Economic MPC for the management of drinking water networks

2015 European Control Conference (ECC)

2015

Abstract-In this paper, a controller design based on robust Model Predictive Control (MPC) and Gaussian Processes (GP) for incorporating the disturbance forecasting has been proposed. Using a probabilistic system representation, the state trajectories considering the influence of disturbances can be obtained through the uncertainty propagation by using GP. Therefore, the worst-case state trajectories evolution over the MPC prediction horizon can be determined, which are potentially used by including them into the MPC cost function and constraints. For the purpose of inspecting the performance of proposed controller, it has been compared with a certainequivalent MPC and a chance-constrained MPC. Results of the application the proposed approach to Barcelona Drinking Water Network (DWN) have shown the effectiveness of the approach and comparison results with the other considered MPC approaches have shown the advantages and drawbacks of each approach.

Section: A System Descriptionmentioning

confidence: 99%

Robust Model Predictive Control based on Gaussian Processes: Application to drinking water networks

Wang

2015 European Control Conference (ECC)

2015

“…This MPC controller represents the supervisory control layer of the water network, providing optimal flow set-points at the regulatory layer (PID and PLCs controlling the respectively valves and pump stations, which operate with a smaller sampling time). Concerning the closed-loop performance, evaluated for a simulation horizon of 96 hours, Figure 3 presents some selected tank and pump behaviours achieved with the application of the proposed control approach summarized in Algorithm 1 for p = {1, 5, 10} and the corresponding evolution of the same elements when using the centralized standard MPC with terminal equality constraint reported in Grosso, Ocampo-Martinez, Puig, Limon, & Pereira. (2014) and the hierarchicallike decentralized MPC proposed in Ocampo-Martinez et al (2011).…”

Section: Case Study Descriptionmentioning

confidence: 99%

A distributed predictive control approach for periodic flow-based networks: application to drinking water systems

Grosso

International Journal of Systems Science

2017

This paper proposes a distributed model predictive control (MPC) approach designed to work in a cooperative manner for controlling flow-based networks showing periodic behaviours. Under this distributed approach, local controllers cooperate in order to enhance the performance of the whole flow network avoiding the use of a coordination layer. Alternatively, controllers use both the monolithic model of the network and the given global cost function to optimize the control inputs of the local controllers but taking into account the effect of their decisions over the remainder subsystems conforming the entire network. In this sense, a global (all-to-all) communication strategy is considered. Although the Pareto optimality cannot be reached due to the existence of non-sparse coupling constraints, the asymptotic convergence to a Nash equilibrium is guaranteed. The resultant strategy is tested and its effectiveness is shown when applied to a large-scale complex flow-based network: the Barcelona drinking water supply system.

“…The sampling time for collecting water demands from the consume sectors is 1 hour. The mathematical model case study as well as other mathematical features can be found in [22].The parameters of the model have been adjusted by using real data provided …”

Section: A System Descriptionmentioning

confidence: 99%

“…Since some manipulated variables u k have relationships with some water demands in (22), the constraint for reduced manipulated variable u k should maintain the constraint for the full manipulated variables u k . Hence,…”

Section: Physical and Operational Constraintsmentioning

confidence: 99%

See 1 more Smart Citation

Stochastic model predictive control based on Gaussian processes applied to drinking water networks

Wang

IET Control Theory & Applications

2016

This paper focuses on developing a stochastic model predictive control (MPC) strategy based on Gaussian Processes (GPs) for propagating system disturbances in a receding horizon way. Using a probabilistic system representation, the state trajectories considering the influence of disturbances can be obtained through the uncertainty propagation by using GPs. This fact allows obtaining the confidence intervals for state evolutions over the MPC prediction horizon that are included into the MPC objective function and constraints. The feasibility of the proposed MPC strategy considering the incorporated results of disturbance forecasting is also discussed. Simulation results obtained from the application of the proposed approach to the Barcelona drinking water network (DWN) taking real demand data into account are presented.The comparison with the well-known certainty-equivalent MPC shows the effectiveness of the proposed stochastic MPC approach.