Abstract
When using direct methods to solve continuous-time nonlinear optimal control problems, regular time meshes having equidistant spacing are most frequently used. However, in some cases, these meshes cannot cope accurately with nonlinear behaviour and increasing uniformly the number of mesh nodes may lead to a more complex problem. We propose an adaptive time-mesh refinement algorithm, considering different levels of refinement and several mesh refinement criteria. Namely, we use information of the adjoint multipliers to decide where to refine further. This technique is here tested to solve two optimal control problems. One involving nonholonomic vehicles with state constraints which is characterized by having strong nonlinearities and by discontinuous controls; the other is also a nonlinear problem of a compartmental SEIR system. The proposed strategy leads to results with higher accuracy and yet with lower overall computational time, when compared to results obtained by meshes having equidistant spacing. We also apply the necessary condition of optimality in the form of the Maximum Principle of Pontryagin to characterize the solution and to validate the numerical results.

Abstract
Direct methods are becoming the most used technique to solve nonlinear optimal control problems. Regular time meshes having equidistant spacing are most frequently used. However, in some cases, these meshes cannot cope accurately with nonlinear behaviour unless a very large number of mesh nodes is used. One way to improve the solution involves adaptive mesh refinement algorithms which allow a non uniform node collocation. In the method presented in this paper, a time mesh refinement strategy based on the local error is developed. The technique was applied to solve two problems involving nonholonomic vehicles and it led to results with higher accuracy and yet with lower overall computational time when compared to a mesh having equidistant nodes. (C) 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

Abstract
This article studies the optimal solution of an irrigation problem. It consists in optimizing the planning of water used so by the water amount in the soil (trajectory) fulfils the cultivation water requirements. We characterize the optimal solution by applying the necessary conditions of optimality in the form of the Maximum Principle. We also compare the results obtained analytically and numerically. (C) 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). Peer-review under responsibility of ISEL Instituto Superior de Engenharia de Lisboa, Lisbon, PORTUGAL.

Abstract
A model to optimize the water use in the irrigation of a farm field via optimal control (water flow) that take into account the evapotranspiration, rainfall, and losses by filtration and runoff was developed in [1]. Here we improve the previous model to take into account real data of rainfall. Model predictive control is applied to replan. We test and compare different nonlinear constrained optimization techniques for solving the nonlinear constrained optimization problem that arises from the discretization of the proposed optimal control problem. Furthermore, we test different time discretization steps.

Abstract
We aim to plan the water usage in the irrigation of a given farmland keeping, at same time, the field cultivation in a good state of preservation. This problem is modeled and tackled as an optimal control problem: minimize the water flow (control) so that the extent water amount in the soil (trajectory) fulfills the cultivation water requirements. To estimate rainfall, we consider two models: one based on the average monthly rainfall of the last 10 years in Lisbon area and another which considers the best linear combination of average monthly rainfall from the last 10 years and the amount of rainfall in the previous month. We study the behavior of the solutions under different weather scenarios and we compare the solutions obtained using our model of rainfall with solutions obtained having a prior knowledge of the rainfall.

Abstract
Direct methods are becoming the most used technique to solve nonlinear optimal control problems. Regular time meshes having equidistant spacing are frequently used. However, in some cases these meshes cannot cope accurately with nonlinear behavior. One way to improve the solution is to select a new mesh with a greater number of nodes. Another way, involves adaptive mesh refinement. In this case, the mesh nodes have non equidistant spacing which allow a non uniform nodes collocation. In the method presented in this paper, a time mesh refinement strategy based on the local error is developed. After computing a solution in a coarse mesh, the local error is evaluated, which gives information about the subintervals of time domain where refinement is needed. This procedure is repeated until the local error reaches a user-specified threshold. The technique is applied to solve the car-like vehicle problem aiming minimum consumption. The approach developed in this paper leads to results with greater accuracy and yet with lower overall computational time as compared to using a time meshes having equidistant spacing.