The objective of this study is to contribute to energy efficiency in the manufacturing industry, more particularly in high energy-consuming integrated production plants. This study considers particularly job-shops where the energy necessary to realize production plans must be minimized while satisfying make-span constraints. When machines are operated in a standard way, for a given production plan, the component of energy consumption which is a variable corresponds to the unproductive periods of the machines between two operations. In this study, the cases of mechanical and thermal machines are examined to identify the energy optimal policies which allow coping efficiently with their unproductive periods. The question was at first to identify the conditions in which a shut down or an idle regime was a better solution for a machine during an unproductive period, however, the performed analysis has led to considering hybrid solutions where the machine is in a first stage shut down and then put into an idle mode until its startup to perform the next operation.

This paper considers the problem of structuring the urban airspace so that UAVs-based-urban-logistics can be performed efficiently. A two-level structure is proposed to ease accessibility of the logistics service and the safety of traffic in the urban airspace. This gives way to cope with two problems: one related with the design of local networks and another one related with the design of corridors between different urban subareas. In both cases, heuristic solution processes are proposed, discussed and illustrated.

This study considers a flexible job-shop scheduling problem where energy cost savings are the primary objective and where the classical objective of the minimization of the make-span is left in the background. An original mixed-integer formulation of this optimization problem is proposed where the processed flows of material and their timing are explicitly considered. Considering the computational complexity of this problem, it is shown how this modelling approach eases the design of a comprehensive heuristic based on energy considerations. The proposed approach is illustrated through a small scale problem.

This paper addresses the design of conflict free trajectories for sets of flights crossing areas subject to wind and meteorological hazards. After presenting the collaborative process based on first come first served principle, a dynamic Fast Marching Tree Star algorithm is proposed. The algorithm first computes initial avoidance trajectories and then updates them according to the new position of the hazard areas. Smart samplings are also proposed to reduce as much as possible the computing times. Indeed, the method generates in less than 1 second avoidance trajectories and guarantees safety. Then, a discussion on this method is conducted and shows its inequity. First simulations illustrate the proposed solution approach with promising results. However, to enhance equity between aircraft, the method is improved through a fairer collaborative process.

Summary Evaluation of flight qualities plays an important role in the design of a civil aircraft. An appropriate level of flight qualities will be the key to achieve the airworthiness certification. With the objective to provide an overall view of flight qualities evaluation for a civil aircraft, a model‐based approach is proposed. Some terms related to flight qualities are defined formally, and the model‐based flight qualities evaluation approach under different situations is formed and proposed. Two working conditions of actuators are considered in the evaluation of flight qualities. When no actuator fails, linearization is adopted to build a linear flight dynamics model for analyzing the main parameter of flight qualities. When some actuators fail, a new off‐line approach for handling qualities evaluation, which employs a controller that generates appropriate commands for healthy actuators, is proposed. The design of the controller is following the idea of classical predictive control technique, and a recurrent linear quadratic programming problem is formulated to find the maximum remaining maneuverability while not avoiding any constraints. Taking the rotational maneuver as a study example, a test‐bed for handling qualities evaluation is built. The numerical simulation results show the feasibility of proposed evaluation approach.

Autonomous Unmanned Aerial Vehicles (UAVs) provide an effective alternative for surveillance in urban areas due to their cost and safety when compared to other traditional methods. The objective of this study is to report the development of a system capable of analyzing digital images of the terrain and identifying potential invasion, unauthorized changes in land and deforestation in some special urban areas. Images are captured by a camera attached to an autonomous helicopter, flying it around the area. For processing the images, an Artificial Neural Network (ANN) technique called Self Organizing Map (SOM) is used.

Adaptive control algorithms are of interest in the flight control systems design not only for their capability to improve performance and reliability but also for handling aerodynamic parameter uncertainties, external disturbances and modeling inaccuracies. This paper addresses a nonlinear direct adaptive sliding mode controller to perform a flight path tracking for a transportation aircraft. The synthesis of the adaptation laws is based on the positivity and Lyapunov design principle. Simulation results for different flight conditions for transportation aircraft are provided to illustrate the effectiveness of the proposed controller.

In this paper, flight guidance dynamics are shown to be implicit differentially flat with respect to the inertial position of an aircraft. This prove the existence of a set of relations between these flat outputs and the state variables representative of flight guidance dynamics and between these flat outputs and the basic inputs to flight guidance dynamics. A neural network is introduced to obtain, from the actual trajectory, nominal flight parameters which can be compared with actual values to detect abnormal behaviour.

Differential flatness, a property of some dynamic systems which has been recognized only recently, has made possible the development of new tools to control complex nonlinear dynamic systems. Many dynamic non linear systems have been proved to be differentially flat. In this paper, it is shown that the inertial position coordinates of an aircraft can be considered as differential flat outputs for its flight guidance dynamics. Since this differential flatness property is implicit, a neural network is introduced, as a numerical device, to deal with the inversion of the guidance dynamics. Then, once conveniently structured and trained, a neural network is able to generate in real time directives to conventional autopilot systems so that a reference trajectory can be tracked. Numerical results relative to the training of the neural network and to trajectory tracking are displayed and discussed.

In the air transport sector where the uncertainty is very frequent due to the high sensitivity of the activity to many factors that influence the behavior of the consumers. Fuzzy logic can be used to produce estimates which take into account the vagueness of the operating environment. In this sector, many important decisions about the supply of service are based on these estimates. In this paper an analysis of the optimal supply conditions under fuzzy demand information is considered for a market where only one carrier is operating. The carrier's supply decision making process is investigated, first when the demand function is obtained through classical methods then when it is given by a fuzzy function. The main advantages of the new approach are then discussed

A bi-criterion approach for the nominal airline crew rostering problem is developed. The nominal crew rostering problem considers the assignment of crew staff to a set of pairings covering all the scheduled flights so that operation costs are minimized while its solution must meet hard constraints resulting from the safety regulations of civil aviation as well as from airline internal agreements. Another goal of the highest interest to airlines is that overall satisfaction of crew staff may have important consequences on the quality and economic return of operations. A new mathematical formulation of the crew scheduling problem which takes into account the satisfaction of crew members is proposed. A heuristic approach, combined with a genetic algorithm technique, is adopted to produce reduced cost solutions associated with acceptable satisfaction levels for the crew staff. The application of the proposed approach to a medium sized airline crew rostering problem is evaluated.

An adaptive solution for the long term management of water resource systems is considered. A fuzzy approach is used to refine the formulation of a recurrent optimization problem attached to the long term problem.

The airline crew rostering problem is considered from a dynamic point of view. The study tackles disturbances which lead to modifications for the monthly personalized assignment of the crew staff to flights. An adaptive solution approach is proposed through the asynchronous resolution of an updated optimization problem whose objective is to minimize changes from the previous crew staff assignment while covering the flights of a sliding time period. A mathematical formulation, including penalties which are applied to the assignment changes, is developed. Since the discomfort caused by these changes is often difficult to quantify, a fuzzy logic approach is developed to realize this evaluation. Then, a numerical solution approach based on a forward dynamic programming technique is described. The proposed solution strategy is applied to a medium size problem.

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.

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.