Design and experimental evaluation of robust controllers for a two-wheeled robot

ABSTRACT The paper presents the design and experimental evaluation of two alternative μ-controllers for robust vertical stabilisation of a two-wheeled self-balancing robot. The controllers design is based on models derived by identification from closed-loop experimental data. In the first design, a signal-based uncertainty representation obtained directly from the identification procedure is used, which leads to a controller of order 29. In the second design the signal uncertainty is approximated by an input multiplicative uncertainty, which leads to a controller of order 50, subsequently reduced to 30. The performance of the two μ-controllers is compared with the performance of a conventional linear quadratic controller with 17th-order Kalman filter. A proportional-integral controller of the rotational motion around the vertical axis is implemented as well. The control code is generated using Simulink® controller models and is embedded in a digital signal processor. Results from the simulation of the closed-loop system as well as experimental results obtained during the real-time implementation of the designed controllers are given. The theoretical investigation and experimental results confirm that the closed-loop system achieves robust performance in respect to the uncertainties related to the identified robot model.

[1]  Toshiyuki Murakami,et al.  Parameter Design of Disturbance Observer for a Robust Control of Two-Wheeled Wheelchair System , 2015, J. Intell. Robotic Syst..

[2]  Mi-Ching Tsai,et al.  Design of Robust Stabilization and Fault Diagnosis for an Auto-balancing Two-Wheeled Cart , 2008, Adv. Robotics.

[3]  S.W. Nawawi,et al.  Multi input single output closed loop identification of two wheel inverted pendulum mobile robot , 2011, 2011 IEEE Student Conference on Research and Development.

[4]  Her-Terng Yau,et al.  Robust Control Method Applied in Self-Balancing Two-Wheeled Robot , 2009, 2009 Second International Symposium on Knowledge Acquisition and Modeling.

[5]  Tong Heng Lee,et al.  Design and Implementation of a Takagi–Sugeno-Type Fuzzy Logic Controller on a Two-Wheeled Mobile Robot , 2013, IEEE Transactions on Industrial Electronics.

[6]  Yufeng Zhuang,et al.  Two-wheeled self-balancing robot dynamic model and controller design , 2014, Proceeding of the 11th World Congress on Intelligent Control and Automation.

[7]  Naoya Hatakeyama,et al.  Movement Control of Two-wheeled Inverted Pendulum Robots Considering Robustness , 2008 .

[8]  S.W. Nawawi,et al.  Dynamic Modeling and Analysis of a Two-Wheeled Inverted Pendulum Robot , 2011, 2011 Third International Conference on Computational Intelligence, Modelling & Simulation.

[9]  Ching-Chang Wong,et al.  Fuzzy Controller Designed by GA for Two-wheeled Mobile Robots , 2007 .

[10]  J. Doyle,et al.  Robust and optimal control , 1995, Proceedings of 35th IEEE Conference on Decision and Control.

[11]  M. Osman Tokhi,et al.  Design and real-time implementation of a fuzzy logic control system for a two-wheeled robot , 2012, 2012 17th International Conference on Methods & Models in Automation & Robotics (MMAR).

[12]  Congying Qiu.  The Design of Fuzzy Adaptive PID Controller of Two-Wheeled Self-Balancing Robot , 2015 .

[13]  Arun D. Mahindrakar,et al.  Position Stabilization and Waypoint Tracking Control of Mobile Inverted Pendulum Robot , 2014, IEEE Transactions on Control Systems Technology.

[14]  Chenxi Sun,et al.  Balance control of two-wheeled self-balancing robot based on Linear Quadratic Regulator and Neural Network , 2013, 2013 Fourth International Conference on Intelligent Control and Information Processing (ICICIP).

[15]  M. M. Azimi,et al.  Model predictive control for a two wheeled self balancing robot , 2013, 2013 First RSI/ISM International Conference on Robotics and Mechatronics (ICRoM).

[16]  George Kantor,et al.  Centrifugal force compensation of a two-wheeled balancing robot , 2010, 2010 11th International Conference on Control Automation Robotics & Vision.

[17]  Alex Simpkins,et al.  System Identification: Theory for the User, 2nd Edition (Ljung, L.; 1999) [On the Shelf] , 2012, IEEE Robotics & Automation Magazine.

[18]  Jian Fang.  The LQR Controller Design of Two-Wheeled Self-Balancing Robot Based on the Particle Swarm Optimization Algorithm , 2014 .

[19]  Guilherme V. Raffo,et al.  Two-wheeled self-balanced pendulum workspace improvement via underactuated robust nonlinear control , 2015 .

[20]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[21]  Mi-Ching Tsai,et al.  Robust and Optimal Control , 2014 .

[22]  R. Avila,et al.  Stabilization of a Wheeled Inverted Pendulum by a Continuous-Time Infinite-Horizon LQG Optimal Controller , 2008, 2008 IEEE Latin American Robotic Symposium.

[23]  Seul Jung,et al.  Balancing and navigation control of a mobile inverted pendulum robot using sensor fusion of low cost sensors , 2012 .

[24]  Junfeng Wu,et al.  A robust control method of two-wheeled self-balancing robot , 2011, Proceedings of 2011 6th International Forum on Strategic Technology.

[25]  Karl A. Stol,et al.  Review of modelling and control of two-wheeled robots , 2013, Annu. Rev. Control..

[26]  Thierry Marie Guerra,et al.  Modeling, control and experimental verification on a two-wheeled vehicle with free inclination: An urban transportation system , 2011 .

[27]  Sophan Wahyudi Nawawi,et al.  Real-time control system for a two-wheeled inverted pendulum mobile robot , 2010 .

[28]  Xiaogang Ruan,et al.  H∞ robust control of Self-Balancing Two-Wheeled Robot , 2010, 2010 8th World Congress on Intelligent Control and Automation.

[29]  Yunong Zhang,et al.  Support vector machine optimal control for mobile wheeled inverted pendulums with unmodelled dynamics , 2010, Neurocomputing.

[30]  Chun-Jung Chen,et al.  Motion control for a two-wheeled vehicle using a self-tuning PID controller , 2008 .