Reference:
J. Jeschke,
D. Sun,
A. Jamshidnejad, and
B. De Schutter,
"Grammatical-evolution-based parameterized model predictive control
for urban traffic networks," Control Engineering Practice,
vol. 132, p. 105431, Mar. 2023.
Abstract:
While Model Predictive Control (MPC) is a promising approach for
network-wide control of urban traffic, the computational complexity of
the, often nonlinear, online optimization procedure is too high for
real-time implementations. In order to make MPC computationally
efficient, this paper introduces a parameterized MPC (PMPC)
approach for urban traffic networks that uses Grammatical Evolution to
construct continuous parameterized control laws using an effective
simulation-based training framework. Furthermore, a projection-based
method is proposed to remove the nonlinear constraints that are
imposed on the parameters of the parameterized control laws and to
guarantee the feasibility of the solution of the MPC optimization
problem. The performance and computational efficiency of the
constructed parameterized control laws are compared to those of a
conventional MPC controller in an extensive simulation-based case
study. The results show that the parameterized control laws, which are
automatically constructed using Grammatical Evolution, decrease the
computational complexity of the online optimization problem by more
than 80% with a decrease in performance by less than 10%.
Bibtex entry:
@article{JesSun:23-015,