This paper deals with the global control of the Riera Blanca catchment in the Barcelona sewer network using a predictive optimal control approach. This catchment has been modelled using a conceptual modelling approach based on decomposing the catchments in subcatchments and representing them as virtual tanks. This conceptual modelling approach allows real-time model calibration and control of the sewer network. The global control problem of the Riera Blanca catchment is solved using a optimal/predictive control algorithm. To implement the predictive optimal control of the Riera Blanca catchment, a software tool named CORAL is used. The on-line control is simulated by interfacing CORAL with a high fidelity simulator of sewer networks (MOUSE). CORAL interchanges readings from the limnimeters and gate commands with MOUSE as if it was connected with the real SCADA system. Finally, the global control results obtained using the predictive optimal control are presented and compared against the results obtained using current local control system. The results obtained using the global control are very satisfactory compared to those obtained using the local control.

Abstract-This paper proposes a reference model approach for the trajectory tracking of a four wheeled omnidirectional mobile robot. In particular, the error model is brought to a quasi-Linear Parameter Varying (LPV) form suitable for designing an errorfeedback controller. It is shown that, if polytopic techniques are used to reduce the number of constraints from infinite to finite, a solution within the standard LPV framework could not exist due to a singularity that appears in the possible values of the input matrix. Adding a switching component to the controller allows to solve this problem. Moreover, a switching LPV virtual actuator is added to the control loop in order to obtain fault tolerance within the fault-hiding paradigm, keeping the stability and some desired performances under the effect of actuator faults without the need of retuning the nominal controller. The effectiveness of the proposed approach is shown and proved through simulation and experimental results.

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This paper describes the application of model-based predictive control (MPC) techniques to the supervisory flow management in large-scale drinking water networks including a telemetry/telecontrol system. MPC is used to generate flow control strategies (set-points for the regulatory controllers) from the sources to the consumer areas to meet future demands, optimizing performance indexes associated to operational goals such as economic cost, safety storage volumes in the network and smoothness of the flow control actions. The designed management strategies are applied to a model of a real case study: the drinking water transport network of Barcelona (Spain).

Leaks are present to some extent in all waterdistribution systems. This paper proposes a leakage localization method based on pressure measurements and the application of principal component analysis to the fault diagnosis in water distribution systems. First, some theoretical basics are introduced, from model building and modeling the fault effects to monitoring. Then a simple hydraulic case study is presented to illustrate the proposed methodology, its particularities and the detection results. A methodology to detect and pre-localize leaks is being developed within a project carried out by Aguas Barcelona, Water Technological Centre CETaqua, and the Technical University of Catalonia (UPC) [4]. The objective of this project is to develop and apply an efficient system to detect and locate leaks in a water distribution network. It integrates methods and technologies available and in use by water companies, including DMA and flow/pressure sensor

This paper describes the application of model-based predictive control (MPC) techniques to the supervisory flow management in large-scale drinking water networks including a telemetry/telecontrol system. MPC is used to generate flow control strategies (set-points for the regulatory controllers) from the sources to the consumer areas to meet future demands, optimizing performance indexes associated to operational goals such as economic cost, safety storage volumes in the network and smoothness of the flow control actions. The designed management strategies are applied to a model of a real case study: the drinking water transport network of Barcelona (Spain).

This paper presents the modeling and robust low-level control design of a redundant mobile robot with four omnidirectional wheels, the iSense Robotic (iSRob) platform, that was designed to test safe control algorithms. iSRob is a multivariable nonlinear system subject to parameter uncertainties mainly due to friction forces. A multilinear model is proposed to approximate the behavior of the system, and the parameters of these models are estimated from closed-loop experimental data applying Gauss–Newton techniques. A robust control technique, quantitative feedback theory (QFT), is applied to design a proportional–integral (PI) controller for robust low-level control of the iSRob system, being this the main contribution of the paper. The designed controller is implemented, tested, and compared with a gain-scheduling PI-controller based on pole assignment. The experimental results show that robust stability and control effort margins against system uncertainties are satisfied and demonstrate better performance than the other controllers used for comparison.

In this paper, a methodology for the optimal management of a combined irrigation and water supply system based on model predictive control (MPC) is proposed. A controloriented modelling methodology for this type of systems is presented as well. MPC is used to generate flow control strategies from the sources to the consumer and irrigation areas to meet future demands with appropriate flows, optimizing operational goals such as network safety volumes in dams and flow control stability in actuators (valves, gates and pumps). The case study of Guadiana river is used to exemplify and verify the proposed optimal management methodology. Results have shown the effectiveness of the proposed modelling and control methodologies.