Group of advanced Automation of Machines, Highly complex processes and Environments

Cloud-based Industrial Cyber-Physical System for Data-driven Reasoning. A Review and Use Case on an Industry 4.0 Pilot Line.


Nowadays, reconfiguration and adaptation by means of optimal re-parametrization in industrial cyber-physical systems (ICPS) is one of the bottlenecks for the digital transformation of the manufacturing industry. This work proposes a cloud-to-edges-based ICPS equipped with machine learning techniques. The proposed reasoning module includes a learning procedure based on two reinforcement learning techniques, running in parallel, for updating both the data-conditioning and processing strategy and the prediction model. The presented solution distributes computational resources and analytic engines in multiple layers and independent modules increasing the smartness and the autonomy for monitoring and control the behavior at shop floor level. The suitability of the proposed solution, evaluated in a pilot line, is endorsed by fast time response (i.e., 0.01s at the edge level) and the appropriate setting of optimal operational parameters for guaranteeing the desired quality surface roughness during macro and micro milling operations.

FURTHER DETAILS ON: A. Villalonga, G. Beruvides, F. Castaño and R. E. Haber, "Cloud-Based Industrial Cyber–Physical System for Data-Driven Reasoning: A Review and Use Case on an Industry 4.0 Pilot Line," in IEEE Transactions on Industrial Informatics, vol. 16, no. 9, pp. 5975-5984, Sept. 2020. https://doi.org10.1109/TII.2020.2971057

Computational Intelligence for Simulating a LiDAR Sensor: Cyber-Physical and Internet-of-Things Automotive Application


In this chapter, an overview of some of the most commonly computational intelligence techniques used to provide new capabilities to sensor networks in Cyber-Physical and Internet-of-Things environments, and for verifying and evaluating the reliability issues of sensor networks is presented. Nowadays, on-chip Light Detection and Ranging (LiDAR) concept has driven a great technological challenge into sensor networks application for Cyber-Physical and Internet-of-Things systems. Therefore, the modelling and simulation of a LiDAR sensor networks is also included in this chapter that is structured as follows. First, a brief description of the theoretical modelling of the mathematical principle of operation is outlined. Subsequently, a review of the state-of-the-art of computational intelligence techniques in sensor system simulations is explained. Likewise, a use case of applying computational intelligence techniques to LiDAR sensor networks in a Cyber-Physical System environment is presented. In this use case, a model library with four specific artificial intelligence-based methods is also designed based on sensory information database provided by the LiDAR simulation. Some of them are multi-layer perceptron neural network, a self-organization map, a support vector machine, and a k-nearest neighbour. The results demonstrate the suitability of using computational intelligence methods to increase the reliability of sensor networks when addressing the key challenges of safety and security in automotive applications.

FURTHER DETAILS ON: Castaño F., Beruvides G., Villalonga A., Haber R.E. (2020) Computational Intelligence for Simulating a LiDAR Sensor. In: van Driel W., Pyper O., Schumann C. (eds) Sensor Systems Simulations. Springer, Cham.

Sensor Reliability in Cyber-Physical Systems Using Internet-of-Things Data: A Review and Case Study


Nowadays, reliability of sensors is one of the most important challenges for widespread application of Internet-of-things data in key emerging fields such as the automotive and manufacturing sectors. This paper presents a brief review of the main research and innovation actions at the European level, as well as some on-going research related to sensor reliability in cyber-physical systems (CPS). The research reported in this paper is also focused on the design of a procedure for evaluating the reliability of Internet-of-Things sensors in a cyber-physical system. The results of a case study of sensor reliability assessment in an autonomous driving scenario for the automotive sector are also shown. A co-simulation framework is designed in order to enable real-time interaction between virtual and real sensors. The case study consists of an IoT LiDAR-based collaborative map in order to assess the CPS-based co-simulation framework. Specifically, the sensor chosen is the Ibeo Lux 4-layer LiDAR sensor with IoT added capabilities. The modeling library for predicting error with machine learning methods is implemented at a local level, and a self-learning-procedure for decision-making based on Q-learning runs at a global level. The study supporting the experimental evaluation of the co-simulation framework is presented using simulated and real data. The results demonstrate the effectiveness of the proposed method for increasing sensor reliability in cyber-physical systems using Internet-of-Things data.

