Alberto Cardoso

The GINSENG system for wireless monitoring and control: Design and deployment experiences

Todays industrial facilities, such as oil refineries, chemical plants, and factories, rely on wired sensor systems to monitor and control the production processes. The deployment and maintenance of such cabled systems is expensive and inflexible. It is, therefore, desirable to replace or augment these systems using wireless technology, which requires us to overcome significant technical challenges. Process automation and control applications are mission-critical and require timely and reliable data delivery, which is difficult to provide in industrial environments with harsh radio environments. In this article, we present the GINSENG system which implements performance control to allow us to use wireless sensor networks for mission-critical applications in industrial environments. GINSENG is a complete system solution that comprises on-node system software, network protocols, and back-end systems with sophisticated data processing capability. GINSENG assumes that a deployment can be carefully planned. A TDMA-based MAC protocol, tailored to the deployment environment, is employed to provide reliable and timely data delivery. Performance debugging components are used to unintrusively monitor the system performance and identify problems as they occur. The article reports on a real-world deployment of GINSENG in an especially challenging environment of an operational oil refinery in Sines, Portugal. We provide experimental results from this deployment and share the experiences gained. These results demonstate the use of GINSENG for sensing and actuation and allow an assessment of its ability to operate within the required performance bounds. We also identify shortcomings that manifested during the evaluation phase, thus giving a useful perspective on the challenges that have to be overcome in these harsh application settings.

Supervision and c-Means clustering of PID controllers for a solar power plant

Abstract A hierarchical control strategy consisting on a supervisory switching of PID controllers, simplified using the c-Means clustering technique, is developed and applied to the distributed collector field of a solar power plant. The main characteristic of this solar plant is that the primary energy source, the solar radiation, cannot be manipulated. It varies throughout the day, causing changes in plant dynamics conducting to distinct several operating points. To guarantee good performances in all operating points, a local PID controller is tuned to each operating point and a supervisory strategy is proposed and applied to switch among these controllers accordingly to the actual measured conditions. Each PID controller has been tuned off-line, by the combination of a dynamic recurrent non-linear neural network model with a pole placement control design. To reduce the number of local controllers, to be selected by the supervisor, a c-Means clustering technique was used. Simulation and experimental results, obtained at Plataforma Solar de Almeria, Spain, are presented showing the effectiveness of the proposed approach.

Gain Tuning of Fuzzy PID Controllers for MIMO Systems: A Performance-Driven Approach

A new methodology for tuning the scaling factors, or gains, of fuzzy proportional-integral-derivative controllers, by taking explicitly into account the closed-loop system performance is proposed in this study. The solution is obtained by solving a nonlinear constrained optimization problem, considering a set of constraints on the scaling factors of the Mamdani-type fuzzy system, and on the plants inputs and outputs. Two distinct approaches are presented, which are associated with the optimization being carried out offline or in real time. The offline tuning scheme assumes the system dynamics described by a nonlinear model, while for the real-time implementation, the plants dynamics is locally approximated by a linear model, with the underlying parameters recursively updated. In order to cope with rather stringent sampling time requirements, the constrained online optimization problem is implemented based on the grid computing paradigm. Given the adaptive nature of the real-time scheme, time-varying dynamics and unknown disturbances can be accommodated in such a way that the closed-loop performance is effectively maximized, while avoiding wind-up phenomena induced by the integrator term. The proposed tuning methodologies are assessed on a benchmark three-tank system and compared against a conventional-based tuning approach. Results from experiments illustrate the feasibility of the proposed approaches and also all the relevance in optimal control systems based on Mamdani-type fuzzy controllers.

Affine Neural Network-Based Predictive Control Applied to a Distributed Solar Collector Field

This paper presents experimental results concerning the control of a distributed solar collector field, where the main objective concerns the regulation of the outlet oil temperature by suitably manipulating the oil flow rate. This is achieved by means of a constrained nonlinear adaptive model-based predictive control framework where the control action sequence is obtained by solving an open-loop optimization problem, subject to a set of constraints. The plant dynamics is approximated by an affine state-space neural network, whose complexity is specified in terms of the cardinality of dominant singular values associated with a subspace oblique projection of data-driven Hankel matrices. The neural network is first trained offline and subsequently improved through a recursive updating of its weights and biases, based on a dual unscented Kalman filter. The control scheme is implemented on the Acurex field of the Plataforma Solar de Almería, Spain. Results from these experiments demonstrate the feasibility of the proposed framework, and highlight the ability to cope with time-varying and unmodeled dynamics, under the form of disturbances, and its inherent capability for accommodating actuation faults.

