Simulation studies on online constraint removal with a Lyapunov function
Michael Jost, Gabriele Pannocchia, Martin M\"onnigmann
TL;DR
This paper evaluates the effectiveness of online constraint removal in accelerating model predictive control by testing 36 system-solver combinations with and without constraint removal, analyzing computational time improvements.
Contribution
It provides a comprehensive simulation-based comparison of constraint removal techniques across multiple MPC implementations and solvers, demonstrating potential computational benefits.
Findings
Constraint removal reduces computational time in MPC implementations.
Performance gains vary depending on the system and solver used.
The study offers insights into when constraint removal is most effective.
Abstract
We apply a recently proposed method for the acceleration of model predictive control (MPC) to 36 MPC implementations, which result from combining six sample receding horizon control problems with six quadratic programming solvers. We implement each of the 36 system-solver-combinations both with and without constraint removal and compare computational times for statistically relevant numbers of runs.
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Taxonomy
TopicsAdvanced Control Systems Optimization · Robotic Path Planning Algorithms · Scheduling and Optimization Algorithms