2015 .

[18]  Wan Kyun Chung,et al.  Disturbance-Observer-Based PD Control of Flexible Joint Robots for Asymptotic Convergence , 2015, IEEE Transactions on Robotics.

[19]  Darwin G. Caldwell,et al.  DESIGN OF A HYDRAULICALLY ACTUATED ARM FOR A QUADRUPED ROBOT , 2015 .

[20]  Peng Lu,et al.  Aircraft Fault-Tolerant Trajectory Control Using Incremental Nonlinear Dynamic Inversion , 2016 .

[21]  Darwin G. Caldwell,et al.  RobCoGen: a code generator for efficient kinematics and dynamics of articulated robots, based on Domain Specific Languages , 2016 .

[22]  Peng Lu,et al.  Framework for state and unknown input estimation of linear time-varying systems , 2016, Autom..

[23]  E. Kampen,et al.  Framework for Simultaneous Sensor and Actuator Fault-Tolerant Flight Control , 2017 .

[24]  Kyoungchul Kong,et al.  High-Precision Robust Force Control of a Series Elastic Actuator , 2017, IEEE/ASME Transactions on Mechatronics.