Randomized Receding Horizon Navigation

The note combines (weak) control Lyapunov function-based nonlinear receding horizon control, with randomized optimization. This approach is applied to the problem of robot navigation in the presence of state and input constraints. It is shown that under certain conditions, relaxing the definiteness...

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Bibliographic Details
Published in:IEEE transactions on automatic control 2010-11, Vol.55 (11), p.2640-2644
Main Authors: Tanner, Herbert G, Piovesan, Jorge L
Format: Article
Language:English
Subjects:
Applied sciences
Computer science
control theory
systems
Constraint optimization
Control system synthesis
Control systems
Control theory. Systems
Cost function
Exact sciences and technology
Horizon
Lyapunov method
Model predictive control (MPC)
Navigation
Nonlinearity
Optimal control
Optimization
Optimization methods
Predictive control
Preserves
randomized algorithms
robot navigation
Robotics
Robots
Robust stability
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Summary:The note combines (weak) control Lyapunov function-based nonlinear receding horizon control, with randomized optimization. This approach is applied to the problem of robot navigation in the presence of state and input constraints. It is shown that under certain conditions, relaxing the definiteness requirements on the terminal cost function allows one to select control inputs through a Monte-Carlo optimization scheme in a way that preserves the stability and convergence properties of the closed loop system. While the particular randomized optimization scheme used here can be substituted for the nonlinear optimal control method of choice, the introduction of randomization in receding horizon optimization is anticipated to offer additional trade-offs between performance and computation speed compared to the fixed-overhead nonlinear optimal control strategies typically employed.
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2010.2063291