Dear Amrit Ghimire,

First of all, it is applied research in a field where human health is the main concern. With the advances made in computing power, digitization and advanced design of single bords; the MPC is positioned as being the controller who responds positively to a certain number of criteria and precisely in this field of application.

For more information about this subject i suggest you to see links and attached file on topic.

Article Stochastic model predictive control approaches applied to dr...

Article Non-linear economic model predictive control of water distri...

Article Economic model predictive control for the operation optimiza...

https://upcommons.upc.edu/bitstream/handle/2117/108961/1771-Stochastic-model-predictive-control-approaches-applied-to-drinking-water-networks.pdf

https://repositorio.uniandes.edu.co/bitstream/handle/1992/12618/u686587.pdf?sequence=1

https://etheses.bham.ac.uk/id/eprint/7207/1/Xie17PhD.pdf

Best regards

Dear Amrit

Control system development in engineering is the formulation of the dynamic equation using inputs, outputs, and other relevant parameters in the system. To implement a controller first needs to have a clear understanding of the characteristics of the water distribution and treatment system. There you may have many inputs and outputs. And the relationship of input and output may be linear or nonlinear. For such a system, you can try Artificial intelligence-based controllers which are capable of handling both linear and nonlinear characteristics of the system. The AI controller can identify equipment irregularities fast and correctly and send them back to control in real-time.

You can refer following two books first one is about classical control systems; from that, you can get a basic idea about control systems. The second one is about AI control systems.

https://www.pdfdrive.com/the-control-systems-handbook-control-system-advanced-methods-second-edition-electrical-engineering-handbook-e175616386.html

https://www.pdfdrive.com/applied-intelligent-control-of-induction-motor-drives-d188836061.html

MPC seems a good choice as it can systematically handle constraints and control optimality yet with the general drawback of the excessive computational burden (which might cause some troubles if you want to execute such a controller in real-time with cheap ECU).

For MPC which entails both accuracy (compared to MPC with linearized plant model) and computational efficiency, you may want to check out the differential-flatness-model-based MPC, e.g., in:

Zejiang Wang, Jingqiang Zha, and Junmin Wang, “Autonomous Vehicle Trajectory Following: A Flatness Model Predictive Control Approach with Hardware-in-the-Loop Verification,” IEEE Transactions on Intelligent Transportation Systems, Vol. 22, No. 9, pp. 5613-5623, Sept. 2021 (DOI: 10.1109/TITS.2020.2987987).

Other control methods which share a similar spirit as MPC exist. For instance, you can take a look at Control Barrier Function Based Quadratic

Programs as in:

[1] A. D. Ames, J. W. Grizzle, and P. Tabuada, “Control barrier function based quadratic programs with application to adaptive cruise control,” in 53rd IEEE Conference on Decision and Control, Los Angeles, CA, USA, Dec. 2014, pp. 6271–6278. doi: 10.1109/CDC.2014.7040372.

Such a method is akin to MPC but has a computational burden of most a QP.