Browsing by Author "Botto, Miguel Ayala"
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- Data Based Modeling of a Large Scale Water Delivery SystemPublication . Fernandes, Marta; Oliveira, Paulo; Vieira, Susana; Mendonça, Luís; Nabais, João Lemos; Botto, Miguel AyalaWater is a vital resource and the growing populations and economies around the globe are pushing its demand worldwide. Therefore, the water conveyance operation should be well managed and improved. This paper proposes the development of reliable models able to predict water levels of a real 24.4 km water delivery channel in real time. This is a difficult task because this is a time-delayed dynamical system distributed over a long distance with nonlinear characteristics and external perturbations. Artificial neural networks are used, which are a well-known modeling technique that has been applied to complex and nonlinear systems. Real data is used for the design and validation of the models. The model obtained has the ability to predict water levels along the channel with minimum error, which can result in significant reduction of wasted water when implementing an automatic controller.
- Hierarchical MPC for Multi-Commodity Transportation NetworksPublication . Nabais, João Lemos; Negenborn, Rudy R.; Carmona-Benítez, Rafael Bernardo; Mendonça, Luís Filipe; Botto, Miguel AyalaTransportation networks are large scale complex systems spatially distributed whose objective is to deliver commodities at the agreed time and at the agreed location. These networks appear in different domain fields, such as communication, water distribution, traffic, logistics and transportation. A transportation network has at the macroscopic level storage capability (located in the nodes) and transport delay (along each connection) as main features. Operations management at transportation networks can be seen as a flow assignment problem. The problem dimension to solve grows exponentially with the number of existing commodities, nodes and connections. In this work we present a Hierarchical Model Predictive Control (H-MPC) architecture to determine flow assignments in transportation networks, while minimizing exogenous inputs effects. This approach has the capacity to keep track of commodity types while solving the flow assignment problem. A flow decomposition of the main system into subsystems is proposed to diminish the problem dimension to solve in each time step. Each subsystem is managed by a control agent. Control agents solve their problems in a hierarchical way, using a so-called push-pull flow perspective. Further problem dimension reduction is achieved using contracted projection sets. The framework proposed can be easily scaled to network topologies in which hundreds of commodities and connections are present.
- A multi-agent architecture for diagnosing simultaneous faults along water canalsPublication . Nabais, João Lemos; Mendonça, Luís F.; Botto, Miguel AyalaWater is intensively used in mankind activities, in particular in agriculture. Water is commonly conveyed for agriculture purposes through water canal networks which are large-scale spatially distributed systems crossing extensive regions. In the presence of leaks, unauthorized water withdrawals, water depth sensor faults or gate faults, the quality of service can be severely compromised. A system able to diagnose which type of fault is present at a given time is of vital importance to access the current state of the water canal and proceed to restore its nominal condition. This paper proposes a multiagent architecture to simultaneously detect, isolate and estimate lateral outflows (e.g., leaks or water withdrawals) and hardware faults (e.g., a gate obstruction or a downstream water depth sensor fault) in water canal networks. First, the main canal network is broken down into several subsystems composed of a single canal pool with the corresponding gate. Then, an agent is assigned to each subsystem aiming at its fault diagnosis. The approach is based on the generation and evaluation of residuals obtained from the comparison of model-based output signals with real data. Application to an experimental water canal bears out the proposed architecture as a valuable tool for monitoring and supervising general water canals.