Roberto Zanetti Freire

PMV-Based Predictive Algorithms for Controlling Thermal Comfort in Building Plants

The present paper is focused on thermal comfort control for building occupants. Thermal comfort is addressed here by the use of PMV index for such measurement. Based on PMV, two predictive strategies characterized by having terminal constraints are proposed and compared. The first is based on generating a temperature set-point signal that optimizes the building (single zone) internal PMV value. The second includes the PMV model in the controller prediction computations, generating a non-linear PMV model having Wiener structure. In both cases, the linear part of the model is built by using Laguerre basis. Simulation results, conducted with actual climate data, illustrate the performance of the thermal comfort control algorithms.

Evolutionary multi-objective optimisation with preferences for multivariable PI controller tuning

We present an evolutionary multiobjective optimisation approach for PI controller tuning.This approach incorporates designers preferences into the optimisation process.The methodology is evaluated in a multivariable process.It is possible to improve pertinency of the approximated Pareto front. Multi-objective optimisation design procedures have shown to be a valuable tool for control engineers. They enable the designer having a close embedment of the tuning process for a wide variety of applications. In such procedures, evolutionary multi-objective optimisation has been extensively used for PI and PID controller tuning; one reason for this is due to their flexibility to include mechanisms in order to enhance convergence and diversity. Although its usability, when dealing with multi-variable processes, the resulting Pareto front approximation might not be useful, due to the number of design objectives stated. That is, a vast region of the objective space might be impractical or useless a priori, due to the strong degradation in some of the design objectives. In this paper preference handling techniques are incorporated into the optimisation process, seeking to improve the pertinency of the approximated Pareto front for multi-variable PI controller tuning. That is, the inclusion of preferences into the optimisation process, in order to seek actively for a pertinent Pareto front approximation. With such approach, it is possible to tune a multi-variable PI controller, fulfilling several design objectives, using previous knowledge from the designer on the expected trade-off performance. This is validated with a well-known benchmark example in multi-variable control. Control tests show the usefulness of the proposed approach when compared with other tuning techniques.

Biogeography-based Optimization approach based on Predator-Prey concepts applied to path planning of 3-DOF robot manipulator

A fundamental problem in robotics consists in trajectory planning. The main task of path planning for robot manipulators is to find an optimal collision-free trajectory from an initial to a final configuration. Furthermore, trajectory planning is devoted to generate the reference inputs for the control system of the manipulator, so as to be able to execute the motion. Many important contributions to this problem have been made in recent years. Recently, techniques based on metaheuristics of natural computing, mainly evolutionary algorithms (EA), have been successfully applied to a large number of robotic applications, including the generation of optimized trajectories for robot manipulators. The aim of this paper is to evaluate a modified Biogeography-based Optimization (BBO) approach based on Predator-Prey concepts (PPBBO) to solve the trajectory planning of a robot manipulator. Simulation experiments are carried on a robot manipulator with three degrees of freedom (3-DOF) to illustrate the efficacy of the BBO approach. Biogeography deals with the geographical distribution of biological organisms. BBO is an optimization method which is motivated by the natures way of distributing habitats. Similar to genetic algorithms, BBO is a population-based stochastic global optimizer. However, in BBO, problem solutions are represented as islands, and the sharing of features between solutions is represented as migration between islands. Results demonstrated that the proposed PPBBO approach converged to promising solutions in terms of quality and convergence rate when compared with the classical BBO.

THERMAL COMFORT BASED PREDICTIVE CONTROLLERS FOR BUILDING HEATING SYSTEMS

Abstract This work is focused on indoor thermal comfort control problem in buildings equipped with HVAC (Heating Ventilation and Air Conditioning) systems. The occupants thermal comfort is addressed here by a comfort zone in the psychometric chart and the PMV (Predict Mean Vote) index. In this context, three control algorithms are proposed by using only-one-actuator system associated to a heating equipment. The methods are based on the model predictive control scheme and on the improvement of indices related to occupants thermal comfort sensation. Simulation results – obtained by using the weather data file for the city of Curitiba, Brazil – are presented to validate the proposed methodology in terms of room air temperature, relative humidity and PMV control.

Static force capability optimization of humanoids robots based on modified self-adaptive differential evolution

The current society requires solutions for many problems in safety, economy, and health. The social concerns on the high rate of repetitive strain injury, work-related osteomuscular disturbances, and domestic issues involving the elderly and handicapped are some examples. Therefore, studies on complex machines with structures similar to humans, known as humanoids robots, as well as emerging optimization metaheuristics have been increasing. The combination of these technologies may result in robust, safe, reliable, and flexible machines that can substitute humans in multiple tasks. In order to contribute to this topic, the static modeling of a humanoid robot and the optimization of its static force capability through a modified self-adaptive differential evolution (MSaDE) approach is proposed and evaluated in this study. Unlike the original SaDE, MSaDE employs a new combination of strategies and an adaptive scaling factor mechanism. In order to verify the effectiveness of the proposed MSaDE, a series of controlled experiments are performed. Moreover, some statistical tests are applied, an analysis of the results is carried out, and a comparative study of the MSaDE performance with other metaheuristics is presented. The results show that the proposed MSaDE is robust, and its performance is better than other powerful algorithms in the literature when applied to a humanoid robot model for the pushing and pulling tasks.

