A non-iterative cascaded predictive control approach for control of irrigation canals

Irrigation canals transport water from water sources (such as large rivers and lakes) to water users (such as farmers). Irrigation canals are typically very large in nature, covering vast geographical areas, and involving a significant number of control actuators, such as pumps, gates, and locks. The control of such canals is aimed at guaranteeing the adequate delivery of water with minimal water spillage and with minimal control structure usage. To take into account forecasts on, e.g., water consumption and weather, model predictive control (MPC) can be used to determine which actions to take. For large-scale systems, in which different parts of the canal are owned by different parties, distributed MPC control could then be employed. Although iterative distributed MPC approaches proposed earlier in the literature may yield overall optimal performance, the amount of iterations required before achieving this performance may be large, and thus require a significant amount of time. In this paper, the structure of systems consisting of serially interconnected subsystems is exploited to obtain an efficient non-iterative, cascaded MPC scheme. Simulation studies on a 7-reach irrigation canal illustrate the performance of this non-iterative scheme in comparison with an iterative scheme.Delft Center for Systems and ControlMechanical, Maritime and Materials Engineerin

Model predictive control of resonance sensitive irrigation canals

Saving water is an economic and ecological need. One way to save water is to reduce losses in irrigation networks by canal automation. The goal of canal automation is to make the right amount of water to at arrive in the right time. In order to achieve this goal, one of the ways is controlling the gates in the irrigation network by some control algorithm. In this work the control of a specific type of canal pools is studied: short and flat pools that are prone to resonance. The downstream water level control of this type of canals is investigated using the example of the 3-reach laboratory canal of the Technical University of Catalonia. Numerical and experimental studies are carried out to investigate the following: the choice of models for predictive control, the possibility to achieve offset-free control while using gravity offtakes and the best choice of control action variables. The objective of this work is to develop a well performing centralized model predictive controller (MPC) for the laboratory canal that is able to handle known and unknown setpoint changes and disturbances, and also to draw further conclusions about controller design for this type of canals. A recently developed model for resonant canals, the Integrator Resonance, is implemented and successfully tested experimentally for the first time. A new method to achieve offset free control for model predictive control is developed and tested numerically and experimentally. A choice of control variables are tested: As opposed to the discharge which is generally used as the control action variable, a state space model is formulated by using the gate opening as control variable without the need of water level measurement downstream of the gates. The results are summarized and conclusions are presented for control of short and flat canals that are prone to resonance

Hierarchical power management in vehicle systems

This dissertation presents a hierarchical model predictive control (MPC) framework for energy management onboard vehicle systems. High performance vehicle systems such as commercial and military aircraft, on- and off-road vehicles, and ships present a unique control challenge, where maximizing performance requires optimizing the generation, storage, distribution, and utilization of energy throughout the entire system and over the duration of operation. The proposed hierarchical approach decomposes control of the vehicle among multiple controllers operating at each level of the hierarchy. Each controller has a model of a corresponding portion of the system for predicting future behavior based on current and future control decisions and known disturbances. To capture the energy storage and power flow throughout the vehicle, a graph-based modeling framework is proposed, where vertices represent capacitive elements that store energy and edges represent paths for power flow between these capacitive elements. For systems with a general nonlinear form of power flow, closed-loop stability is established through local subsystem analysis based on passivity. The ability to assess system-wide stability from local subsystem analysis follows from the particular structure of the interconnections between each subsystem, their corresponding controller, and neighboring subsystems. For systems with a linear form of power flow, robust feasibility of state and actuator constraints is achieved using a constraint tightening approach when formulating each MPC controller. Finally, the hierarchical control framework is applied to an example thermal fluid system that represents the fuel thermal management system of an aircraft. Simulation and experimental results clearly demonstrate the benefits of the proposed hierarchical control approach and the practical applicability to real physical systems with nonlinear dynamics, unknown disturbances, and actuator delays