Roger M. Faye

Decentralized predictive controller for delivery canals

Presents the design of a predictive controller for decentralized control of an irrigation delivery canal. The overall canal is decomposed into single subsystems (pools) separated by a control structure (gate). The subsystems are inter-dependent because flow through the structures depends on both upstream and downstream water level. In the paper, predictive control theory is used to formulate a set of local controllers for the automatic operation of gates of each pool. The control scheme presented takes into account explicitly the interactions among subsystems. The objective of each controller is to maintain the downstream end water level of each pool at a constant target value under external perturbation. To evaluate the controller performance in realistic conditions, it is tested on an open-canal flow nonlinear model.

An intelligent decision support system for irrigation system management

Considers the design of a decision support system for the short term water resource management of an irrigation system. The operations of similar systems are often impaired by different stochastic events like device failure, heavy rains or dry periods and new long term goals. To be effective, such a decision support system which is based on knowledge techniques (state identification) and adaptive optimization (short term plans), requires the development of an information system based on water resource demand and supply. This information system gathers data from different fields (hydrology, meteorology and agriculture) so that accurate predictions about available reserves and demand levels can be performed. So, this communication presents the structure of the decision support system and focuses on tactical management information needs. The case study considered deals with a three-reach irrigation system.

Decentralized adaptive predictive control of multi-reach irrigation canal

This paper presents the design of a predictive controller for decentralized control of an irrigation canal. The overall canal is decomposed into N subsystems (reaches) separated by the control structure (gate). The subsystems are interdependent because flow through the structures depends on both the upstream and downstream water level. In this paper, predictive control theory is used to formulate a set of local controllers for the automatic operation of each reach gate. The control scheme presented explicitly takes into account the interactions among subsystems. The objective of each controller is to maintain the downstream end water level of each reach at a constant target value under external perturbation. To evaluate the controller performance in realistic conditions, it is tested on an open-canal flow nonlinear model. The case study considered deals with 8 canal reaches.

Decentralized adaptive predictive control of multireach irrigation canal

This paper presents the design of a predictive controller for decentralized control of an irrigation canal. The overall canal is decomposed into N subsystems (reaches) separated by a control structure (gate). The subsystems are interdependent because flow through the structures depends on both up- and downstream water levels. Here, predictive control theory is used to formulate a set of local controllers for the automatic operation of each reach gate. The control scheme presented explicitly takes into account the interactions among subsystems. The objective of each controller is to maintain the downstream end water level of each reach at a constant target value under external perturbation. To evaluate the controller performance in realistic conditions, it is tested on an open-canal flow non-linear model. The case study considered deals with eight canal reaches.

A Dynamic Booking Model Revenue for Airline Management

In this communication the design of a decision support system for improving the booking policy of an airline company is considered. Since the Airline Deregulation Act of 1978 in the USA, many studies on revenue (yield) management have been performed, some of them particularly towards the implementation of the dynamic programming technique for decision making. Here a step further is taken in this direction by the proposal of a new recursive model for maximum expected revenue evaluation which, contrarily to other approaches, takes into account daily booking requests arrivals.