Tuning of PID controller based on a multiobjective genetic algorithm applied to a robotic manipulator

Most controllers optimization and design problems are multiobjective in nature, since they normally have several (possibly conflicting) objectives that must be satisfied at the same time. Instead of aiming at finding a single solution, the multiobjective optimization methods try to produce a set of good trade-off solutions from which the decision maker may select one. Several methods have been devised for solving multiobjective optimization problems in control systems field. Traditionally, classical optimization algorithms based on nonlinear programming or optimal control theories are applied to obtain the solution of such problems. The presence of multiple objectives in a problem usually gives rise to a set of optimal solutions, largely known as Pareto-optimal solutions. Recently, Multiobjective Evolutionary Algorithms (MOEAs) have been applied to control systems problems. Compared with mathematical programming, MOEAs are very suitable to solve multiobjective optimization problems, because they deal simultaneously with a set of solutions and find a number of Pareto optimal solutions in a single run of algorithm. Starting from a set of initial solutions, MOEAs use iteratively improving optimization techniques to find the optimal solutions. In every iterative progress, MOEAs favor population-based Pareto dominance as a measure of fitness. In the MOEAs context, the Non-dominated Sorting Genetic Algorithm (NSGA-II) has been successfully applied to solving many multiobjective problems. This paper presents the design and the tuning of two PID (Proportional–Integral–Derivative) controllers through the NSGA-II approach. Simulation numerical results of multivariable PID control and convergence of the NSGA-II is presented and discussed with application in a robotic manipulator of two-degree-of-freedom. The proposed optimization method based on NSGA-II offers an effective way to implement simple but robust solutions providing a good reference tracking performance in closed loop.     

Multiobjective pseudo‐variable neighborhood descent for a bicriteria parallel machine scheduling problem with setup time

A multiobjective variable neighborhood descent (VND) based heuristic is developed to solve a bicriteria parallel machine scheduling problem. The problem considers two objectives, one related to the makespan and the other to the flow time, where the setup time depends on the sequence, and the machines are identical. The heuristic has a set of neighborhood structures based on swap, remove, and insertion moves. We propose changing the local search inside the VND to a sequential search through the neighborhoods to obtain nondominated points for the Pareto‐front quickly. In the numerical tests, we consider a single‐objective version of the heuristic, comparing the results on 510 benchmark instances to show that it is quite effective. Moreover, new instances are generated in accordance with the literature for the bicriteria problem, showing the ability of the proposed heuristic to return an efficient set of nondominate solutions compared with the well‐known nondominated sorting genetic algorithm II.     

Design complexity control in truss optimization

Truss optimization based on the ground structure approach often leads to designs that are too complex for practical purposes. In this paper we present an approach for design complexity control in truss optimization. The approach is based on design complexity measures related to the number of bars (similar to Asadpoure et al. Struct Multidisc Optim 51(2):385–396 2015) and a novel complexity measure related to the number of nodes of the structure. Both complexity measures are continuously differentiable and thus can be used together with gradient based optimization algorithms. The numerical examples show that the proposed approach is able to reduce design complexity, leading to solutions that are more fit for engineering practice. Besides, the examples also indicate that in some cases it is possible to significantly reduce design complexity with little impact on structural performance. Since the complexity measures are non convex, a global gradient based optimization algorithm is employed. Finally, a detailed comparison to a classical approach is presented.     

Personalizing self-organizing music spaces with anchors: design and evaluation

We propose and evaluate a system for content-based visualization and exploration of music collections. The system is based on a modification of Kohonen’s Self-Organizing Map algorithm and allows users to choose the locations of clusters containing acoustically similar tracks on the music space. A user study conducted to evaluate the system shows that the possibility of personalizing the music space was perceived as difficult. Conversely, the user study and objective metrics derived from users’ interactions with the interface demonstrate that the proposed system helped individuals create playlists faster and, under some circumstances, more effectively. We believe that personalized browsing interfaces are an important area of research in Multimedia Information Retrieval, and both the system and user study contribute to the growing work in this field.     

