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NN-based Prediction Interval for Nonlinear Processes Controller

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  • Intelligent Control and Applications
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Abstract

Neural networks (NNs) are extensively used in modelling, optimization, and control of nonlinear plants. NN-based inverse type point prediction models are commonly used for nonlinear process control. However, prediction errors (root mean square error (RMSE), mean absolute percentage error (MAPE) etc.) significantly increase in the presence of disturbances and uncertainties. In contrast to point forecast, prediction interval (PI)-based forecast bears extra information such as the prediction accuracy. The PI provides tighter upper and lower bounds with considering uncertainties due to the model mismatch and time dependent or time independent noises for a given confidence level. The use of PIs in the NN controller (NNC) as additional inputs can improve the controller performance. In the present work, the PIs are utilized in control applications, in particular PIs are integrated in the NN internal model-based control framework. A PI-based model that developed using lower upper bound estimation method (LUBE) is used as an online estimator of PIs for the proposed PI-based controller (PIC). PIs along with other inputs for a traditional NN are used to train the PIC to predict the control signal. The proposed controller is tested for two case studies. These include, a chemical reactor, which is a continuous stirred tank reactor (case 1) and a numerical nonlinear plant model (case 2). Simulation results reveal that the tracking performance of the proposed controller is superior to the traditional NNC in terms of setpoint tracking and disturbance rejections. More precisely, 36% and 15% improvements can be achieved using the proposed PIC over the NNC in terms of IAE for case 1 and case 2, respectively for setpoint tracking with step changes.

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Authors and Affiliations

  1. Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, Australia

    Mohammad Anwar Hosen, Abbas Khosravi, H. M. Dipu Kabir, Michael Johnstone, Douglas Creighton & Saeid Nahavandi

  2. School of Electrical and Electronic Engineering, University of Adelaide, Adelaide, Australia

    Peng Shi

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  1. Mohammad Anwar Hosen
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  2. Abbas Khosravi
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  3. H. M. Dipu Kabir
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  6. Saeid Nahavandi
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  7. Peng Shi
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Correspondence to Mohammad Anwar Hosen.

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This research was fully supported by the Institute for Intelligent Systems Research and Innovation (IISRI) at Deakin University, Australia.

Mohammad Anwar Hosen received his B.Sc.(Hons.) degree in chemical engineering from the Bangladesh University of Engineering and Technology, Dhaka, Bangladesh, in 2004, an M.Eng.Sc. degree in chemical engineering from the University of Malaya, Kuala Lumpur, Malaysia, in 2010 and a Ph.D. degree from the Deakin University, Waurn Ponds, Australia, in 2015. He joined to IISRI as a Research Fellow just after completing his Ph.D. degree in 2015. His current research interests include the development and application of AI techniques for modelling and control of nonlinear systems, uncertainty quantification, analytical modelling and process simulation.

Abbas Khosravi received his Ph.D. degree from Deakin University, Australia in 2010. He is currently an associate professor in the Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University. His research interests include the development and application of artificial intelligence techniques for data analysis, pattern recognition, uncertainty quantification, and optimisation.

H. M. Dipu Kabir received his B.Sc. degree from the Department of EEE, Bangladesh University of Engineering and Technology, in 2011. He received an M.Phil. degree from the Department of ECE, the Hong Kong University of Science and Technology, in 2016. He received a Ph.D. degree from the Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, in 2020. He was with the Samsung Bangladesh R&D Center as a Software Engineer from 2011 to 2013. He is currently working as a Research Fellow at Deakin University.

Michael Johnstone received his B.Eng. and Ph.D. degrees from Deakin University, Australia, in 2000 and 2010, respectively. He is currently an Associate Professor at the Institute for Intelligent Systems Research and Innovation at Deakin University. His main research interests are in systems dynamics, agent-based modelling and discrete event simulation for decision support.

Douglas Creighton is a Professor of Systems Engineering at Deakin University and Deputy Director of the Institute of Intelligent Systems Research and Innovation. He received a Bachelor of Systems Engineering and a Bachelor of Science (Physics) from the Australian National University and a Ph.D. degree in industrial engineering/AI from Deakin University. His expertise in modelling and simulation, data analytics and systems thinking is used to inform system design, and strategic and operational decision making for today’s complex infrastructure systems. His research interests include algorithms and methodologies to improve estimation and performance of complex systems operating under uncertainty, variability, and continuous change. He has a 20-year track record working with industry and government in transport, manufacturing, logistics, defence and government, and fostering cross sector innovation.

Saeid Nahavandi received his B.Sc. (Hons.), M.Sc., and Ph.D. degrees in control engineering from Durham University, UK, in 1985, 1986, and 1991, respectively. He is an Alfred Deakin Professor, Pro Vice-Chancellor, Chair of Engineering, and the Founding Director of the Institute for Intelligent Systems Research and Innovation at Deakin University. His research interests include modeling of complex systems, robotics and haptics. He has published over 1000 scientific papers in various international journals and conferences, and been awarded over 50 competitive grants over the past 30 year and holds six patents, two of which have resulted in two very successful startups (Universal Motion Simulator Pty Ltd and FLAIM Systems Pty Ltd). Prof. Nahavandi is a Fellow of IEEE (FIEEE), Engineers Australia (FIEAust), the Institution of Engineering and Technology (FIET). He is a Fellow of the Australian Academy of Technology and Engineering (ATSE).

Peng Shi received his Ph.D. degree in electrical engineering from the University of Newcastle, Australia in 1994 and a Ph.D. degree in mathematics from the University of South Australia in 1998. He was awarded the Doctor of Science degree from the University of Glamorgan, Wales in 2006; and the Doctor of Engineering degree from the University of Adelaide, Australia in 2015.

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Hosen, M.A., Khosravi, A., Kabir, H.M.D. et al. NN-based Prediction Interval for Nonlinear Processes Controller. Int. J. Control Autom. Syst. 19, 3239–3252 (2021). https://doi.org/10.1007/s12555-020-0342-8

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