Jalel Ben Hmida

A feedback linearization control scheme for maximum power generation in wind energy systems

As the penetration of wind turbines into the power grid increases, greater emphasis is placed on designing advanced control approaches for optimum power generation. This paper proposes a novel control scheme for a DFIG-driven variable speed wind energy system. The approach aims at reaching optimum performance in terms of power generation at the point of common coupling. The control scheme is derived based on the feedback linearization technique, allowing both decoupling and linearization of the nonlinear multivariable system. The proposed approach is based on the theory of feedback linearization and is aimed at achieving optimum power generation at any wind speed within the operating range. Application of the proposed approach to a DFIG-based variable speed wind turbine has led to optimum operations at various speed ranges. Simplicity and ease of implementation of the overall scheme along with its fast dynamic response in addition to the optimum power production are the main positive features of the proposed approach.

Production scheduling for continuous manufacturing systems with quality constraints

This research is motivated by a real world production scheduling problem in a continuous manufacturing system involving multiple objectives, multiple products and multiple processing lines with various inventory, production and quality constraints. Because of the conflicting objectives, a global optimization approach is considered as not feasible by the plant management. Given a customer demand forecast, two practical heuristic or sequential optimization algorithms are developed to generate daily production schedules for two primary objectives: minimize shipment delays (pull-backward procedure) and minimize average inventory levels (push-forward procedure). A third heuristic algorithm (reduce switch-over procedure) which is based on the current management practice is also developed to serve as a benchmark. A factorial experiment was performed to evaluate the performance of the heuristic procedures and to identify factors that might affect the performance differences among heuristics. Since each heuristic is designed to give priority to one of the three conflicting objectives, none of them is absolutely superior to the other algorithms in all aspects. However, the first two heuristic procedures performed better than the current management practice in shipment delays and average daily inventory. The production schedules generated by the two procedures also satisfy the quality constraints. The experimental results also showed that the performance of the algorithms is significantly affected by product mix, inventory levels, and demand pattern.

A self tuning adaptive approach for actuator failure compensation in aircraft systems

A self tuning adaptive approach is proposed for actuator failure compensation in aircraft systems. The control algorithm is designed so that under specific fault conditions, an estimation of the fault is obtained using a self tuning method, alleviating therefore the need for a fault detection and isolation algorithm (FDI) as required by active fault tolerant control (FTC) approaches. To demonstrate the effectiveness of the proposed approach, an F-16 aircraft system subject to a set of fault patterns in different actuators is considered. The self tuning adaptive approach recovers well from the induced faults in the actuators.

Inventory Management and Maintenance in Offshore Vessel Industry

An evaluation of the inventory policy at an offshore vessel company was conducted. Currently the items in inventory are used at random intervals as they are used only when parts fail. To solve the problem of reducing a very large inventory but keep enough to allow an uninterrupted service to the customer, we propose a real and economically efficient multicriteria inventory policy using inventory classification method integrated with a preventive maintenance program. The proposed method can be summarized in the following three steps: first, rank the parts according to both the lead time from supplier and the cost of downtime failure and use a matrix display to show the most critical items. In the second step develop a preventive maintenance program for the most critical parts to minimize downtime due to failure. Finally in the third step standardize most critical parts to reduce the inventory which saves the company money while maintaining the same level of service to its customers. Our study showed that the company with a very large inventory could effectively reduce its size by focusing on key parts.

A frequency-domain approach to improve ANNs generalization quality via proper initialization

The ability to train a network without memorizing the input/output data, thereby allowing a good predictive performance when applied to unseen data, is paramount in ANN applications. In this paper, we propose a frequency-domain approach to evaluate the network initialization in terms of quality of training, i.e., generalization capabilities. As an alternative to the conventional time-domain methods, the proposed approach eliminates the approximate nature of network validation using an excess of unseen data. The benefits of the proposed approach are demonstrated using two numerical examples, where two trained networks performed similarly on the training and the validation data sets, yet they revealed a significant difference in prediction accuracy when tested using a different data set. This observation is of utmost importance in modeling applications requiring a high degree of accuracy. The efficiency of the proposed approach is further demonstrated on a real-world problem, where unlike other initialization methods, a more conclusive assessment of generalization is achieved. On the practical front, subtle methodological and implementational facets are addressed to ensure reproducibility and pinpoint the limitations of the proposed approach.