Accelerating genetic optimization of nonlinear model predictive control
by learning optimal search space size
- URL: http://arxiv.org/abs/2305.08094v1
- Date: Sun, 14 May 2023 08:10:49 GMT
- Title: Accelerating genetic optimization of nonlinear model predictive control
by learning optimal search space size
- Authors: Eslam Mostafa, Hussein A. Aly, Ahmed Elliethy
- Abstract summary: This paper proposes an approach to accelerate the optimization of NMPC by learning optimal space size.
The proposed approach was compared on two nonlinear systems and compared with two other-based NMPC approaches.
- Score: 0.8057006406834467
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Nonlinear model predictive control (NMPC) solves a multivariate optimization
problem to estimate the system's optimal control inputs in each control cycle.
Such optimization is made more difficult by several factors, such as
nonlinearities inherited in the system, highly coupled inputs, and various
constraints related to the system's physical limitations. These factors make
the optimization to be non-convex and hard to solve traditionally. Genetic
algorithm (GA) is typically used extensively to tackle such optimization in
several application domains because it does not involve differential
calculation or gradient evaluation in its solution estimation. However, the
size of the search space in which the GA searches for the optimal control
inputs is crucial for the applicability of the GA with systems that require
fast response. This paper proposes an approach to accelerate the genetic
optimization of NMPC by learning optimal search space size. The proposed
approach trains a multivariate regression model to adaptively predict the best
smallest search space in every control cycle. The estimated best smallest size
of search space is fed to the GA to allow for searching the optimal control
inputs within this search space. The proposed approach not only reduces the
GA's computational time but also improves the chance of obtaining the optimal
control inputs in each cycle. The proposed approach was evaluated on two
nonlinear systems and compared with two other genetic-based NMPC approaches
implemented on the GPU of a Nvidia Jetson TX2 embedded platform in a
processor-in-the-loop (PIL) fashion. The results show that the proposed
approach provides a 39-53\% reduction in computational time. Additionally, it
increases the convergence percentage to the optimal control inputs within the
cycle's time by 48-56\%, resulting in a significant performance enhancement.
The source code is available on GitHub.
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