A dual-mode model predictive controller for robot formations

Control of nonholonomic autonomous robots by using an input-output feedback linearization technique has been well explored. It has been noted that model predictive control (MPC) methods may have advantages over state feedback laws when applied to mobile robots, including consideration of constraints on inputs or state vectors. However, MPC algorithms require on-line optimization, resulting in a significant computational burden for large systems or formations of robots. In this paper, we develop a dual-model MPC algorithm which uses an input-output feedback linearization controller for states-within a specified terminal constraint set. Control of formations of nonholonomic robots in leader-follower configurations is simulated off-line, and performance characteristics of the dual-mode MPC controller are contrasted with those of the input-output feedback linearization controller alone.

[1]  Vijay Kumar,et al.  Cooperative Control of Robot Formations , 2002 .

[2]  Camillo J. Taylor,et al.  A vision-based formation control framework , 2002, IEEE Trans. Robotics Autom..

[3]  William B. Dunbar,et al.  Model predictive control of coordinated multi-vehicle formations , 2002, Proceedings of the 41st IEEE Conference on Decision and Control, 2002..

[4]  Vijay Kumar,et al.  Hybrid control of formations of robots , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[5]  A. J. Healey,et al.  Application of formation control for multi-vehicle robotic minesweeping , 2001, Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228).

[6]  David Q. Mayne,et al.  Constrained model predictive control: Stability and optimality , 2000, Autom..

[7]  Vijay Kumar,et al.  Controlling formations of multiple mobile robots , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[8]  D. Q. Mayne,et al.  Suboptimal model predictive control (feasibility implies stability) , 1999, IEEE Trans. Autom. Control..

[9]  Eduardo Camponogara,et al.  Distributed model predictive control , 2002 .

[10]  Jie Yu,et al.  Unconstrained receding-horizon control of nonlinear systems , 2001, IEEE Trans. Autom. Control..

[11]  D. Mayne,et al.  Robust receding horizon control of constrained nonlinear systems , 1993, IEEE Trans. Autom. Control..

[12]  Naomi Ehrich Leonard,et al.  Virtual leaders, artificial potentials and coordinated control of groups , 2001, Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228).