Raúl M. del Toro

Self-adaptive systems: A survey of current approaches, research challenges and applications

Abstract Self-adaptive software is capable of evaluating and changing its own behavior, whenever the evaluation shows that the software is not accomplishing what it was intended to do, or when better functionality or performance may be possible. The topic of system adaptivity has been widely studied since the mid-60s and, over the past decade, several application areas and technologies relating to self-adaptivity have assumed greater importance. In all these initiatives, software has become the common element that introduces self-adaptability. Thus, the investigation of systematic software engineering approaches is necessary, in order to develop self-adaptive systems that may ideally be applied across multiple domains. The main goal of this study is to review recent progress on self-adaptivity from the standpoint of computer sciences and cybernetics, based on the analysis of state-of-the-art approaches reported in the literature. This review provides an over-arching, integrated view of computer science and software engineering foundations. Moreover, various methods and techniques currently applied in the design of self-adaptive systems are analyzed, as well as some European research initiatives and projects. Finally, the main bottlenecks for the effective application of self-adaptive technology, as well as a set of key research issues on this topic, are precisely identified, in order to overcome current constraints on the effective application of self-adaptivity in its emerging areas of application.

Optimal fuzzy control system using the cross-entropy method. A case study of a drilling process

This paper focuses on the optimal tuning of fuzzy control systems using the cross-entropy precise mathematical framework. The design of an optimal fuzzy controller for cutting force regulation in a network-based application and applied to the drilling process is described. The key issue is to obtain optimal fuzzy controller parameters that yield a fast and accurate response with minimum overshoot by minimising the integral time absolute error (ITAE) performance index. Simulation results show that the cross-entropy method does find the optimal solution (i.e. input scaling factors) very accurately, and it can be programmed and implemented very easily (few setting parameters). The results of a comparative study demonstrate that optimal tuning with the cross-entropy method provides a good transient response (without overshoot) and a better error-based performance index than simulated annealing [17], the Nelder-Mead method [14] and genetic algorithms [33]. The experimental results demonstrate that the proposed optimal fuzzy control provides outstanding transient response without overshoot, a small settling time and a minimum steady-state error. The application of optimal fuzzy control reduces rapid drill wear and catastrophic drill breakage due to the increasing and oscillatory cutting forces that occur as the drill depth increases.

System Identification of the High Performance Drilling Process for Network-Based Control

A simple, fast, network-based experimental procedure for identifying the dynamics of the high-performance drilling (HPD) process is proposed and successfully applied. This identification technique utilizes a single-input (feed rate), single-output (resultant force) system with a dual step input function. The model contains the delays of both the network architecture (a PROFIBUS type network) and the dead time related with the plant dynamic itself. Classical identification techniques are used to obtain first order, second order, and third order models on the basis of the recorded input/output data. The developed models relate the dynamic behavior of resultant force versus commanded feed rate in HPD. Model validation is performed through error-based performance indices and correlation analyses. Experimental verification is performed using two different work piece materials. The models match perfectly with real-time force behavior in drilling operations and are easily integrated with many control strategies. Furthermore, these results demonstrate that the HPD process is somewhat non-linear with a remarkable difference in gain due to work piece material; however, the dynamic behavior does not change significantly.Copyright

Using simulated annealing for optimal tuning of a PID controller for time-delay systems: an application to a high-performance drilling process

This paper shows a strategy based on simulated annealing for the optimal tuning of a PID controller to deal with time-varying delay. The main goal is to minimize the integral time absolute error (ITAE) performance index and the overshoot for a drilling-force control system. The proposed strategy is compared with other classic tuning rules (the Ziegler-Nichols and Cohen-Coon tuning formulas). Other tuning laws derived from genetic algorithms and the Simplex search algorithm for unconstrained optimization are also included in the comparative study. The results demonstrate that simulated annealing provides an optimal tuning of the PID controller, which means better transient response (less overshoot) and less ITAE than with other methods.

From artificial cognitive systems and open architectures to cognitive manufacturing systems

Considering constantly increasing demand for shift from mass production to mass customization and the need to maintain high level of automation despite permanent changes in manufacturing technologies and tools new approaches and solutions have to be provided in manufacturing. Cyber-Physical Systems and Industrial Internet of Things are enabling smart manufacturing to tackle the challenge of data processing, integration and interpretation, but beyond uniformed data collection and visualization. The cognitive approach is argued to introduce brain and biologically-inspired algorithms capable to better adapt industrial systems for unforeseen conditions. Such approach should provide flexible and robust solution for manufacturing systems, enabling new level of adaptability and re-configurability in the system by self-X capabilities. In this paper contemporary solutions applicable for introduction of cognitive capabilities in manufacturing systems are studied and the architecture for cognitive manufacturing system employing benefits of Industrial Internet and Cognitive Control is proposed.

