Tunnel boring machines (TBMs) are usually the first choice for tunneling construction with its advantages on high safety, time saving, and less operators. Cutterhead system is an important component for TBMs since it is used to excavate rocks and soil. It is difficult to guarantee both the boring efficiency and energy saving under the excavating rock disturbances and the constraints on the driving motors in TBMs by manual operation. To deal with this problem, it is necessary to develop advanced control algorithms for the cutterhead system. Thus, we investigate an economic model predictive control (EMPC) structure for cutterhead system in TBMs. A generalized nonlinear dynamic model of TBM cutterhead system is built based on the first principle method. An economic cost is constructed with the boring efficiency and energy cost to evaluate the tunnel construction quality. EMPC algorithms are designed to optimize the constructed economic cost for a cutterhead system to guarantee good economic performance. It is shown that EMPC can improve the economic performance of the cutterhead system.
AlbalawiFDurandHChristofidesPD (2017) Distributed economic model predictive control for operational safety of nonlinear processes. AIChE Journal63(8): 3404–3418.
2.
EllisMDurandHChristofidesPD (2014) A tutorial review of economic model predictive control methods. Journal of Process Control24(8): 1156–1178.
3.
FuJFuZSongS (2021) Model predictive control-based control strategy to reduce driving-mode switching times for parallel hybrid electric vehicle. Transactions of the Institute of Measurement and Control43(1): 167–177.
4.
GhorbaniSSafaviAANaghaviSV (2021) Event-triggered robust model predictive control for Lipschitz nonlinear networked control systems subject to communication delays. Transactions of the Institute of Measurement and Control43(5): 1126–1142.
5.
HeDQiuTLuoR (2020) Fuel efficiency-oriented platooning control of connected nonlinear vehicles: A distributed economic MPC approach. Asian Journal of Control22(4): 1628–1638.
6.
HuoJHouNSunW, et al. (2017) Analyses of dynamic characteristics and structure optimization of tunnel boring machine cutter system with multi-joint surface. Nonlinear Dynamics87(1): 237–254.
7.
HuoJSunWChenJ, et al. (2011) Disc cutters plane layout design of the full-face rock tunnel boring machine (TBM) based on different layout patterns. Computers & Industrial Engineering61(4): 1209–1225.
8.
JiaYMengKWuK, et al. (2020) Optimal load frequency control for networked power systems based on distributed economic MPC. IEEE Transactions on Systems, Man, and Cybernetics: Systems51(4): 2123–2133.
9.
LiXYuHZengP, et al. (2014) Study on linear vibration model of shield TBM cutterhead driving system. International Journal of Acoustics and Vibrations19(2): 89–106.
10.
LiXHYuHBYuanMZ, et al. (2010) Dynamic modeling and analysis of shield TBM cutterhead driving system. Journal of Dynamic Systems, Measurement, and Control132: 044504.
11.
LiaoJChenZYaoB (2017) High-performance adaptive robust control with balanced torque allocation for the over-actuated cutter-head driving system in tunnel boring machine. Mechatronics46: 168–176.
12.
LingJSunWHuoJ, et al. (2015) Study of TBM cutterhead fatigue crack propagation life based on multi-degree of freedom coupling system dynamics. Computers & Industrial Engineering83: 1–14.
13.
LiuSZhangJLiuJ (2015) Economic MPC with terminal cost and application to an oilsand primary separation vessel. Chemical Engineering Science136: 27–37.
14.
MaJQinSJSalsburyT (2014) Application of economic MPC to the energy and demand minimization of a commercial building. Journal of Process Control24(8): 1282–1291.
15.
MoetamedzadehHRKhanmirzaEPourfardA, et al. (2019) Intelligent nonlinear model predictive control of gas pipeline networks. Transactions of the Institute of Measurement and Control41(16): 4569–4589.
16.
QiZLuoQZhangH (2022) A tube-based robust MPC for duty-cycled rotation needle steering systems with bounded disturbances. Transactions of the Institute of Measurement and Control44(4): 960–970.
17.
RostamiJ (2016) Performance prediction of hard rock tunnel boring machines (TBMs) in difficult ground. Tunnelling and Underground Space Technology57: 173–182.
18.
ScattoliniR (2009) Architectures for distributed and hierarchical model predictive control—A review. Journal of Process Control19(5): 723–731.
19.
ShengquanLJuanLYongweiT, et al. (2020) Model-based model predictive control for a direct-driven permanent magnet synchronous generator with internal and external disturbances. Transactions of the Institute of Measurement and Control42(3): 586–597.
20.
TouretzkyCRBaldeaM (2014) Integrating scheduling and control for economic MPC of buildings with energy storage. Journal of Process Control24(8): 1292–1300.
21.
WangHWangJZhaoY, et al. (2021a) Tunneling parameters optimization based on multi-objective differential evolution algorithm. Soft Computing25(5): 3637–3656.
22.
WangLKangYZhaoX, et al. (2015) Disc cutter wear prediction for a hard rock TBM cutterhead based on energy analysis. Tunnelling and Underground Space Technology50: 324–333.
23.
WangXZhuHZhuM, et al. (2021b) An integrated parameter prediction framework for intelligent TBM excavation in hard rock. Tunnelling and Underground Space Technology118: 104196.
24.
ZhangLWangBLiY, et al. (2021a) Distributed stochastic model predictive control for cyber–physical systems with multiple state delays and probabilistic saturation constraints. Automatica129: 109574.
25.
ZhangLWangBZhengY, et al. (2022) Robust packetized MPC for networked systems subject to packet dropouts and input saturation with quantized feedback. IEEE Transactions on Cybernetics53(11): 6987–6997.
26.
ZhangLXieWZhongZ, et al. (2018) Robust model predictive control synthesis for state-delayed systems with randomly occurring input saturation nonlinearities. Transactions of the Institute of Measurement and Control40(1): 179–190.
27.
ZhangQHuWLiuZ, et al. (2020) TBM performance prediction with Bayesian optimization and automated machine learning. Tunnelling and Underground Space Technology103: 103493.
28.
ZhangQQuCKangY, et al. (2012) Identification and optimization of energy consumption by shield tunnel machines using a combined mechanical and regression analysis. Tunnelling and Underground Space Technology28: 350–354.
29.
ZhangYZhengYLiS, et al. (2021b) Economic model predictive control for the operation optimization of water distribution networks with risks. Asian Journal of Control23(1): 128–142.
30.
ZhouJQiuYZhuS, et al. (2021) Estimation of the TBM advance rate under hard rock conditions using XGBoost and Bayesian optimization. Underground Space6(5): 506–515.
