Agustín Gajate

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.

Hybrid Incremental Modeling Based on Least Squares and Fuzzy

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.

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Recently, new neuro-fuzzy inference algorithms have been developed to deal with the time-varying behavior and uncertainty of many complex systems. This paper presents the design and application of a novel transductive neuro-fuzzy inference method to control force in a high-performance drilling process. The main goal is to study, analyze, and verify the behavior of a transductive neuro-fuzzy inference system for controlling this complex process, specifically addressing the dynamic modeling, computational efficiency, and viability of the real-time application of this algorithm as well as assessing the topology of the neuro-fuzzy system (e.g., number of clusters, number of rules). A transductive reasoning method is used to create local neuro-fuzzy models for each input/output data set in a case study. The direct and inverse dynamics of a complex process are modeled using this strategy. The synergies among fuzzy, neural, and transductive strategies are then exploited to deal with process complexity and uncertainty through the application of the neuro-fuzzy models within an internal model control (IMC) scheme. A comparative study is made of the adaptive neuro-fuzzy inference system (ANFIS) and the suggested method inspired in a transductive neuro-fuzzy inference strategy. The two neuro-fuzzy strategies are evaluated in a real drilling force control problem. The experimental results demonstrated that the transductive neuro-fuzzy control system provides a good transient response (without overshoot) and better error-based performance indices than the ANFIS-based control system. In particular, the IMC system based on a transductive neuro-fuzzy inference approach reduces the influence of the increase in cutting force that occurs as the drill depth increases, reducing the risk of rapid tool wear and catastrophic tool breakage.

-NN for Monitoring Tool Wear in Turning Processes

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) 1-22 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.

A Transductive Neuro-Fuzzy Controller: Application to a Drilling Process

The control of nonlinear systems has been putting especial attention in the use of Artificial Intelligent techniques, where fuzzy logic presents one of the best alternatives due to the exploit of human knowledge. However, several fuzzy logic real-world applications use manual tuning (human expertise) to adjust control systems. On the other hand, in the Intelligent Transport Systems (ITS) field, the longitudinal control (throttle and brake management) is an important topic because external perturbations can generate uncomfortable accelerations as well as unnecessary fuel consumption. In this work, we utilize a neuro-fuzzy system to use human driving knowledge to tune and adjust the input-output parameters of a fuzzy if-then system. The neuro-fuzzy system considered in this work is ANFIS (Adaptive-Network-based Fuzzy Inference System). Results show several improvements in the control system adjusted by neuro-fuzzy techniques in comparison to the previous manual tuned controller, mainly in comfort and efficient use of actuators.

Artificial cognitive control system based on the shared circuits model of sociocognitive capacities. A first approach

This paper presents the application to the modeling of a novel technique of artificial intelligence. Through a transductive learning process, a neuro-fuzzy inference system enables to create a different model for each input to the system at issue. The model was created from a given number of known data with similar features to data input. The sum of these individual models yields greater accuracy to the general model because it takes into account the particularities of each input. To demonstrate the benefits of this kind of modeling, this system is applied to the tool wear modeling for turning process.

Design and implementation of a neuro-fuzzy system for longitudinal control of autonomous vehicles

Este trabajo muestra el diseno y la implementacion de un sistema neuroborroso para el modelado y control en red de un proceso de taladrado de alto rendimiento. El sistema neuroborroso considerado en este estudio es el conocido como Adaptive Network based Fuzzy Inference System (ANFIS), en el que las reglas borrosas se obtienen a partir de datos entrada/salida. Para el diseno del sistema de control se ha elegido el paradigma del control por modelo interno. Los resultados obtenidos son positivos tanto en la simulacion como en la aplicacion al control en red de la fuerza de corte. Desde el punto de vista tecnico, se aumenta la tasa de arranque de material y al mismo tiempo se garantiza un aprovechamiento efectivo de la vida util de la herramienta de corte. Este buen comportamiento del sistema de control neuroborroso basado en control por modelo interno se ha verificado por medio de varias cifras de merito

Transductive-Weighted Neuro-Fuzzy Inference System for Tool Wear Prediction in a Turning Process

There are many complex processes waiting for artificial cognitive solutions able to deal with new, complex, unknown, or arbitrary tasks efficiently. In this work, the modified shared circuits model (MSCM) for artificial cognitive control is presented. The main goal is to surpass the limitations of the shared circuits models and to formalize an integrated computational solution on the basis of a neuroscientific and psychological approach. Two novelties of the proposed systems are a commutation or switching mechanism between modules in order to reproduce efficiently the imitation, deliberation and mindreading characteristics of human sociocognitive skills. Another contribution is the introduction of a self-optimization strategy based on cross entropy in order to fulfil the control goals. The closed-loop behaviour of the drilling force demonstrates that the MSCM approach is an alternative and feasible option in the field of artificial cognitive control to deal with processes complexity and uncertainty.

Control Neuroborroso en Red: aplicación al Proceso de Taladrado de Alto Rendimiento

This paper presents the application of a neural fuzzy inference method to the field of control systems using the internal model control paradigm (IMC). Through a transductive reasoning system, a neuro-fuzzy inference system enables local models to be created for each input/output set in the system at issue. These local models are created for modeling the direct and inverse dynamics of the process. The models are then applied according to IMC paradigm. In order to demonstrate the benefits of this technique for control systems, it is applied for networked cutting force control in a high-performance drilling process. A comparative study between a well-established neuro-fuzzy technique and the suggested method is performed.

Modified shared circuits model for manufacturing processes control: from psychology and neuroscience to a computational solution

One way to compensate for the influence of drilling depth on useful tool life while enhancing productivity is to introduce real-time control of the cutting force .This paper presents the design and implementation of a neurofuzzy system for modeling and control of a high-performance drilling process in an Ethernet- based application. The neurofuzzy system is an adaptive-network-based fuzzy inference system (ANFIS), where fuzzy rules are obtained from input/output data. The design of the control system is based on the internal model control paradigm. Simulation and experimental results demonstrate the suitability of the proposed approach. An overshoot free transient response and a good settling time are obtained, thereby reducing the risk of rapid drill wear and catastrophic drill breakage.