FURTHER DETAILS ON: Castaño, F.; Strzelczak, S.; Villalonga, A.; Haber, R.E.; Kossakowska, J. Sensor Reliability in Cyber-Physical Systems Using Internet-of-Things Data: A Review and Case Study. Remote Sens. 2019, 11, 2252

Automatic Selection of Optimal Parameters Based on Simple Soft-Computing Methods: A Case Study of Micromilling Processes.


Nowadays, the application of novel soft-computing methods to new industrial processes is often limited by the actual capacity of the industry to assimilate state-of-the-art computational methods. The selection of optimal parameters for efficient operation is very challenging in microscale manufacturing processes, because of intrinsic nonlinear behavior and reduced dimensions. In this paper, a decision-making system for selecting optimal parameters in micromilling operations is designed and implemented using simple and efficient soft-computing techniques. The procedure primarily consists of four steps: an experimental characterization; the modeling of cutting force and surface roughness by means of a multilayer perceptron; multiobjective optimization using the cross-entropy method, taking into account productivity and surface quality; and a decision-making procedure for selecting the most appropriate parameters using a fuzzy inference system. Finally, two different alloys for micromilling processes are considered, in order to evaluate the proposed system: a titanium-based alloy and a tungsten-copper alloy. The experimental study demonstrated the effectiveness of the proposed solution for automated decision-making, based on simple soft-computing methods, and its successful application to a real-life industrial challenge.

FURTHER DETAILS ON: I. la Fé-Perdomo, G. Beruvides, R. Quiza, R. Haber and M. Rivas, "Automatic Selection of Optimal Parameters Based on Simple Soft-Computing Methods: A Case Study of Micromilling Processes," in IEEE Transactions on Industrial Informatics, vol. 15, no. 2, pp. 800-811, Feb. 2019.

Digital Twin-Based Optimization for Ultraprecision Motion Systems with Backlash and Friction


A digital twin-based optimization procedure is presented for an ultraprecision motion system with a flexible shaft connecting the motor to the (elastic) load, which is subject to both backlash and friction. The main contributions of the study are the design of the digital twin and its implementation, assuming a two-mass drive system. The procedure includes the virtual representation of mechanical and electrical components, non-linearities (backlash and friction), and the corresponding control system. A procedure for digital twin-based optimization is also presented, in which the maximum absolute position error is minimized while maintaining accuracy and with no significant increase in the control effort. The optimal settings for the controller parameters and for the backlash peak amplitude, the backlash peak time, and the hysteresis amplitude are then determined, in order to guarantee an appropriate dynamic response in the presence of backlash and friction. The surface quality of certain manufactured components, such as hip and knee implants, depends on the smoothness and the accuracy of the real trajectory produced in the cutting process that is strongly influenced by the maximum position error. Simulations and experimental studies are presented using a real platform and two references for trajectory control, as well as a comparison of four digital twin-based optimization methods. The simulation study and the real-time experiments demonstrate the suitability of the digital twin-based optimization procedure and lay the foundations for the implementation of the proposed method at an industrial level.

FURTHER DETAILS ON: R. H. Guerra, R. Quiza, A. Villalonga, J. Arenas and F. Castaño, "Digital Twin-Based Optimization for Ultraprecision Motion Systems With Backlash and Friction," in IEEE Access, vol. 7, pp. 93462-93472, 2019,

Self-Tuning Method for Increased Obstacle Detection Reliability Based on Internet of Things LiDAR Sensor Models.


On-chip LiDAR sensors for vehicle collision avoidance are a rapidly expanding area of research and development. The assessment of reliable obstacle detection using data collected by LiDAR sensors has become a key issue that the scientific community is actively exploring. The design of a self-tuning methodology and its implementation are presented in this paper, to maximize the reliability of LiDAR sensors network for obstacle detection in the 'Internet of Things' (IoT) mobility scenarios. The Webots Automobile 3D simulation tool for emulating sensor interaction in complex driving environments is selected in order to achieve that objective. Furthermore, a model-based framework is defined that employs a point-cloud clustering technique, and an error-based prediction model library that is composed of a multilayer perceptron neural network, and k-nearest neighbors and linear regression models. Finally, a reinforcement learning technique, specifically a Q-learning method, is implemented to determine the number of LiDAR sensors that are required to increase sensor reliability for obstacle localization tasks. In addition, a IoT driving assistance user scenario, connecting a five LiDAR sensor network is designed and implemented to validate the accuracy of the computational intelligence-based framework. The results demonstrated that the self-tuning method is an appropriate strategy to increase the reliability of the sensor network while minimizing detection thresholds.

