Reference:
J. Jeschke and
B. De Schutter,
"Parametrized model predictive control approaches for urban traffic
networks," Proceedings of the 16th IFAC Symposium on Control in
Transportation Systems (CTS 2021), Lille, France, pp. 284-291,
June 2021.
Abstract:
Model Predictive Control (MPC) has shown promising results in the
control of urban traffic networks, but unfortunately it has one major
drawback. The, often nonlinear, optimization that has to be performed
at every control time step is computationally too complex to use MPC
controllers for real-time implementations (i.e. when the online
optimization is performed within the control time interval of the
controlled network). This paper proposes an effective parametrized MPC
control approach to lower the computational complexity of the MPC
controller. Two parametrized control laws are proposed that can be
used in the parametrized MPC framework, one based on the prediction
model of the MPC controllers, and another is constructed using
Grammatical Evolution (GE). The performance and computational
complexity of the parametrized MPC approach is compared to a
conventional MPC controller by performing an extensive
simulation-based case study. The simulation results show that for the
given case study the parametrized MPC approach is real-time
implementable while the performance decreases with less than 3% with
respect to the conventional MPC controller.