[1]
Rebellon, Harold E., et al, Thermoelectric modules: applications and opportunities in building environments for sustainable energy generation: from biomass, municipal waste, and other sources, Engineered Science 29 (2024): 1164.
Google Scholar
[2]
Akroot, Abdulrazzak, Mohamed Almaktar, and Feras Alasali, The Integration of Renewable Energy into a Fossil Fuel Power Generation System in Oil-Producing Countries: A Case Study of an Integrated Solar Combined Cycle at the Sarir Power Plant, Sustainability 16.11 (2024): 4820.
DOI: 10.3390/su16114820
Google Scholar
[3]
Rezania, Shahabaldin, et al, Review on waste-to-energy approaches toward a circular economy in developed and developing countries, Processes 11.9 (2023): 2566.
DOI: 10.3390/pr11092566
Google Scholar
[4]
Kachalla, Ibrahim Ali, and Christian Ghiaus, Electric Water Boiler Energy Prediction: State-of-the-Art Review of Influencing Factors, Techniques, and Future Directions, Energies 17.2 (2024): 443.
DOI: 10.3390/en17020443
Google Scholar
[5]
Bjelić, Draženko, et al, Waste to energy" as a driver towards a sustainable and circular energy future for the Balkan countries, Energy, Sustainability and Society 14.1 (2024): 3.
DOI: 10.1186/s13705-023-00435-y
Google Scholar
[6]
Kasiński, Sławomir, and Marcin Dębowski, Municipal Solid Waste as a Renewable Energy Source: Advances in Thermochemical Conversion Technologies and Environmental Impacts, Energies 17.18 (2024): 4704.
DOI: 10.3390/en17184704
Google Scholar
[7]
Hu, Zongyang, et al, Efficient model predictive control of boiler coal combustion based on NARX neutral network, Journal of Process Control 134 (2024): 103158.
DOI: 10.1016/j.jprocont.2023.103158
Google Scholar
[8]
Tavoosi, J., and A. Mohammadzadeh, A new recurrent radial basis function network-based model predictive control for a power plant boiler temperature control, International Journal of Engineering 34.3 (2021): 667-675.
DOI: 10.5829/ije.2021.34.03c.11
Google Scholar
[9]
Wang, Jun, Baocang Ding, and Ping Wang, Modeling and Finite-Horizon MPC for a Boiler-Turbine System Using Minimal Realization State-Space Model, Energies 15.21 (2022): 7935.
DOI: 10.3390/en15217935
Google Scholar
[10]
Tian, Hao, Jian Tang, and Tianzheng Wang, Furnace temperature model predictive control based on particle swarm rolling optimization for municipal solid waste incineration, Sustainability 16.17 (2024): 7670.
DOI: 10.3390/su16177670
Google Scholar
[11]
Lan, Jianglin, Efficient model predictive control for nonlinear systems modelled by deep neural networks, arXiv preprint arXiv:2405.10372 (2024).
Google Scholar
[12]
Kůdela, Jakub, et al, Optimal control of combined heat and power station operation, Optimization and Engineering 25.1 (2024): 121-145.
DOI: 10.1007/s11081-023-09848-2
Google Scholar
[13]
Zhang, Liang, et al, Challenges and opportunities of machine learning control in building operations, Building Simulation. Vol. 16. No. 6. Beijing: Tsinghua University Press, 2023.
Google Scholar
[14]
Saloux, Etienne, Jason Runge, and Kun Zhang, Operation optimization of multi-boiler district heating systems using artificial intelligence-based model predictive control: Field demonstrations, Energy 285 (2023): 129524.
DOI: 10.1016/j.energy.2023.129524
Google Scholar
[15]
Stanger, Lukas, et al, Model predictive control of a dual fluidized bed gasification plant, Applied Energy 361 (2024): 122917.
DOI: 10.1016/j.apenergy.2024.122917
Google Scholar
[16]
Tang, Jian, et al, An Overview of Artificial Intelligence Application for Optimal Control of Municipal Solid Waste Incineration Process, Sustainability 16.5 (2024): 2042.
DOI: 10.3390/su16052042
Google Scholar
[17]
Liu, Minghao, et al, A Chattering-Suppression Sliding Mode Controller for an Underwater Manipulator Using Time Delay Estimation, Journal of Marine Science and Engineering 11.9 (2023): 1742.
DOI: 10.3390/jmse11091742
Google Scholar
[18]
Gayvoronskiy, Sergey, Tatiana Ezangina, and Ivan Khozhaev, Parametric Synthesis of a Water Level Controller for a Boiler Unit on a Base of D-Partition in Vertices of aParametric Polytope, 2020 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM). IEEE, 2020.
DOI: 10.1109/icieam48468.2020.9111944
Google Scholar
[19]
Jha, R. S., and Mandar M. Lele, Dynamic modeling of a water tube boiler, Heat Transfer 51.7 (2022): 6087-6121.
DOI: 10.1002/htj.22581
Google Scholar
[20]
Gowthaman, E., et al, Performance analysis of hybrid fuzzy-PID controller action on boiler drum level control, 2016 Online International Conference on Green Engineering and Technologies (IC-GET). IEEE, 2016.