FURTHER DETAILS ON: Castaño, F.; Beruvides, G.; Villalonga, A.; Haber, R.E. Self-Tuning Method for Increased Obstacle Detection Reliability Based on Internet of Things LiDAR Sensor Models. Sensors 2018, 18, 1508.

Coping with complexity when predicting surface roughness in milling processes: Hybrid incremental model with optimal parametrization.


The complexity of machining processes relies on the inherent physical mechanisms governing these processes including nonlinear, emergent, and time-variant behavior. The measurement of surface roughness is a critical step done offline by expensive quality control procedures. The surface roughness prediction using an online efficient computational method is a difficult task due to the complexity of machining processes. The paradigm of hybrid incremental modeling makes it possible to address the complexity and nonlinear behavior of machining processes. Parametrization of models is, however, one bottleneck for full deployment of solutions, and the optimal setting of model parameters becomes an essential task. This paper presents a method based on simulated annealing for optimal parameters tuning of the hybrid incremental model. The hybrid incremental modeling plus simulated annealing is applied for predicting the surface roughness in milling processes. Two comparative studies to assess the accuracy and overall quality of the proposed strategy are carried out. The first comparative demonstrates that the proposed strategy is more accurate than theoretical, energy-based, and Taguchi models for predicting surface roughness. The second study also corroborates that hybrid incremental model plus simulated annealing is better than a Bayesian network and a multilayer perceptron for correctly predicting the surface roughness.

FURTHER DETAILS ON: Beruvides, G., Castaño, F., Haber, R. E., Quiza, R., & Villalonga, A. (2017). Coping with Complexity When Predicting Surface Roughness in Milling Processes: Hybrid Incremental Model with Optimal Parametrization. Complexity.

Obstacle Recognition Based on Machine Learning for On-Chip LiDAR Sensors in a Cyber-Physical System


Collision avoidance is an important feature in advanced driver-assistance systems, aimed at providing correct, timely and reliable warnings before an imminent collision (with objects, vehicles, pedestrians, etc.). The obstacle recognition library is designed and implemented to address the design and evaluation of obstacle detection in a transportation cyber-physical system. The library is integrated into a co-simulation framework that is supported on the interaction between SCANeR software and Matlab/Simulink. From the best of the authors’ knowledge, two main contributions are reported in this paper. Firstly, the modelling and simulation of virtual on-chip light detection and ranging sensors in a cyber-physical system, for traffic scenarios, is presented. The cyber-physical system is designed and implemented in SCANeR. Secondly, three specific artificial intelligence-based methods for obstacle recognition libraries are also designed and applied using a sensory information database provided by SCANeR. The computational library has three methods for obstacle detection: a multi-layer perceptron neural network, a self-organization map and a support vector machine. Finally, a comparison among these methods under different weather conditions is presented, with very promising results in terms of accuracy. The best results are achieved using the multi-layer perceptron in sunny and foggy conditions, the support vector machine in rainy conditions and the self-organized map in snowy conditions.

FURTHER DETAILS ON: Castaño, F.; Beruvides, G.; Haber, R.E.; Artuñedo, A. Obstacle Recognition Based on Machine Learning for On-Chip LiDAR Sensors in a Cyber-Physical System. Sensors 2017

Characterization of tool-workpiece contact during the micromachining of conductive materials


The characterization of dynamic cutting in micro-machining operations is essential for real-time monitoring of tool performance. The analysis of tool-edge/material contact and its electrical resistivity is therefore an interesting avenue of research for monitoring tool-workpiece interaction. This study examines mechanical cutting operations in micromilling operations that remove material to meet the design requirements of conductive parts. It draws from previous research into the theoretical models of cutting mechanisms in milling operations, to present a mathematical characterization of the tool-edge/material contact area. The rationale behind this research is that the contact area between two conductive materials is one of the main factors in determining the magnitude of resistance to the flow of an electric current between both materials. The study also offers a theoretical analysis of tool-edge radial immersion angles on entry and exit and their dynamic behavior. The analysis is mainly centered on cutting operations and cutting-time intervals, where tool-material contact is intermittent. Our theoretical analysis is experimentally corroborated by measuring tool-edge immersion time and tool-edge/material contact time. Promising results are reported that contribute to the development of a technological method for high-precision, real-time monitoring of tool-workpiece interaction and cutting detection in micromachining operations.

