José Boaventura-Cunha

Framework Using ROS and SimTwo Simulator for Realistic Test of Mobile Robot Controllers

In robotics, a reliable simulation tool is an important design and test resource because the performance of algorithms is evaluated before being implemented in real mobile robots. The virtual environment makes it possible to conduct extensive experiments in controlled scenarios, without the dependence of a physical platform, in a faster and inexpensive way. Although, simulators should be able to represent all the relevant characteristics that are present in the real environment, like dynamic (shape, mass, surface friction, etc.), impact simulation, realistic noise, among other factors, in order to guarantee the accuracy and reliability of the results.

A Multilayer Model Predictive Control Methodology Applied to a Biomass Supply Chain Operational Level

Forest biomass has gained increasing interest in the recent years as a renewable source of energy in the context of climate changes and continuous rising of fossil fuels prices. However, due to its characteristics such as seasonality, low density, and high cost, the biomass supply chain needs further optimization to become more competitive in the current energetic market. In this sense and taking into consideration the fact that the transportation is the process that accounts for the higher parcel in the biomass supply chain costs, this work proposes a multilayer model predictive control based strategy to improve the performance of this process at the operational level. The proposed strategy aims to improve the overall supply chain performance by forecasting the system evolution using behavioural dynamic models. In this way, it is possible to react beforehand and avoid expensive impacts in the tasks execution. The methodology is composed of two interconnected levels that closely monitor the system state update, in the operational level, and delineate a new routing and scheduling plan in case of an expected deviation from the original one. By applying this approach to an experimental case study, the concept of the proposed methodology was proven. This novel strategy enables the online scheduling of the supply chain transport operation using a predictive approach.

Distributed monitoring system for precision enology of the Tawny Port wine aging process

Abstract Aging of Tawny Port wine is a multifactorial process critical for attaining the desired quality. Real time monitoring of important intrinsic and extrinsic factors that are known to affect the time and quality of the aging process are important to optimize and to manage the natural variability between wines aged in different long-used wood barrels. For this study, a distributed monitoring system was installed in sixteen oak barrels, placed in two adjacent wineries – one of them with controlled temperature – in the Douro Demarcated Region, Portugal. The monitoring process was performed using a RS-485 industrial network, which interconnects sensors that continuously measure wine temperature, pH, redox potential and wine’s dissolved oxygen, as well as other sensors that measure parameters related to the barrels’ environmental context, such as room temperature and relative humidity. This work presents the design, development and implementation of a remote distributed system to monitor such parameters, aiming to determine the existence of behaviour models for Port Tawny wine during aging in long-used oak barrels, depending on their storage history and to understand the evolution of wine pH, dissolved oxygen and redox potential in real winery conditions as well as their dependence on the wine’s storage temperature. This approach is based on easy-to-use embedded systems, with the aim of giving a relevant contribution to other projects in the area of precision enology.

Automation and Control in Greenhouses: State-of-the-Art and Future Trends

This paper presents the state-of-the-art in terms of automation and control for protected cultivation in greenhouses. Aspects such as modeling, instrumentation, energy optimization and applied robotics are considered, aiming at not only to identify latest research topics, but also to foster continuous improvement in key cutting-edge problems.

Model Predictive Control Applied to a Supply Chain Management Problem

Supply chains are ubiquitous in any commercial delivery systems. The exchange of goods and services, from different supply points to distinct destinations scattered along a given geographical area, requires the management of stocks and vehicles fleets in order to minimize costs while maintaining good quality services. Even if the operating conditions remain constant over a given time horizon, managing a supply chain is a very complex task. Its complexity increases exponentially with both the number of network nodes and the dynamical operational changes. Moreover, the management system must be adaptive in order to easily cope with several disturbances such as machinery and vehicles breakdowns or changes in demand. This work proposes the use of a model predictive control paradigm in order to tackle the above referred issues. The obtained simulation results suggest that this strategy promotes an easy tasks rescheduling in case of disturbances or anticipated changes in operating conditions.

