TY - JOUR
T1 - Grammatical-Evolution-based parameterized Model Predictive Control for urban traffic networks
AU - Jeschke, Joost
AU - Sun, Dingshan
AU - Jamshidnejad, Anahita
AU - De Schutter, Bart
PY - 2023
Y1 - 2023
N2 - 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%.
AB - 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%.
KW - Grammatical Evolution
KW - Model Predictive Control
KW - Parameterized controller
KW - Urban traffic control
UR - http://www.scopus.com/inward/record.url?scp=85146257958&partnerID=8YFLogxK
U2 - 10.1016/j.conengprac.2022.105431
DO - 10.1016/j.conengprac.2022.105431
M3 - Article
AN - SCOPUS:85146257958
VL - 132
JO - Control Engineering Practice
JF - Control Engineering Practice
SN - 0967-0661
M1 - 105431
ER -