Adaptive and non-adaptive model predictive control of an irrigation channel

Previous Article
Traffic flow models with phase transitions on road networks
NHM Home
This Issue
Next Article
Modal decomposition of linearized open channel flow

June 2009, 4(2): 303-324. doi: 10.3934/nhm.2009.4.303

Adaptive and non-adaptive model predictive control of an irrigation channel

João M. Lemos ^1, , Fernando Machado ^2, , Nuno Nogueira ^2, , Luís Rato ^3, and Manuel Rijo ^4,

1.	INESC-ID/IST/UTL, R. Alves Redol 9, 1000-029 Lisboa, Portugal
2.	INESC-ID, R. Alves Redol 9, 1000-029 Lisboa, Portugal, Portugal
3.	Departamento de Informática, Universidade de Évora, Rua Romão Ramalho 59, 7000-671 Évora, Portugal
4.	Núcleo de Hidráulica e Controlo de Canais, Universidade de Évora, Pólo da Mitra, Apartado 94, 7002-554 Évora, Portugal

Received October 2008 Revised February 2009 Published June 2009

Abstract
Related Papers

The performance achieved with both adaptive and non-adaptive Model Predictive Control (MPC) when applied to a pilot irrigation channel is evaluated. Several control structures are considered, corresponding to various degrees of centralization of sensor information, ranging from local upstream control of the different channel pools to multivariable control using only proximal pools, and centralized multivariable control relying on a global channel model. In addition to the non-adaptive version, an adaptive MPC algorithm based on redundantly estimated multiple models is considered and tested with and without feedforward of adjacent pool levels, both for upstream and downstream control. In order to establish a baseline, the results of upstream and local PID controllers are included for comparison. A systematic simulation study of the performances of these controllers, both for disturbance rejection and reference tracking is shown.

Keywords: Control, Irrigation Channels., Predictive Control, Adaptive Control.

Mathematics Subject Classification: Primary: 93C83, 93C95; Secondary: 93C40, 93A1.

Citation: João M. Lemos, Fernando Machado, Nuno Nogueira, Luís Rato, Manuel Rijo. Adaptive and non-adaptive model predictive control of an irrigation channel. Networks & Heterogeneous Media, 2009, 4 (2) : 303-324. doi: 10.3934/nhm.2009.4.303