DOI: 10.1109/get.2016.7916709
Google Scholar
[21]
Yonezawa, Heisei, Ansei Yonezawa, and Itsuro Kajiwara, Experimental verification of model-free active damping system based on virtual controlled object and fuzzy sliding mode control, Mechanical Systems and Signal Processing 224 (2025): 111961.
DOI: 10.1016/j.ymssp.2024.111961
Google Scholar
[22]
Jan, Ahmed Zubair, Krzysztof Kedzia, and Muhammad Jamshed Abbass, Fractional-order PID controller (FOPID)-based iterative learning control for a nonlinear boiler system, Energies 16.3 (2023): 1045.
DOI: 10.3390/en16031045
Google Scholar
[23]
Perez, Anthony, and Yu Yang. Offset-free ARX-based adaptive model predictive control applied to a nonlinear process, ISA transactions 123 (2022): 251-262.
DOI: 10.1016/j.isatra.2021.05.030
Google Scholar
[24]
Tian, Xiaoying, and Pengfei Gao, RBF-ARX model-based fast robust predictive control strategy, Proceedings of the 2024 4th International Conference on Artificial Intelligence, Big Data and Algorithms. 2024.
DOI: 10.1145/3690407.3690508
Google Scholar
[25]
Peng, H., et al, The RBF-ARX Model Based Modeling And Predictive Control for a Class Of Nonlinear Processes, IFAC Proceedings Volumes 35.1 (2002): 443-448.
DOI: 10.3182/20020721-6-es-1901.00323
Google Scholar
[26]
Vasičkaninová, Anna, et al, Model predictive control of a tubular chemical reactor, 2019 22nd International Conference on Process Control (PC19). IEEE, 2019.
DOI: 10.1109/pc.2019.8815033
Google Scholar
[27]
Yang, Yang, et al, Modeling and control approach for dual clutch transmission vehicles starting process based on state-dependent autoregressive with exogenous model, IEEE Access 8 (2020): 158712-158726.
DOI: 10.1109/access.2020.3014162
Google Scholar
[28]
Tian, Binbin, and Hui Peng, RBF-ARX model-based MPC approach to inverted pendulum: An event-triggered mechanism, Chaos, Solitons & Fractals 176 (2023): 114081.
DOI: 10.1016/j.chaos.2023.114081
Google Scholar
[29]
De Giorgi, Maria Grazia, Luciano Strafella, and Antonio Ficarella, Neural nonlinear autoregressive model with exogenous input (NARX) for turboshaft aeroengine fuel control unit model, Aerospace 8.8 (2021): 206.
DOI: 10.3390/aerospace8080206
Google Scholar
[30]
Tian, Binbin, and Hui Peng, RBF-ARX model-based MPC approach to inverted pendulum: An event-triggered mechanism, Chaos, Solitons & Fractals 176 (2023): 114081.
DOI: 10.1016/j.chaos.2023.114081
Google Scholar
[31]
Nikentari, Nerfita, and H-L. Wei, Multi-task learning using non-linear autoregressive models and recurrent neural networks for tide level forecasting, International Journal of Electrical and Computer Engineering (IJECE) 14.1 (2024): 960-970.
DOI: 10.11591/ijece.v14i1.pp960-970
Google Scholar
[32]
Li, Lianhui, Adham Manyara, and Jie Liu, Structural parameter optimization of radial basis function neural network based on improved genetic algorithm and cost function model, Advances in Mechanical Engineering 16.11 (2024): 16878132241298190.
DOI: 10.1177/16878132241298190
Google Scholar
[33]
Kang, Tiao, et al, Deep Learning-Based State-Dependent ARX Modeling and Predictive Control of Nonlinear Systems, IEEE Access 11 (2023): 32579-32594.
DOI: 10.1109/access.2023.3263180
Google Scholar
[34]
Chacko, Sanjay Joseph, P. C. Neeraj, and Rajesh Joseph Abraham, Optimizing LQR controllers: A comparative study, Results in Control and Optimization 14 (2024): 100387.
DOI: 10.1016/j.rico.2024.100387
Google Scholar
[35]
Kumari, Soni, Madan Mohan Rayguru, and Shreyansh Upadhyaya, Analysis of Model Predictive Controller versus Linear Quadratic Regulator for DC-DC Buck Converter Systems, 2023 7th International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2023.
DOI: 10.1109/iciccs56967.2023.10142519
Google Scholar
[36]
Byrski, Witold, et al, Comparison of LQR with MPC in the adaptive stabilization of a glass conditioning process using soft-sensors for parameter identification and state observation, Control Engineering Practice 146 (2024): 105884.
DOI: 10.1016/j.conengprac.2024.105884
Google Scholar
[37]
Wu, Fangyu, et al, Composing MPC with LQR and neural network for amortized efficiency and stable control, IEEE Transactions on Automation Science and Engineering 21.2 (2023): 2088-2101.
DOI: 10.1109/tase.2023.3259428
Google Scholar
[38]
Tang, Jian, Hao Tian, and Tianzheng Wang, A Review of Model Predictive Control for the Municipal Solid Waste Incineration Process, Sustainability 16.17 (2024): 7650.
DOI: 10.3390/su16177650
Google Scholar