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This document describes the implementation of a conical tank control system using an adaptive neurofuzzy system. For implementation, an indirect approach is used where the controller is optimized using the model obtained during the plant identification carried out using data obtained during the system operation. Furthermore, implementation includes training of neuro fuzzy-systems and application to control a conical tank. Regarding plant identification, preliminary training takes place using data obtained for different input values. The controller configuration is established considering the analogy with a discrete-time linear system. The simulation shows that the control system manages to approach the desired response given by the considered reference model.

Systems of distributed generation have shown to be a remarkable alternative to a rational use of energy. Nevertheless, the proper functioning of them still manifests a range of challenges, including both the adequate energy dispatch depending on the variability of consumption and the interaction between generators. This paper describes the implementation of an adaptive neurofuzzy system for voltage control, regarding the changes observed in the consumption within the distribution system. The proposed design employs two neurofuzzy systems, one for the plant dynamics identification and the other for control purposes. This focus optimizes the controller using the model achieved through the identification of the plant, whose changes are produced by charge variation; consequently, this process is adaptively performed. The results show the performance of the adaptive neurofuzzy system via statistical analysis.

Huge losses and serious threats to ecosystems are common consequences of forest fires. This work describes a forest fire controller based on fuzzy logic and decision-making methods aiming at enhancing forest fire prevention, detection, and fighting systems. In the proposal, the environmental monitoring of several dynamic risk factors is performed with wireless sensor networks and analysed with the proposed fuzzy-based controller. With respect to this, meteorological variables, polluting gases and the oxygen level are measured in real time to estimate the existence of forest fire risks in the short-term and to detect the recent occurrence of fire outbreaks over different forest areas. Besides, the Analytic Hierarchy Process method is used to determine the level of fire spread, and, when necessary, environmental alerts are sent by a Web service and received by a mobile application. For this purpose, integrity, confidentiality, and authenticity of environmental information and alerts are protected with implementations of Lamport’s authentication scheme, Diffie-Lamport signature, and AES-CBC block cipher.

Nonlinear systems control is a main issue in control theory. Many developed applications suffer from a mathematical foundation not as general as the theory of linear systems. This paper proposes a control strategy of nonlinear systems with unknown dynamics by means of a set of local linear models obtained by a supervised neural gas network. The proposed approach takes advantage of the neural gas feature by which the algorithm yields a very robust clustering procedure. The direct model of the plant constitutes a piece-wise linear approximation of the nonlinear system and each neuron represents a local linear model for which a linear controller is designed. The neural gas model works as an observer and a controller at the same time. A state feedback control is implemented by estimation of the state variables based on the local transfer function that was provided by the local linear model. The gradient vectors obtained by the supervised neural gas algorithm provide a robust procedure for feedforward nonlinear control, that is, supposing the inexistence of disturbances.