Artificial cognitive control with self-x capabilities

This computational architecture is inspired and fed by recent progress in neuroscience.The design and implementation of self-learning and self-optimization capabilities.The implementation in a low-cost computational platform to facilitate technology transfer in industry. Nowadays, even though cognitive control architectures form an important area of research, there are many constraints on the broad application of cognitive control at an industrial level and very few systematic approaches truly inspired by biological processes, from the perspective of control engineering. Thus, our main purpose here is the emulation of human socio-cognitive skills, so as to approach control engineering problems in an effective way at an industrial level. The artificial cognitive control architecture that we propose, based on the shared circuits model of socio-cognitive skills, seeks to overcome limitations from the perspectives of computer science, neuroscience and systems engineering. The design and implementation of artificial cognitive control architecture is focused on four key areas: (i) self-optimization and self-leaning capabilities by estimation of distribution and reinforcement-learning mechanisms; (ii) portability and scalability based on low-cost computing platforms; (iii) connectivity based on middleware; and (iv) model-driven approaches. The results of simulation and real-time application to force control of micro-manufacturing processes are presented as a proof of concept. The proof of concept of force control yields good transient responses, short settling times and acceptable steady-state error. The artificial cognitive control architecture built into a low-cost computing platform demonstrates the suitability of its implementation in an industrial setup.

Self-adaptive strategy based on fuzzy control systems for improving performance in wireless sensors networks

The solutions to cope with new challenges that societies have to face nowadays involve providing smarter daily systems. To achieve this, technology has to evolve and leverage physical systems automatic interactions, with less human intervention. Technological paradigms like Internet of Things (IoT) and Cyber-Physical Systems (CPS) are providing reference models, architectures, approaches and tools that are to support cross-domain solutions. Thus, CPS based solutions will be applied in different application domains like e-Health, Smart Grid, Smart Transportation and so on, to assure the expected response from a complex system that relies on the smooth interaction and cooperation of diverse networked physical systems. The Wireless Sensors Networks (WSN) are a well-known wireless technology that are part of large CPS. The WSN aims at monitoring a physical system, object, (e.g., the environmental condition of a cargo container), and relaying data to the targeted processing element. The WSN communication reliability, as well as a restrained energy consumption, are expected features in a WSN. This paper shows the results obtained in a real WSN deployment, based on SunSPOT nodes, which carries out a fuzzy based control strategy to improve energy consumption while keeping communication reliability and computational resources usage among boundaries.

Connectivity control in WSN based on fuzzy logic control

The connectivity of a wireless sensor network (WSN), specified as the percentage of nodes that are able to reach the base station (BS) that relays nodes data to other networks, has to be kept as high as possible, without either increasing significantly the energy consumption or worsening the WSN overall performance. Modelling accurately a WSN and designing a control system for accomplishing the desired network connectivity is an effortful task. In this paper, an approach based on fuzzy logic control is proposed, as it provides a better trade-off between accuracy, effort and time. The control system running in each node will manage both the communication range to guarantee a minimum number of neighbors called node degree, and the node degree itself, depending on the nodes battery level at each moment. The fuzzy controller running in a node will monitor the own nodes parameters, without flooding WSN with monitoring messages.

Detecting Nano-Scale Vibrations in Rotating Devices by Using Advanced Computational Methods

This paper presents a computational method for detecting vibrations related to eccentricity in ultra precision rotation devices used for nano-scale manufacturing. The vibration is indirectly measured via a frequency domain analysis of the signal from a piezoelectric sensor attached to the stationary component of the rotating device. The algorithm searches for particular harmonic sequences associated with the eccentricity of the device rotation axis. The detected sequence is quantified and serves as input to a regression model that estimates the eccentricity. A case study presents the application of the computational algorithm during precision manufacturing processes.

Consensus-Based Cooperative Control Based on Pollution Sensing and Traffic Information for Urban Traffic Networks

Nowadays many studies are being conducted to develop solutions for improving the performance of urban traffic networks. One of the main challenges is the necessary cooperation among different entities such as vehicles or infrastructure systems and how to exploit the information available through networks of sensors deployed as infrastructures for smart cities. In this work an algorithm for cooperative control of urban subsystems is proposed to provide a solution for mobility problems in cities. The interconnected traffic lights controller (TLC) network adapts traffic lights cycles, based on traffic and air pollution sensory information, in order to improve the performance of urban traffic networks. The presence of air pollution in cities is not only caused by road traffic but there are other pollution sources that contribute to increase or decrease the pollution level. Due to the distributed and heterogeneous nature of the different components involved, a system of systems engineering approach is applied to design a consensus-based control algorithm. The designed control strategy contains a consensus-based component that uses the information shared in the network for reaching a consensus in the state of TLC network components. Discrete event systems specification is applied for modelling and simulation. The proposed solution is assessed by simulation studies with very promising results to deal with simultaneous responses to both pollution levels and traffic flows in urban traffic networks.