FURTHER DETAILS ON: Castaño, F., Haber, R. E., & del Toro, R. M. (2017). “Characterization of tool-workpiece contact during the micromachining of conductive materials”. Mechanical Systems and Signal Processing, 83, 489–505.



Nowadays, the micrometric and nanometric dimensional precision of industrial components is a common feature of micro-milling manufacturing processes. Hence, great importance is given to such aspects as online metrology and real-time monitoring systems for accurate control of surface roughness and dimensional quality. A real-time monitoring system is proposed here to predict surface roughness with an estimation error of 9.5%, by using the vibration signal that is emitted during the milling process. In the experimental setup, the z-axis component vibration is measured using two different diameters under several cutting conditions. Then, an adaptive neuro-fuzzy inference system model is implemented for modeling surface roughness, yielding a high goodness of fit indices and a good generalization capability. Finally, the optimization process is carried out by considering two contradictory objectives: unit machining time and surface roughness. A multi-objective genetic algorithm is used to solve the optimization problem, obtaining a set of non-dominated solutions. Pareto front representation is a useful decision-making tool for operators and technicians in the micro-milling process. An example of the Pareto front utility-based approach that selects two points close to both extreme ends of the frontier is described in the paper. In the first case (point 1), machine time is of greater importance, and in the second case (point 2), importance is attached to surface roughness. In general terms, users can select different combinations, at all times moving along the Pareto front.

FURTHER DETAILS ON: G. Beruvides, F. Castaño, R. Quiza, and R. E. Haber, "Surface roughness modeling and optimization of tungsten–copper alloys in micro-milling processes," Measurement, vol. 86, pp. 246-252, 2016/05/01/ 2016. 



The strong points of Estimation-of Distribution algorithms (EDAs) and specifically cross-entropy methods are widely acknowledged. One of the main advantages of EDAs is that the fusion of prior information into the optimization procedure is straight forward, there by reducing convergence time when such information is available. This study presents the modified Multi-Objective Cross-Entropy (MOCE+) method, based on a new procedure for addressing constraints: (i) the use of variable cut off values for selecting the elitist population; and, (ii) filtering of the elitist population after each epoch. We study the proposed method in different test suites and compare its performance with some other well-known optimization methods. The comparative study demonstrates the good figures of merit of the MOCE+ method in complex test suites. Finally, the proposed method is applied to the multi-objective optimization of a micro-drilling process. Two conflicting targets are considered: total drilling time and vibrations on the plane that is perpendicular to the drilling axis. The Pareto front, obtained through the optimization process, is analyzed through quality metrics and the available options in the decision-making process. Overall, the quality metrics of the MOCE+ method were better than the metrics of the other optimization methods considered in this work. The reported optimization of the micro-drilling process with the proposed method could potentially have a direct impact on improvements in industrial efficiency.

FURTHER DETAILS ON: Beruvides, G.; Quiza, R.; Haber, R. "Multi-objective optimization based on an improved cross-entropy method. A case study of a micro-scale manufacturing process" Information Sciences, Springer, 334-335, 9, pp. 161-173, 2015, DOI: 10.1016/j.ins.2015.11.040.



A first approach for designing and implementing an artificial cognitive control system based on the shared circuit models is presented in this work. The shared circuits model approach of sociocognitive capacities recently proposed by Hurley in The shared circuits model (SCM): how control, mirroring, and simulation can enable imitation, deliberation, and mindreading. Behavioral and Brain Sciences 31(1) (2008) 122 is enriched and improved in this work. A five-layer computational architecture for designing artificial cognitive control systems is proposed on the basis of a modified shared circuits model for emulating sociocognitive experiences such as imitation, deliberation, and mindreading. In order to show the enormous potential of this approach, a simplified implementation is applied to a case study. An artificial cognitive control system is applied for controlling force in a manufacturing process that demonstrates the suitability of the suggested approach.

