J.L. Navarro

Rigas: an expert server task in real-time environments*

Abstract The integration of expert systems in real time environments provides new possibilities to the process control of complex systems. Intelligent tasks working together with traditional control tasks under real time constraints introduce some problems in order to guarantee a response time of the system. In this paper, the use of a a real time expert system for process control, called RIGAS, encapsulated in an expert server task is described. The expert server is formed by an scheduler, a blackboard, and a set of task with real time constraints.

A fuzzy clustering algorithm enhancing local model interpretability

In this work, simple modifications on the cost index of particular local-model fuzzy clustering algorithms are proposed in order to improve the readability of the resulting models. The final goal is simultaneously providing local linear models (reasonably close to the plant’s Jacobian) and clustering in the input space so that desirable characteristics (regarding final model accuracy, and convexity and smoothness of the cluster membership functions) are improved with respect to other proposals in literature. Some examples illustrate the proposed approach.

Fuzzy clustering algorithm for local model control

Abstract Fuzzy modelling has interpretability of the obtained models as a fundamental goal. In this paper a control-oriented local-model fuzzy clustering algorithm will try that local models approximate the linearized plant model on their validity zones. A family of clustering algorithms is presented so that it incorporates some desirable characteristics regarding convexity and smoothness of the final identified clusters, with advantages regarding other methodologies such as Gustaffson-Kessel. The algorithm simultaneously provides local linear models and input clustering, being suitable for Takagi-Sugeno models and local linear models decomposition of complex systems.

Intelligent Industrial Control

Abstract Most of the available industrial adaptive controllers are developed with special features, that is, application oriented, and conceived for stand-alone operation. Modularity, reliability and easy operation ask for distributed intelligent control systems, where local controllers can perform adaptive or direct digital control supervised by a higher level computer controller. In this paper the most common features of advanced industrial controlers are reviewed. A control scheme based on a console-operated computer supervising the operation of a net of local controllers is presented. Typical tasks of these controllers are: monitoring the process and determining the operation mode (i.e., start-up, normal operation, overloading operation, shut-down, etc.), adaption scheme selection, assignment of variables to local controllers and operator interface. A general discussion of the properties of this scheme is followed by its partial implementation based on available controllers.

An interactive course on "Logic controllers design using Grafcet"

Abstract The use of Grafcet as a design methodology for logic controllers is increasing, and PLCs manufacturers are including Grafcet as a programming language at its software. However, learning the basics of Grafcet is not an easy task for those not used to Petri Nets but, provided it is a graphic language, multimedia can provide the appropriate framework for a fast and easy learning environment. In this paper, a web-based introductory course on Grafcet basics is presented, whose interactive simulation capabilities are used in automation examples and exercises, then giving to students a closer vision to real process to be automated.

Local model-based fuzzy control of switch-mode DC/DC converters

Abstract This work proposes a model based fuzzy controller as a control technique for switch-mode dc/dc converters. These systems have a nonlinear dynamic behavior, as they work in switch-mode. In this paper the equations of a buck converter are analyzed and a fuzzy gain scheduler among local models based on several operating points is proposed. A simple state feedback control law is designed for every of the local models and a fuzzy rule, which acts as scheduler. The proposed fuzzy control is evaluated by means of computer simulations of these models for reference output, load regulation and line regulation.

Improvement of a CLE stochastic simulation of gene synthetic network with quorum sensing and feedback in a cell population

We investigate the possibility of performing stochastic simulation of a synthetic gene circuit that includes a cell-to-cell communication system with an intracellular feedback control circuit. We propose an implementation of the CLE stochastic simulation method that makes possible to simulate gene synthetic circuits involving cell-to-cell communication. We find the minimum number of samples ensuring the statistic moments over the population do not degrade. This allows us to reduce the total simulation time and improve the efficiency of the method. Also we reveal that one realization of the population of interconnected cells, provided there is enough time to perform the time average, is useful to obtain representatives of the long-term moments of the population. Finally we show the potential of the improved approach by performing an analysis of the influence of the parameters in the LuxR promoter on the noise strength of the population.

A Closed-loop Exponential Feeding Law for Multi-substrate Fermentation Processes

This article addresses the computation of invariant and stabilizing control laws for dual-substrate fed-batch fermentors. The design is based on two commonly used model structures. It will be shown how to derive partial state feedbacks, using only biomass and volume as measures, that keep the substrates at a desired concentration provided the model is good enough and does not change with time. In the paper an analysis of invariance and a study of global stability within the framework of partial stability is provided.

Diseño de controladores en varios puntos de funcionamiento para una clase de modelos borrosos Takagi-Sugeno afines

When controlling Takagi-Sugeno fuzzy systems, verification of some sector conditions is usually assumed. However, setpoint changes may alter the sector bounds. Alternatively, setpoint changes may be considered as an offset addition in many cases, and hence affine Takagi-Sugeno models may be better suited to this problem. This work discusses a nonconstant change of variable in order to carry out offset-elimination in a class of MIMO canonical affine Takagi-Sugeno models. Once the offset is cancelled, standard fuzzy control design techniques can be applied for arbitrary setpoints. The canonical models studied use as state representation a set of basic variables and their derivatives.

Fuzzy Logic Implementation of Industrial Controllers

Abstract One of the mayor advantages of fuzzy controllers is its friendly interaction between the human and the controller. This property allows that the user of a fuzzy controller understands the controller behavior and he could also add some knowledge from his expertise. This paper describes a common framework that allows to easily change controllers widely used in the industry by a fuzzy control structure. Based on this approach a design methodology is proposed that transforms the industrial controller in a fuzzy control equivalent and subsequent modifications are made based on algebraic or heuristic knowledge. Finally, some examples are described to show the application of this procedure.