Model predictive control (MPC) algorithm which can predict future states and generate accurate control sequences is adopted to control multiple measurement points collaboratively in a temperature system. To carry out the multiobjective optimization problem (MOP) according to MPC, multiobjective evolutionary algorithms based on decomposition (MOEA/D) and its improved version have been employed. The improved MOEA/D algorithm was compared with its original version through simulation, and the practical effectiveness is verified by tests on a self-made dual-point temperature control system. Finally, the improved MOEA/D algorithm was embedded into the control system of the experimental device, and temperature-tracking control experiments were conducted. The results show that the algorithm applied here can simultaneously control the temperature of two points and keep them close to the target temperature curve. It helps optimize the multiobjective control by virtue of MPC, and particularly opens up new possibilities to achieve optimal temperature control in different engineering applications.
AfaqMJebelliAAhmadR (2023) An intelligent thermal management fuzzy logic control system design and analysis using ANSYS fluent for a mobile robotic platform in extreme weather applications. Journal of Intelligent and Robotic Systems107(1): 11.
2.
AhmadMFIsaNAMLimWH, et al. (2022) Differential evolution: A recent review based on state-of-the-art works. Alexandria Engineering Journal61(5): 3831–3872.
3.
ÅströmKJEykhoffP (1971) System identification—A survey. Automatica7(2): 123–162.
4.
BoraseRPMaghadeDKSondkarSY, et al. (2020) A review of PID control, tuning methods and applications. International Journal of Dynamics and Control9(2): 818–827.
5.
BorreggineSMonopoliVGRizzelloG, et al. (2019) A review on model predictive control and its applications in power electronics. In: 2019 AEIT international conference of electrical and electronic technologies for automotive (AEIT AUTOMOTIVE), Turin, 2–4 July, pp. 1–6. New York: IEEE.
6.
ChenXFangYLiuQ, et al. (2022) Temperature and voltage dynamic control of PEMFC Stack using MPC method. Energy Reports8: 798–808.
7.
ChenXWangHChuJ, et al. (2021) Hybrid neighborhood and global replacement strategies for multi objective evolutionary algorithm based on decomposition. Evolutionary Intelligence15(3): 1715–1728.
8.
DebKGoyalM (1996) A combined genetic adaptive search (GeneAS) for engineering design. Computer Science and Informatics26: 30–45.
9.
DebKJainH (2012) Handling many-objective problems using an improved NSGA-II procedure. In: 2012 IEEE congress on evolutionary computation, Brisbane, QLD, Australia, 10–15 June, pp. 1–8. New York: IEEE.
10.
DebKPratapAAgarwalS, et al. (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation6(2): 182–197.
11.
FadhlianaNRSuryawanSHAriyadi (2021) Implementation of fuzzy logic on microcontroller for quails coop temperature control. In: 2021 4th international seminar on research of information technology and intelligent systems (ISRITI), Yogyakarta, Indonesia, 16–17 December, pp. 498–502. New York: IEEE.
12.
FaedoNOlayaSRingwoodJV (2017) Optimal control, MPC and MPC-like algorithms for wave energy systems: An overview. IFAC Journal of Systems and Control1: 37–56.
13.
HasimNArasMSMRashidMZA, et al. (2012) Development of fuzzy logic water bath temperature controller using MATLAB. In: 2012 IEEE international conference on control system, computing and engineering, Penang, Malaysia, 23–25 November, pp. 11–16. New York: IEEE.
14.
HuiLQingfuZ (2009) Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II. IEEE Transactions on Evolutionary Computation13(2): 284–302.
15.
ItoKHaraY (2017) Multipoint-measurement multipoint-heating greenhouse temperature control with wooden pellet fuel using an adaptive model predictive control approach with a genetic algorithm. In: 2017 25th Mediterranean conference on control and automation (MED), Valletta, 3–6 July, pp. 54–59. New York: IEEE.
16.
JamilAATuWFAliSW, et al. (2022) Fractional-order PID controllers for temperature control: A review. Energies15(10): 3800.
17.
LuWXiaWYeQ, et al. (2022) The design of high-precision temperature measurement circuit with time-sharing multiplexing. In: 2022 IEEE 5th international conference on electronics technology (ICET), Chengdu, China, 13–16 May, pp. 160–164. New York: IEEE.
18.
MayneDQ (2014) Model predictive control: Recent developments and future promise. Automatica50(12): 2967–2986.
19.
NguyenA-TTaniguchiTEciolazaL, et al. (2019) Fuzzy control systems: Past, present and future. IEEE Computational Intelligence Magazine14(1): 56–68.
20.
PantMZaheerHGarcia-HernandezL, et al. (2020) Differential evolution: A review of more than two decades of research. Engineering Applications of Artificial Intelligence90: 103479.
21.
ShamiTMEl-SalehAAAlswaittiM, et al. (2022) Particle swarm optimization: A comprehensive survey. IEEE Access10: 10031–10061.
22.
ShenLHeJYangC, et al. (2016) Temperature uniformity control of large-scale vertical quench furnaces for aluminum alloy thermal treatment. IEEE Transactions on Control Systems Technology24(1): 24–39.
23.
SunGLanYZhaoR (2017) Differential evolution with Gaussian mutation and dynamic parameter adjustment. Soft Computing23(5): 1615–1642.
24.
TalerDSobotaTJaremkiewiczM, et al. (2022) Control of the temperature in the hot liquid tank by using a digital PID controller considering the random errors of the thermometer indications. Energy239: 122771.
25.
TanabeRIshibuchiH (2019) Review and analysis of three components of the differential evolution mutation operator in MOEA/D-DE. Soft Computing23(23): 12843–12857.
26.
TrivediASrinivasanDSanyalK, et al. (2017) A survey of multiobjective evolutionary algorithms based on decomposition. IEEE Transactions on Evolutionary Computation21(3): 440–462.
27.
WangW-XLiK-STaoX-Z, et al. (2020) An improved MOEA/D algorithm with an adaptive evolutionary strategy. Information Sciences539: 1–15.
28.
WangZZhangQGongM, et al. (2014) A replacement strategy for balancing convergence and diversity in MOEA/D. In: 2014 IEEE congress on evolutionary computation (CEC), Beijing, China, 6–11 July, pp. 2132–2139. New York: IEEE.
29.
XuQXuZMaT (2020) A survey of multiobjective evolutionary algorithms based on decomposition: Variants, challenges and future directions. IEEE Access8: 41588–41614.
30.
YaoYShekharDK (2021) State of the art review on model predictive control (MPC) in Heating Ventilation and Air-Conditioning (HVAC) field. Building and Environment200: 107952.
31.
YinLGaoQ (2021) Multi-objective proportional–integral–derivative optimization algorithm for parameters optimization of double-fed induction generator-based wind turbines. Applied Soft Computing110: 107673.
32.
ZhangQLiH (2007) MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation11(6): 712–731.
33.
ZhangXYangLSunX, et al. (2023) ECMS-MPC Energy Management Strategy for Plug-In Hybrid Electric Buses Considering Motor Temperature Rise Effect. IEEE Transactions on Transportation Electrification9(1): 210–221.
34.
ZhangZJinY (2020) Design of temperature remote monitoring system based on STM32. In: 2020 IEEE international conference on artificial intelligence and computer applications (ICAICA), Dalian, China, 27–29 June, pp. 757–759. New York: IEEE.
35.
ZhouHQiaoJ (2018) Multiobjective optimal control for wastewater treatment process using adaptive MOEA/D. Applied Intelligence49(3): 1098–1126.