FURTHER DETAILS ON: A. Sánchez Boza, R. H. Guerra, and A. Gajate, "Artificial cognitive control system based on the shared circuits model of sociocognitive capacities. A first approach," Engineering Applications of Artificial Intelligence, vol. 24, pp. 209-219, 2011.



The tuning of a fuzzy controller aims at achieving a suitable closed-loop response according to certain design specifications that are usually dictated by the system performance required. Last decades reflect the enormous effort of the scientific community towards clear and effective tuning strategies for fuzzy control systems. One of the main design difficulties is the setting of initial fuzzy systems (membership functions, number of rules, etc.). When some input–output data are available, the use of neuro-fuzzy techniques and fuzzy clustering strategies becomes powerful computational tools to accomplish an appropriate design. Another crucial issue is the setting of fuzzy control parameters such as scaling factors, which is not a trivial operation. If a performance indices or figure of merits are well defined from a control and computational viewpoints, and a rough mathematical description of the process dynamics is available, the use of an optimization technique is one of the most appropriate strategies for optimal tuning of fuzzy and neuro-fuzzy systems.In particular, this chapter is focused on the use of learning and gradient-free optimization techniques for optimal tuning. Two case studies are also reported in order to demonstrate the suitability of the proposed strategies.

FURTHER DETAILS ON: "Intelligent Tuning of Fuzzy Controllers by Learning and Optimization" , book Fuzzy Modeling and Control: Theory and Applications, Volume 9, 2014, pp 135-158.



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.

FURTHER DETAILS ON: F. D. Macías-Escrivá, R. Haber, R. Del Toro, and V. Hernandez, "Self-adaptive systems: A survey of current approaches, research challenges and applications," Expert Systems with Applications, vol. 40, pp. 7267-7279, 2013.



At sites of Hyundai technology gensets installed in Cuba there have been some operational problems that prevent a quick and accurate response to different operators occurring situations.For example, viscosity regulators on the banks of the sites have suffered failures due to vibration and high temperatures, causing disruptions, increased fuel consumption, disruption in energy supply, increased operating costs, increased frequency maintenance, affected the effectiveness of the Group and shortening their life, becoming more sensitive to be linked to the national system.In addition, due to poor performance and poor location of thermometers makers measure the temperature of the gases leaving the cylinder engines there is a growing tendency to irreparable damage to the same, since in some cases the reading is not is possible and the reliability of the measurement is not guaranteed. This paper presents a solution to the above problems through monitoring tool that allows to incorporate the implementation of monitoring and control of temperature measurements, the ability to interact with the viscosity, new alarms and corrective actions to the operator must be proposed perform.The actions outlined above are expected to contribute to improving energy availability of this technology groups. In addition to providing energy efficiency, the spending is removed by continuous valve procurement, installation and maintenance. With regard to the viscosity of the site operation is guaranteed full capacity and saves on the purchase of instruments such as viscometers.

FURTHER DETAILS ON: Y. Llosas Pardo, R. Haber-Guerra, A. Cobos-Castro, Y. Llosas Albuerne, and H. Dominguez, "Monitoring tool for improving the performance of fuel oil generato sets installed in Cuba," Dyna (Spain), vol. 89, 2014.



Run out is one of the major problems in microdrilling processes, causing unexpectedly short tool life, sudden breakage, and dimensional inaccuracy. In this study, a two-step monitoring system for run out detection is proposed. The first step uses the fast Fourier transform for extracting features from the online measured force signals. In the second step, a neural network-based model predicts the process condition from the previously obtained features. The model was trained and tested by using force signals obtained from tungsten and titanium alloys, which are widely applied in electronic and aerospace industries. A 0.1-mm-diameter microdrill was used in the experimental study, and three different feed rates were applied for each material. The trainedmodel was validated with data that was not used in the training process. In this validation, the system was able to detect more than 70 % of the run out conditions with less than 10 % of false detections. For microdrills, detecting and reducing run out can yield considerablegains in tool life and productivity.