Sliding Mode Generalized Predictive Control Based on Dual Optimization

This work presents a new approach to tune the parameters of the discontinuous component of the Sliding Mode Generalized Predictive Controller (SMGPC) subject to constraints. The strategy employs Particle Swarm Optimization (PSO) to minimize a second aggregated cost function. The continuous component is obtained by the standard procedure, by Sequential Quadratic Programming (SQP), thus yielding a dual optimization scheme. Simulations and performance indexes for a non minimum linear model result in a better performance, improving robustness and tracking accuracy.

Extended Stability Conditions for CDM Controller Design

The coefficient diagram method (CDM) is one of the easiest methods for model based control system design. Its core is based on an algebraic method but it also encompasses a graphical analysis diagram that helps the user to evaluate the three main closed-loop system requirements: dynamic behaviour, robustness and stability. This later characteristic is analysed by a set of stability conditions derived from the previous work of Lipatov and Sokolov on sufficient conditions for stability. However, in CDM, only a fraction of the total conditions are considered. This work will show that this fact increases the inconclusive area within the stability space. Moreover an extended set of CDM stability conditions, in conjunction with its graphical interpretation, will be presented.

An analytical approach to assess the influence of the massive wall material, thickness and ventilation system on the Trombe wall thermal performance

The influence of the massive wall material, thickness and ventilation system on the Trombe wall thermal performance was analysed based on an analytical methodology. Results obtained from experimental work will also be added to this study. During the heating season, for the non-ventilated Trombe wall, the global heat gains decrease is not proportional to the thickness increase, and this ratio depends on the massive wall material heat storage capacity. A ventilation system in the massive wall leads to higher heat gains due to the air convection, but this growth is not in the same proportion for the different materials. If solid brick or earth is used, heat gain values are much higher than those obtained if there is no ventilation system, increasing to the double in the case of earth and 2.5 times more in the case of solid brick. When the massive wall is ventilated and made of granite, an increase in the gains of 44.06% is obtained when compared with the non-ventilated. During the cooling season, closing the ventilation system and the external shutter leads to heat gains considerably lower than those obtained during the heating season. In this case, earth can be a suitable material.

Digital Technologies for Forest Supply Chain Optimization: Existing Solutions and Future Trends

The role of digital technologies for fostering sustainability and efficiency in forest-based supply chains is well acknowledged and motivated several studies in the scope of precision forestry. Sensor technologies can collect relevant data in forest-based supply chains, comprising all activities from within forests and the production of the woody raw material to its transformation into marketable forest-based products. Advanced planning systems can help to support decisions of the various entities in the supply chain, e.g., forest owners, harvest companies, haulage companies, and forest product processing industry. Such tools can help to deal with the complex interdependencies between different entities, often with opposing objectives and actions—which may increase efficiency of forest-based supply chains. This paper analyzes contemporary literature dealing with digital technologies in forest-based supply chains and summarizes the state-of-the-art digital technologies for real-time data collection on forests, product flows, and forest operations, as well as planning systems and other decision support systems in use by supply chain actors. Higher sustainability and efficiency of forest-based supply chains require a seamless information flow to foster integrated planning of the activities over the supply chain—thereby facilitating seamless data exchange between the supply chain entities and foster new forms of collaboration. Therefore, this paper deals with data exchange and multi-entity collaboration aspects in combination with interoperability challenges related with the integration among multiple process data collection tools and advanced planning systems. Finally, this interdisciplinary review leads to the discussion of relevant guidelines that can guide future research and integration projects in this domain.

Swarm-based auto-tuning of PID posicast control for uncertain systems

Posicast feedback control systems are very sensitive to model uncertainty. This paper proposes the use of Particle Swarm Optimization (PSO) to auto-tune two-degrees of freedom control systems. The system considers as a pre-filter a half-cycle Posicast command shaper and a PID controller in the feedback loop. A model reference technique is proposed to track differences among model and system to be controlled, feeding a decision block which will trigger an auto-tuning optimization mechanism. Preliminary simulation results are presented showing the proposed technique effectiveness to deal with prescribed plant uncertainties.