Marta Cuneo

Modeling and Feedback Control for Resource Allocation and Performance Analysis in Container Terminals

A dynamic discrete-time model of container flows in maritime terminals is proposed as a system of queues. Such queues are controlled via input variables that account for the use of the available resources given by the capacities of the handling machines used to move containers inside a terminal. Two feedback control strategies for the allocation of such resources are described. The first consists of a resource assignment that is proportional to the corresponding queue lengths; in the second, the assignment is obtained by the one-step-ahead optimization of a performance cost function according to a myopic approach. Simulation results are reported to compare such methodologies for the purpose of sensitivity and scenario analyses in the management of a maritime terminal.

A recursive algorithm for nonlinear least-squares problems

Abstract The solution of nonlinear least-squares problems is investigated. The asymptotic behavior is studied and conditions for convergence are derived. To deal with such problems in a recursive and efficient way, it is proposed an algorithm that is based on a modified extended Kalman filter (MEKF). The error of the MEKF algorithm is proved to be exponentially bounded. Batch and iterated versions of the algorithm are given, too. As an application, the algorithm is used to optimize the parameters in certain nonlinear input–output mappings. Simulation results on interpolation of real data and prediction of chaotic time series are shown.

On the convergence of EKF-based parameters optimization for neural networks

An algorithm based on the extended Kalman filter (EKF) for optimization of parameters in neural networks is presented and a convergence analysis of the estimated parameters values to the optimal ones is made. By using results on stochastic stability of EKF in filtering for discrete-time nonlinear systems, it is proved that the approximation error of the proposed learning method is locally exponentially bounded in mean square. Such training can be performed also in batch mode and outperforms well-known training methods, as shown by means of simulation results.

Nonlinear predictive control for the management of container flows in maritime intermodal terminals

The increase of efficiency in the management of container terminals is addressed via a predictive control approach to allocate the available handling resources. The predictive control action results from the minimization of a performance cost function that measures the lay times of carriers over a forward horizon. Such an approach to predictive control is based on a model of container flows inside a terminal as a system of queues. Binary variables are included into the model to represent the events of departure or stay of a carrier, thus the proposed approach requires the on-line solution of a mixed-integer nonlinear programming problem. Two techniques for solving such a problem are proposed that account for the presence of binary variables as well as nonlinearities into the model and the cost function. The first relies on the application of a standard branch-and-bound algorithm. The second is based on the idea of dealing with the decisions associated with the binary variables as step functions. In this case, real nonlinear programming techniques are used to find a solution. Finally, a third approach is proposed that is based on the idea of approximating off line the feedback control laws that result from the application of the two previous approaches. The approximation is made using a neural network, which allows one to construct an approximate feedback control law and generate the corresponding on-line control action with a small computational burden. Simulation results are reported to compare such methodologies.

Nonlinear Model Predictive Control for Resource Allocation in the Management of Intermodal Container Terminals

Nonlinear model predictive control is proposed to allocate the available transfer resources in themanagement of container terminals by minimizing a performance cost function that measures the lay times of carriers over a forward horizon. Such an approach to predictive control is based on a model of the container flows inside a terminal as a system of queues. Binary variables are included into the model to represent the events of departure or stay of a carrier, thus the proposed approach requires the on-line solution of a mixed-integer nonlinear programming problem. Different techniques for solving such problem are considered that account for the presence of binary variables as well as nonlinearities into the model and cost function. The first relies on the application of a standard branch-and-bound algorithm. The second is based on the idea of dealing with the decisions associated with the binary variables as step functions. In this case, real nonlinear programming techniques are used to find a solution. Finally, a third approach is proposed that is based on the idea of approximating off line the feedback control law that results from the application of the second one. The approximation is made using a neural network that allows to construct an approximate suboptimal feedback control law by optimizing the neural weights. Simulation results are reported to compare such methodologies.

State observers with first-/second-order sliding-mode for nonlinear systems with bounded noises

A full-order sliding-mode state observer for a class of nonlinear continuous-time dynamic systems is proposed and conditions for the stability of the estimation error in the absence of noises are provided. If the system is affected by bounded disturbances, under such conditions the existence of an attractive invariant set for the estimation error is ensured. The design of the observer can be made via LMI techniques. A second-order sliding-mode differentiator is introduced, which makes available the first time derivative of the measurable output in finite time. This artificially constructed measurement is exploited to reduce the invariant set of the estimation error.

Sliding-mode state observers for multi-output nonlinear systems with bounded noises on dynamics and measurements

A full-order sliding-mode state observer for a class of nonlinear continuous-time dynamic systems is proposed and conditions for the stability of the estimation error in the absence of noises are provided. If the system is affected by bounded disturbances, under such conditions the existence of an attractive invariant set for the estimation error is ensured. The design of the observer can be made via LMI techniques. Numerical results are reported to show the effectiveness of the proposed approach.

MODEL-BASED FEEDBACK CONTROL OF CONTAINER HANDLING IN INTERMODAL TERMINALS

Abstract A discrete-time model of the container flows for maritime terminals is proposed. This modelling framework enables to construct feedback control strategies for resource allocation. We presented two control approaches. The first one is based on a fair resource assignment of the container transfer capacities. The second one is obtained by the optimization of a cost function. Simulation results are presented to compare such control paradigms.