WSN evaluation in industrial environments first results and lessons learned

The GINSENG project develops performance-controlled wireless sensor networks that can be used for time-critical applications in hostile environments such as industrial plant automation and control. GINSENG aims at integrating wireless sensor networks with existing enterprise resource management solutions using a middleware. A cornerstone is the evaluation in a challenging industrial environment — an oil refinery in Portugal. In this paper we first present our testbed. Then we introduce our solution to access, debug and flash the sensor nodes remotely from an operations room in the plant or from any location with internet access. We further present our experimental methodology and show some exemplary results from the refinery testbed.

Estimating the potential water reuse based on fuzzy reasoning

Studies worldwide suggest that the risk of water shortage in regions affected by climate change is growing. Decision support tools can help governments to identify future water supply problems in order to plan mitigation measures. Treated wastewater is considered a suitable alternative water resource and it is used for non-potable applications in many dry regions around the world. This work describes a decision support system (DSS) that was developed to identify current water reuse potential and the variables that determine the reclamation level. The DSS uses fuzzy inference system (FIS) as a tool and multi-criteria decision making is the conceptual approach behind the DSS. It was observed that water reuse level seems to be related to environmental factors such as drought, water exploitation index, water use, population density and the wastewater treatment rate, among others. A dataset was built to analyze these features through water reuse potential with a FIS that considered 155 regions and 183 cities. Despite some inexact fit between the classification and simulation data for agricultural and urban water reuse potential it was found that the FIS was suitable to identify the water reuse trend. Information on the water reuse potential is important because it issues a warning about future water supply needs based on climate change scenarios, which helps to support decision making with a view to tackling water shortage.

Simulation platform for Wireless Sensor Networks based on the TrueTime toolbox

Research on wireless sensor networks (WSN) has received considerable attention in the last few years due to their unique characteristics, including, flexibility, self-organization, easy deployment, which make them an ideal candidate for low-cost monitoring. In order to help WSN planning and to enable the design of new protocols and applications and assess their performance, several existent simulation platforms have been extended to include simulation frameworks for WSN. In this paper, a higher level simulation platform for WSN is proposed based on the truetime toolbox. Relevant features include graphical representation of communication components, wireless communication and battery-driven operation. Special attention was paid to the 3D graphical interface, simulator interactivity and its extendibility. Simulation results demonstrate the applicability and usability of the proposed simulation platform.

Scheduling of PID controllers by means of a neural network with application to a solar power plant

This paper concerns the application of a neural network control strategy to the distributed collector field of a solar power plant The neural network is trained based on measured data from the plant providing a way of scheduling between a set of PID controllers, a priori timed in different operating points by means of Takahashi rules. The present work consists in a hybrid scheme combining the potentialities of neural networks for approximation purposes with the well-know theory and widespread industrial application of BID techniques. Experimental results collected at Plataforma Solar de Almeria (Spain), show the effectiveness of the proposed approach.

A support vector machine based technique for online detection of outliers in transient time series

This paper deals with online detection and accommodation of outliers in transient time series by appealing to a machine learning technique. The methodology is based on a Least Squares Support Vector Machine technique together with a sliding window-based learning algorithm. A modification to this method is proposed so as to extend its application to transient raw data collected from transmitters attached to a Wireless Sensor Network. The performance of two approaches are compared on a particular controlled data set.

A distributed multi-agent approach for resilient supervision over a IPv6 WSAN infrastructure

Wireless Sensor and Actuator Networks has become an important area of research. They can provide flexibility, low operational and maintenance costs and they are inherently scalable. In the realm of Internet of Things the majority of devices is able to communicate with one another, and in some cases they can be deployed with an IP address. This feature is undoubtedly very beneficial in wireless sensor and actuator networks applications, such as monitoring and control systems. However, this kind of communication infrastructure is rather challenging as it can compromise the overall system performance due to several factors, namely outliers, intermittent communication breakdown or security issues. In order to improve the overall resilience of the system, this work proposes a distributed hierarchical multi-agent architecture implemented over a IPv6 communication infrastructure. The Contiki Operating System and RPL routing protocol were used together to provide a IPv6 based communication between nodes and an external network. Experimental results collected from a laboratory IPv6 based WSAN test-bed, show the relevance and benefits of the proposed methodology to cope with communication loss between nodes and the server.