Cascaded evolutionary multiobjective identification based on correlation function statistical tests for improving velocity analyzes in swimming

By using biomechanical analyses applied to sports many researchers are providing important information to coaches and athletes in order to reach better performance in a shorter time. In swimming, these kinds of analyses are being used to evaluate, to detect and to improve the skills of high level athletes. Recently, evolutionary computing theories have been adopted to support swim velocity profile identification. Based on velocity profiles recognition, it is possible to identify distinct characteristics and classify swimmers according to their abilities. In this way, this work presents an application of Radial Basis Function Neural Network (RBF-NN) associated to a proposed cascaded evolutionary procedure composed by a genetic and Multiobjective Differential Evolution (MODE) algorithms as optimization method for searching the best fitness within a set of parameters to configure the RBF-NN. The main goal and novelty of the proposed approach is to enable, through the adoption of cascaded multiobjective optimization, the use of correlation based tests in order to select both the model lagged inputs and the associated parameters in a supervised fashion. Finally, the real data of a Brazilian elite female swimmer in crawl and breaststroke styles obtained into a 25 meters swimming pool have been identified by the proposed method. The soundness of the approach is illustrated with the adherence to the model validity tests and the values of the multiple correlation coefficients between 0.95 and 0.93 for two tests for both breaststroke and crawl strokes, respectively.

Cascaded free search differential evolution applied to nonlinear system identification based on correlation functions and neural networks

This paper presents a procedure for input selection and parameter estimation for system identification based on Radial Basis Functions Neural Networks (RBFNNs) models and Free Search Differential Evolution (FSDE). We adopt a cascaded evolutionary algorithm approach and problem decomposition to define the model orders and the related model parameters based on higher orders correlation functions. Thus, we adopt two distinct populations: the first to select the lags on the inputs and outputs of the system and the second to define the parameters for the RBFNN. We show the results when the proposed methodology is applied to model a coupled drives system with real acquired data. We use to this end the canonical binary genetic algorithm (selection of lags) and the recently proposed FSDE (definition of the model parameters), which is very convenient for the present problem for having few control parameters. The results show the validity of the approach when compared to a classical input selection algorithm.

Otimização da Capacidade de Força Estática de Robôs Humanoides Usando Metaheurísticas Bio-Inspiradas

 Due to the high rate of repetitive strain injury, work-related osteomuscular disturbances and domestic issues involving elderly and handicapped, the researches on the complex machines with similar structure to humans, known as humanoid robots, have been increasing as well as the emerging optimization metaheuristics on evolutionary computation and swarm intelligence fields. The combination of these technologies can result in machines able to substitute humans in certain tasks. The static modeling of a humanoid robot and the optimization of its static force capability through optimization metaheuristics widespread in the literature are both presented in this study. After a series of controlled experiments, some statistical tests are applied and an analysis of the results is carried out to indicate which of the metaheuristics presents the best performance regarding to the proposed optimization problem.

A Zaslavskii firefly approach applied to Loney's solenoid benchmark

Nature-inspired algorithms of the swarm intelligence field perform powerfully and efficiently in solving global optimization problems. Inspired by nature, these metaheuristic algorithms have obtained promising performance over continuous domains of optimization problems. Recently, a new swarm intelligence approach called firefly algorithm (FA) has emerged. The FA is a stochastic paradigm based on the idealized behavior of the flashing characteristics of fireflies. However, to achieve good performance with FA, the tuning of control parameters is essential as its performance is sensitive to the choice of the randomization parameter (α) setting. This paper introduces a FA approach combined with chaotic sequences generated by Zaslavskii map (FACZ) to tune the randomization parameter. Simulations of Loneys solenoid benchmark problem examine the effectiveness of the conventional FA and the proposed FACZ algorithms. Simulation results and comparisons with the FACZ demonstrated that the performance of the FA is promising in the Loneys solenoid case.

On the Improvement of Elite Swimmers Velocity Identification by Using Neural Network Associated to Multiobjective Optimization

Considering that technical skill is the major determinant characteristic of success among competitive swimmers, it is important to coaches to quantify the differences that make one swimmer more efficient than another. One of the most important grants in swimming is the velocity, which can be related to drag forces and provide substantial information about the swimmer technique. The main purpose of this study was to determine the best model to compare swimmers in terms of velocity. In this work, a Radial Basis Function Neural Network (RBF-NN) was used to model the nonlinearity of swim velocity time series. The RBF-NN parameters were adjusted by using four multiobjective optimization methods. The best results in terms of RBF-NN configuration were obtained by the Differential Evolution based algorithms.