[1]	Rudy R. Negenborn, Peter-Jules van Overloop, Tamás Keviczky, Bart De Schutter. Distributed model predictive control of irrigation canals. Networks & Heterogeneous Media, 2009, 4 (2) : 359-380. doi: 10.3934/nhm.2009.4.359
[2]	Luís Tiago Paiva, Fernando A. C. C. Fontes. Sampled–data model predictive control: Adaptive time–mesh refinement algorithms and guarantees of stability. Discrete & Continuous Dynamical Systems - B, 2019, 24 (5) : 2335-2364. doi: 10.3934/dcdsb.2019098
[3]	Lars Grüne, Marleen Stieler. Multiobjective model predictive control for stabilizing cost criteria. Discrete & Continuous Dynamical Systems - B, 2017, 22 (11) : 1-24. doi: 10.3934/dcdsb.2018336
[4]	Nahid Banihashemi, C. Yalçın Kaya. Inexact restoration and adaptive mesh refinement for optimal control. Journal of Industrial & Management Optimization, 2014, 10 (2) : 521-542. doi: 10.3934/jimo.2014.10.521
[5]	Tayel Dabbous. Adaptive control of nonlinear systems using fuzzy systems. Journal of Industrial & Management Optimization, 2010, 6 (4) : 861-880. doi: 10.3934/jimo.2010.6.861
[6]	Judy Day, Jonathan Rubin, Gilles Clermont. Using nonlinear model predictive control to find optimal therapeutic strategies to modulate inflammation. Mathematical Biosciences & Engineering, 2010, 7 (4) : 739-763. doi: 10.3934/mbe.2010.7.739
[7]	Gregory Zitelli, Seddik M. Djouadi, Judy D. Day. Combining robust state estimation with nonlinear model predictive control to regulate the acute inflammatory response to pathogen. Mathematical Biosciences & Engineering, 2015, 12 (5) : 1127-1139. doi: 10.3934/mbe.2015.12.1127
[8]	Didier Georges. Infinite-dimensional nonlinear predictive control design for open-channel hydraulic systems. Networks & Heterogeneous Media, 2009, 4 (2) : 267-285. doi: 10.3934/nhm.2009.4.267
[9]	Aliki D. Muradova, Georgios K. Tairidis, Georgios E. Stavroulakis. Adaptive Neuro-Fuzzy vibration control of a smart plate. Numerical Algebra, Control & Optimization, 2017, 7 (3) : 251-271. doi: 10.3934/naco.2017017
[10]	Zhao-Han Sheng, Tingwen Huang, Jian-Guo Du, Qiang Mei, Hui Huang. Study on self-adaptive proportional control method for a class of output models. Discrete & Continuous Dynamical Systems - B, 2009, 11 (2) : 459-477. doi: 10.3934/dcdsb.2009.11.459
[11]	James P. Nelson, Mark J. Balas. Direct model reference adaptive control of linear systems with input/output delays. Numerical Algebra, Control & Optimization, 2013, 3 (3) : 445-462. doi: 10.3934/naco.2013.3.445
[12]	Luís Tiago Paiva, Fernando A. C. C. Fontes. Adaptive time--mesh refinement in optimal control problems with state constraints. Discrete & Continuous Dynamical Systems - A, 2015, 35 (9) : 4553-4572. doi: 10.3934/dcds.2015.35.4553
[13]	Tehuan Chen, Chao Xu, Zhigang Ren. Computational optimal control of 1D colloid transport by solute gradients in dead-end micro-channels. Journal of Industrial & Management Optimization, 2018, 14 (3) : 1251-1269. doi: 10.3934/jimo.2018052
[14]	Yoora Kim, Gang Uk Hwang, Hea Sook Park. Feedback limited opportunistic scheduling and admission control for ergodic rate guarantees over Nakagami-$m$ fading channels. Journal of Industrial & Management Optimization, 2009, 5 (3) : 553-567. doi: 10.3934/jimo.2009.5.553
[15]	Kyung Jae Kim, Jin Soo Park, Bong Dae Choi. Admission control scheme of extended rtPS algorithm for VoIP service in IEEE 802.16e with adaptive modulation and coding. Journal of Industrial & Management Optimization, 2010, 6 (3) : 641-660. doi: 10.3934/jimo.2010.6.641
[16]	Lixin Xu, Wanquan Liu. A new recurrent neural network adaptive approach for host-gate way rate control protocol within intranets using ATM ABR service. Journal of Industrial & Management Optimization, 2005, 1 (3) : 389-404. doi: 10.3934/jimo.2005.1.389
[17]	Qun Lin, Ryan Loxton, Kok Lay Teo. The control parameterization method for nonlinear optimal control: A survey. Journal of Industrial & Management Optimization, 2014, 10 (1) : 275-309. doi: 10.3934/jimo.2014.10.275
[18]	Alain Bensoussan, John Liu, Jiguang Yuan. Singular control and impulse control: A common approach. Discrete & Continuous Dynamical Systems - B, 2010, 13 (1) : 27-57. doi: 10.3934/dcdsb.2010.13.27
[19]	Cristiana J. Silva, Helmut Maurer, Delfim F. M. Torres. Optimal control of a Tuberculosis model with state and control delays. Mathematical Biosciences & Engineering, 2017, 14 (1) : 321-337. doi: 10.3934/mbe.2017021
[20]	Victor Ayala, Adriano Da Silva, Luiz A. B. San Martin. Control systems on flag manifolds and their chain control sets. Discrete & Continuous Dynamical Systems - A, 2017, 37 (5) : 2301-2313. doi: 10.3934/dcds.2017101

2018 Impact Factor: 0.871

American Institute of Mathematical Sciences