FURTHER DETAILS ON: Int J Adv Manuf Technol, DOI 10.1007/s00170-014-6091-1



There is now an emerging need for an efficient modeling strategy to develop a new generation of monitoring systems. One method of approaching the modeling of complex processes is to obtain a global model. It should be able to capture the basic or general behavior of the system, by means of a linear or quadratic regression, and then superimpose a local model on it that can capture the localized nonlinearities of the system. In this paper, a novel method based on a hybrid incremental modeling approach is designed and applied for tool wear detection in turning processes. It involves a two-step iterative process that combines a global model with a local model to take advantage of their underlying, complementary capacities. Thus, the first step constructs a global model using a least squares regression. A local model using the fuzzy k-nearest-neighbors smoothing algorithm is obtained in the second step. A comparative study then demonstrates that the hybrid incremental model provides better error-based performance indices for detecting tool wear than a transductive neurofuzzy model and an inductive neurofuzzy model.

FURTHER DETAILS ON: F. Penedo, R. E. Haber, A. Gajate, and R. M. Del Toro, "Hybrid incremental modeling based on least squares and fuzzy K-NN for monitoring tool wear in turning processes," IEEE Transactions on Industrial Informatics, vol. 8, pp. 811-818, 2012.



This paper shows a strategy for the optimal tuning of a fuzzy controller in a networked control system using an offline simulated annealing approach. The optimal tuning of the fuzzy controller using a maximum known delay is based on the integral time absolute error (ITAE) performance index. The goal is to obtain the optimal tuning parameters for the input scaling factors where the ITAE performance index is minimized. In this study, a step change in the force reference signal is considered a disturbance, and the goal is to assess how well the system follows set-point changes using the ITAE criterion. In order to improve the efficiency of high-performance drilling processes while preserving tool life, the current study focuses on the design and implementation of an optimal fuzzy-control system for drilling force. Simulation results demonstrate good convergence properties of the proposed strategy. Experimental tests of the drilling of two materials (GGG40 and 17-4 PH) corroborate the excellent transient response and the minimum overshoot predicted by the simulation results. Thus, the optimal fuzzy control system reduces the influence of the increase in cutting force that occurs at larger drill depths, eliminating the risk of rapid drill wear and catastrophic drill breakage.

FURTHER DETAILS ON: R. E. Haber, R. Haber-Haber, A. Jiménez, and R. Galán, "An optimal fuzzy control system in a network environment based on simulated annealing. An application to a drilling process," Applied Soft Computing Journal, vol. 9, pp. 889-895, 2009.



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.

FURTHER DETAILS ON: R. E. Haber, R. M. Del Toro, and A. Gajate, "Optimal fuzzy control system using the cross-entropy method. A case study of a drilling process," Information Sciences, vol. 180, pp. 2777-2792, 2010.



This paper focuses on the design and implementation of a fuzzy-logic-based torque control system, embedded in an open-architecture computer numerical control (CNC), in order to provide an optimization function for the material removal rate. The control system adjusts the feed rate and spindle speed simultaneously as needed, to regulate the cutting torque using the CNC's own resources without requiring additional hardware overheads. The control system consists of two inputs (i.e., torque error and change of error), two outputs (i.e., the feed rate and spindle speed increment) fuzzy controller, and a self-tuning mechanism, all of which are embedded within the kernel of a standard open control. The self-tuning strategy is based on the measured peaks in the torque error signal of the closed-loop system response. The self-tuning fuzzy controller is applied to the milling process in a production environment in order to demonstrate the improvements in performance and effectiveness. Two approaches are tested, and their performance is assessed using several performance measurements. These approaches are the two-input/two-output for the fuzzy controller and a single-output fuzzy controller (i.e., only feed-rate modification), with and without the self-tuning mechanism. The results demonstrate that the proposed control strategy provides better transient performance, accuracy, and machining cycle time than the others, thus, increasing the metal removal rate.

FURTHER DETAILS ON: R. E. Haber and J. R. Alique, "Fuzzy logic-based torque control system for milling process optimization," IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, vol. 37, pp. 941-950, 2007.



