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Temporal Parallelization of Dynamic Programming and Linear Quadratic Control
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DOI：10.1109/TAC.2022.3147017.png)
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Abstract
En 中文
This article proposes a general formulation for temporal parallelization of dynamic programming for optimal control problems. We derive the elements and associative operators to be able to use parallel scans to solve these problems with logarithmic time complexity rather than linear time complexity. We apply this methodology to problems with finite state and control spaces, linear quadratic tracking control problems, and to a class of nonlinear control problems. The computational benefits of the parallel methods are demonstrated via numerical simulations run on a graphics processing unit.
Keywords:
Dynamic programming
Heuristic algorithms
Optimal control
Trajectory
Approximation algorithms
Time complexity
Cost function
Associative operator
dynamic programming
Index Terms
graphics processing unit (GPU)
multicore processing
optimal control
parallel computing
Journal
IF:
7
Papers:
1.3W
Citations:
6.7W