Ocular movements under taskload manipulations: Influence of geometry on saccades in air traffic control simulated tasks

Traffic geometry is a factor that contributes to cognitive complexity in air traffic control. In conflict‐detection tasks, geometry can affect the attentional effort necessary to correctly perceive and interpret the situation; online measures of situational workload are therefore highly desirable. In this study, we explored whether saccadic movements vary with changes in geometry. We created simple scenarios with two aircraft and simulated a conflict detection task. Independent variables were the conflict angle and the distance to convergence point. We hypothesized lower saccadic peak velocity (and longer duration) for increasing complexity, that is, for increasing conflict angles and for different distances to convergence point. Response times varied accordingly with task complexity. Concerning saccades, there was a decrease of peak velocity (and a related increase of duration) for increased geometry complexity for large saccades (>15°). The data therefore suggest that geometry is able to influence “reaching” saccades and not “fixation” saccades. © 2011 Wiley Periodicals, Inc.     

Systems of representation: a pedagogical model for design education in the information age

Abstract

The course SDR: Sistemas de Representación(Systems of Representation), is the last stage of a line of work which has the objective of integrating information technologies in the education of architecture in a meaningful way. A distinctive mark of this pedagogic approach has been to look upon computer technology as an opportunity to rethink the methods, contents and goals of architecturaleducation,in thelight of contemporary culture.

The course is structured in six themes, each one standing for a ‘system of representation’: ‘text’, ‘figure’, ‘object’, ‘image’, ‘space’ and ‘light’. Within every system, a variety of issues dealing with the concept of ‘representation’ are addressed in an interdisciplinary manner. It is a compulsory course lasting three semesters, in the second and third year of a five-year architectural program. It has been offered since the academic year 1999/2000.     

Every team deserves a second chance: an extended study on predicting team performance

Voting among different agents is a powerful tool in problem solving, and it has been widely applied to improve the performance in finding the correct answer to complex problems. We present a novel benefit of voting, that has not been observed before: we can use the voting patterns to assess the performance of a team and predict their final outcome. This prediction can be executed at any moment during problem-solving and it is completely domain independent. Hence, it can be used to identify when a team is failing, allowing an operator to take remedial procedures (such as changing team members, the voting rule, or increasing the allocation of resources). We present three main theoretical results: (1) we show a theoretical explanation of why our prediction method works; (2) contrary to what would be expected based on a simpler explanation using classical voting models, we show that we can make accurate predictions irrespective of the strength (i.e., performance) of the teams, and that in fact, the prediction can work better for diverse teams composed of different agents than uniform teams made of copies of the best agent; (3) we show that the quality of our prediction increases with the size of the action space. We perform extensive experimentation in two different domains: Computer Go and Ensemble Learning. In Computer Go, we obtain high quality predictions about the final outcome of games. We analyze the prediction accuracy for three different teams with different levels of diversity and strength, and show that the prediction works significantly better for a diverse team. Additionally, we show that our method still works well when trained with games against one adversary, but tested with games against another, showing the generality of the learned functions. Moreover, we evaluate four different board sizes, and experimentally confirm better predictions in larger board sizes. We analyze in detail the learned prediction functions, and how they change according to each team and action space size. In order to show that our method is domain independent, we also present results in Ensemble Learning, where we make online predictions about the performance of a team of classifiers, while they are voting to classify sets of items. We study a set of classical classification algorithms from machine learning, in a data-set of hand-written digits, and we are able to make high-quality predictions about the final performance of two different teams. Since our approach is domain independent, it can be easily applied to a variety of other domains.     

Thermodynamic and Quality Properties of Lemon Juice Powder as Affected by Maltodextrin and Arabic Gum

Sorption isotherms of lemon juice (LE) powders with and without additives—18% maltodextrin (MA) or 18% gum Arabic (GA)—were determined at 20–50°C. Addition of additives was shown to affect the isotherms in such a way that, at the same water activity, samples LE + GA and LE + MA presented lower equilibrium moisture content and were not so affected by varying temperature. The net isosteric heats of sorption of juice powders with additives were higher (less negative) than those of lemon juice powders, suggesting that there are more active polar sites in the product without addition of GA or MA. In general, the quality properties decreased with the addition of maltodextrin and gum arabic and it was obtained similar values for LE + GA and LE + MA.