This chapter describes the basic concepts involved in advanced CNC systems for new machining processes. It begins with a description of some of the classic ideas about numerical control. Particular attention is paid to problems in state-of-the-art numerical control at the machine level, such as trajectory generation and servo control systems. There is a description of new concepts in advanced CNC systems involving multi-level hierarchical control architectures, which include not only the machine level, but also include a process level and a supervisory level. This is followed by a description of the sensory system for machining processes, which is essential for implementing the concept of the ideal machining unit. The chapter then goes on to offer an introduction to openarchitecture CNC systems. It describes communications in industrial environments and an architecture for networked control and supervision via the Internet. Finally, there is a brief summary of the systems available to assist in programming and the architectures of current CNC systems. There is also a description of the most recent developments in manual programming for current CNC systems and possible architectures for these systems, with the different uses of PCs and their various operating systems.

FURTHER DETAILS ON: J. R. Alique and R. Haber, "Advanced controls for new machining processes," book Machine Tools for High Performance Machining, ed: Springer London, 2009, pp. 159-218.



The unmanned control of the steering wheel is, at present, one of the most important challenges facing researchers in autonomous vehicles within the field of intelligent transportation systems (ITSs). In this paper, we present a two-layer control architecture for automatically moving the steering wheel of a mass-produced vehicle. The first layer is designed to calculate the target position of the steering wheel at any time and is based on fuzzy logic. The second is a classic control layer that moves the steering bar by means of an actuator to achieve the position targeted by the first layer. Real-time kinematic differential global positioning system (RTK-DGPS) equipment is the main sensor input for positioning. It is accurate to about 1 cm and can finely locate the vehicle trajectory. The developed systems are installed on a Citroën Berlingo van, which is used as a testbed vehicle. Once this control architecture has been implemented, installed, and tuned, the resulting steering maneuvering is very similar to human driving, and the trajectory errors from the reference route are reduced to a minimum. The experimental results show that the combination of GPS and artificial-intelligence-based techniques behaves outstandingly. We can also draw other important conclusions regarding the design of a control system derived from human driving experience, providing an alternative mathematical formalism for computation, human reasoning, and integration of qualitative and quantitative information.

FURTHER DETAILS ON: J. E. Naranjo, C. González, R. García, T. De Pedro, and R. E. Haber, "Power-steering control architecture for automatic driving," IEEE Transactions on Intelligent Transportation Systems, vol. 6, pp. 406-415, 2005.



The goal of this paper is to show preliminary results concerning the development of an open software platform supported by portable, low-cost technologies accepted worldwide (i.e., real-time CORBA), aiming at networked monitoring of a complex electromechanical process. A high-speed machining process was selected as a case study. Preliminary results in the networked real-time monitoring of a high-speed machining process were encouraging in spite of constraints dictated by computerized numerical control. The results also corroborated the viability of the platform for monitoring on the basis of current communications and computation technologies using open architectures.

FURTHER DETAILS ON: R. E. Haber, K. Cantillo, and J. E. Jiménez, "Networked sensing for high-speed machining processes based on CORBA," Sensors and Actuators, A: Physical, vol. 119, pp. 418-426, 2005.



This paper shows the viability of implementing a control strategy based on the internal-model control paradigm, which is a useful synergy of a dynamic ANN trained from real-life data and used to predict process output and a fuzzy-logic control (FLC) that enhances the control system's overall performance. A force control problem involving a complex electromechanical system, represented here by the machining process, is considered as a case study. The main goal is to control a single-output variable, cutting force, by changing a single-input variable, feed rate. The proposed neurofuzzy-control (NFC) scheme consists of a dynamic model using ANNs to estimate process output, and a fuzzy-logic controller (FLC) with the same static gain as the inverse model to determine the control inputs (feed rate) necessary to keep the cutting force constant. Four approaches, the fuzzy-logic controller (FLC), the direct inverse controller based on ANNs (DIC-NN), the internal-model controller (IMC-NN) and a neurofuzzy controller (NFC), are simulated and their performances are assessed in terms of several performance measurements. The results demonstrate that the NFC strategy provides better disturbance rejection than the IMC-NN and the FLC for the cases analyzed.

