Rodolfo E. Haber

Power-steering control architecture for automatic driving

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 Citroe/spl uml/n 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.

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.

An optimal fuzzy control system in a network environment based on simulated annealing. An application to a drilling process

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.

Toward intelligent machining: hierarchical fuzzy control for the end milling process

The difficulties in implementing adaptive and other advanced control schemes in industrial machining processes have encouraged researchers to combine the utilization of one hierarchical level, a fuzzy control algorithm, and robust sensing systems. The main idea of this paper deals with self-regulating controllers (SRCs). The control signals scaling factor (output scaling factor) is self-regulated during the control process, and it can assure the optimum gain setting for the hierarchical fuzzy controller. An important role in this strategy is performed by a robust sensing system based on current sensors. For comparison, the CNC-PLCs own control loops, a hierarchical fuzzy controller based on look-up tables, and the hierarchical fuzzy controller with a self-regulating output scaling factor GC are studied. The performances of these controllers are compared. The results indicate that the hierarchical fuzzy controller with a self-regulating output scaling factor yields the best performances among them. The index known as the metal removal rate is increased, and the in-process time is reduced by 50%. Thus, higher production rates are obtained. The hierarchical fuzzy controller is equipped with three basic requirements: flexibility, low cost, and compatibility with any CNC manufacturer.

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.

K

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 CNCs 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.

-NN for Monitoring Tool Wear in Turning Processes

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.

Fuzzy Logic-Based Torque Control System for Milling Process Optimization

The objective of process control in a reduction furnace is to optimise nickel recovery, while minimising fuel consumption and environmental contamination. This entails the exact control of temperature and gas composition in the furnace. Controlling the temperature of a multiple hearth furnace is a difficult task. Fast and extensive changes in operating conditions occur, complicated by non-linear and time-varying behaviour of the process and interaction between the different variables. Failing to solve the control problem with a normal PID controller led to development of a knowledge-based fuzzy controller, which keeps the temperature as close to the set profile as possible. Such controller is endowed with a set of 60 rule bases, which are dynamically switched depending on technological constraints and/or operating regions. The algorithm used for the resulting MIMO controller has a Mamdani-type inference system. This paper describes the implementation and the first field tests of a control algorithm, the results of which are very positive and encouraging.

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

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%).