Sparse in the Time Stabilization of a Bicycle Robot Model: Strategies for Event- and Self-Triggered Control Approaches

In this paper, the problems of event- and self-triggered control are studied for a nonlinear bicycle robot model. It has been shown that by applying control techniques based on triggering conditions, it is possible to reduce both state-based performance index, as well as the number of triggers, in comparison to a standard linear-quadratic control which consumes less energy of the control system and decreases the potential mechanical wear of the robot parts. The results presented in this paper open a new research field for further studies, as discussed in the Summary section, and form the basis for further research in energy-efficient control techniques for stabilizing a bicycle robot.

[1]  Huibert Kwakernaak,et al.  Linear Optimal Control Systems , 1972 .

[2]  Jan M. Maciejowski,et al.  Predictive control : with constraints , 2002 .

[3]  K.J. Astrom,et al.  Bicycle dynamics and control: adapted bicycles for education and research , 2005, IEEE Control Systems.

[4]  Dariusz Horla,et al.  Performance of Feedback Linearization Based Control of Bicycle Robot in Consideration of Model Inaccuracy , 2016, AUTOMATION.

[5]  Dariusz Horla,et al.  Robust LQR with actuator failure control strategies for 4DoF model of unmanned bicycle robot stabilised by inertial wheel , 2015, 2015 International Conference on Industrial Engineering and Systems Management (IESM).

[6]  Dimos V. Dimarogonas,et al.  Self-Triggered Model Predictive Control for Nonlinear Input-Affine Dynamical Systems via Adaptive Control Samples Selection , 2016, IEEE Transactions on Automatic Control.

[7]  E.S. Tehrani,et al.  Dynamic neural network-based estimator for fault diagnosis in reaction wheel actuator of satellite attitude control system , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[8]  Dariusz Horla,et al.  Robust Actuator Fault-Tolerant LQR Control of Unmanned Bicycle Robot. A Feedback Linearization Approach , 2016, AUTOMATION.

[9]  Dimos V. Dimarogonas,et al.  A self-triggered visual servoing model predictive control scheme for under-actuated underwater robotic vehicles , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[10]  Tony Hadland,et al.  Bicycle Design: An Illustrated History , 2014 .

[11]  L. Behera,et al.  On balancing control strategies for a reaction wheel pendulum , 2004, Proceedings of the IEEE INDICON 2004. First India Annual Conference, 2004..

[12]  Huiping Li,et al.  Self-triggered distributed model predictive control of nonholonomic systems , 2017, 2017 11th Asian Control Conference (ASCC).

[13]  Eduardo D. Sontag,et al.  Neural Networks for Control , 1993 .

[14]  Dariusz Horla,et al.  A Comparison of Control Strategies for 4DoF Model of Unmanned Bicycle Robot Stabilised by Inertial Wheel , 2015, Progress in Automation, Robotics and Measuring Techniques.

[15]  W. P. M. H. Heemels,et al.  Robust self-triggered MPC for constrained linear systems , 2014, 2014 European Control Conference (ECC).

[16]  Karl Johan Åström,et al.  The Reaction Wheel Pendulum , 2007, The Reaction Wheel Pendulum.

[17]  Paulo Tabuada,et al.  An introduction to event-triggered and self-triggered control , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[18]  Nicolas Marchand,et al.  Event-Based Control of the Inverted Pendulum: Swing up and Stabilization , 2013 .

[19]  W. P. M. H. Heemels,et al.  Periodic Event-Triggered Control for Linear Systems , 2013, IEEE Trans. Autom. Control..

[20]  Dariusz Horla,et al.  Introduction of Feedback Linearization to Robust LQR and LQI Control – Analysis of Results from an Unmanned Bicycle Robot with Reaction Wheel , 2019 .

[21]  Wpmh Maurice Heemels,et al.  Self-triggered MPC for constrained linear systems and quadratic costs , 2012 .

[22]  Paulo Tabuada,et al.  Self-triggered linear quadratic control , 2014, Autom..

[23]  D. Herlihy.  Bicycle: The History , 2004 .

[24]  Dariusz Horla,et al.  Robust LQR and LQI control with actuator failure of a 2DOF unmanned bicycle robot stabilized by an inertial wheel , 2016, Int. J. Appl. Math. Comput. Sci..

[25]  Dariusz Horla,et al.  Analysis of feedback vs. Jacobian linearization approaches applied to robust LQR control of 2DoF bicycle robot model , 2016, 2016 17th International Conference on Mechatronics - Mechatronika (ME).

[26]  Qing Gao.  Universal Fuzzy Controllers for Non-affine Nonlinear Systems , 2016 .

[27]  Zhong-Ping Jiang,et al.  Event-based control of nonlinear systems with partial state and output feedback , 2015, Autom..

[28]  Joseph Norwood,et al.  Twentieth century physics , 1976 .

[29]  Michael Muehlebach,et al.  Nonlinear Analysis and Control of a Reaction-Wheel-Based 3-D Inverted Pendulum , 2017, IEEE Transactions on Control Systems Technology.

[30]  Farbod Fahimi,et al.  Autonomous Robots: Modeling, Path Planning, and Control , 2008 .