FURTHER DETAILS ON: R. E. Haber, "Special section: "Soft-computing and advanced techniques in new algorithmic approaches to existing application areas"," Future Generation Computer Systems, vol. 21, pp. 1015-1018, 2005.



In this paper a fuzzy-control system has been designed, implemented and embedded in an open CNC. The integration process, design steps and results of applying an embedded fuzzy-control system are shown through the example of real machining operations. The controller uses internal CNC signals (i.e. spindle-motor current) that are gathered and mathematically processed by means of an integrated application. The results show that, at least in rough milling operations, internal CNC signals can double as an intelligent, sensorless control system. Actual industrial tests show a higher machining efficiency (i.e. in-process time is reduced by 10% and total estimated savings the system would provide are about 78%).

FURTHER DETAILS ON: R. E. Haber, J. R. Alique, A. Alique, J. Hernández, and R. Uribe-Etxebarria, "Embedded fuzzy-control system for machining processes: Results of a case study," Computers in Industry, vol. 50, pp. 353-366, 2003.



A fuzzy controller that is suitable for regulating the milling process and ensuring absolute stability with a finite domain (i.e. local asymptotic stability) is presented. The stability analysis is performed on the basis of two versions of the circle criterion: (i) the extended circle criterion reducing the problem to the scalar case; and (ii) the multiple-input multiple-output circle criterion, here stated using a linear matrix inequality in order to profit from the advantages of convex optimisation. In order to verify the robust stability of the fuzzy control system, the plant gain is considered to be uncertain, and the allowed range for this uncertainty is maximised. Simulations based on the linearised plant model demonstrate how the improvement of robust stability affects the dynamics of the control loop. The robust stability improvement turns out to also yield a better fuzzy controller performance. A real-time application proves both stability and dynamic performance in an industrial environment.

FURTHER DETAILS ON: R. E. H. Guerra, G. Schmitt-Braess, R. H. Haber, A. Alique, and J. R. Alique, "Using circle criteria for verifying asymptotic stability in PI-like fuzzy control systems: Application to the milling process," IEE Proceedings: Control Theory and Applications, vol. 150, pp. 619-627, 2003.



In this paper we present a fuzzy adaptation scheme for PD control with gravity compensation of robot manipulators. We demonstrate, by taking into account the full nonlinear and multivariable nature of the robot dynamics, that the overall closed loop system is locally asymptotically stable. Besides the theoretical result, the proposed control scheme has two practical features. First, it compensates for static friction in the robot joints, and second, it considers the real torque actuators capabilities to avoid torque saturation. Experimental results on a two-degree of freedom direct-drive robot show the usefulness of the proposed control approach.

FURTHER DETAILS ON: R. Kelly, R. Haber, R. E. Haber-Guerra, and F. Reyes, "Lyapunov stable control of robot manipulators: A fuzzy self-tuning procedure," Intelligent Automation and Soft Computing, vol. 5, pp. 313-326, 1999.



Monitoring of micro-scale machining processes is a key issue in efficient manufacturing. Monitoring not only reduces the need for expert operators, thereby lowering costs, but it also decreases the probability of unexpected tool breakage, which may involve damage to the workpiece or, even, to the machine-tool. Process monitoring is also of immense importance in view of the tiny tool diameters used in micro-mechanical machining. In this study, a microdrilling process was experimentally studied, which involved three different TiAlN-coated drills (diameters 0.1 mm; 0.5 mm and 1.0 mm), of a tungsten-copper alloy. Variations in tool dimensions were measured after the completion of each hole, while force and vibration signals were measured throughout the cutting process. Features were extracted from the signals by using time-domain statistics, fast Fourier transform, wavelet transform, and Hilbert-Huang transform. These features were correlated with the number of drilled holes by using statistical regressions, neural networks and neuro-fuzzy systems. The study shows that the combination of wavelet transform and neural network systems yielded the most suitable prediction of the use of tool. These results are relevant for further studies on the implementation of tool condition monitoring systems for micromechanical machining processes.

FURTHER DETAILS ON: G. Beruvides, R. Quiza, R. Del Toro, and R. E. Haber, "Sensoring systems and signal analysis to monitor tool wear in microdrilling operations on a sintered tungsten-copper composite material," Sensors and Actuators, A: Physical, vol. 199, pp. 165-175, 2013.