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Rascon R.,Autonomous University of Baja California | Penaloza-Mejia O.,Sonora Institute of Technology | Castro J.G.,Technological Institute of Nogales
European Journal of Control | Year: 2016

In this paper is proposed a control algorithm based on the first order sliding mode technique. The control design adds an exponential reaching law and a disturbance estimator to improve performance, achieving a reduction of the convergence time to the reference, as well as a reduction of the reaching time towards the sliding surface. Also, by compensating the estimated disturbance, it is possible to reduce the amplitude of the chattering in the control signal. As the control design is intended to be applied in mechanical systems, a velocity observer design is also proposed. Bringing together the above aspects, the proposed controller renders an improved performance over the classical first order sliding mode controller. The stability of the closed-loop system is proved using quadratic functions. The performance of the proposed control structure is illustrated and compared with other controllers via numerical simulations and real-time experiments in a mechanical system. © 2016 European Control Association. Source


Javalera V.,Technological Institute of Nogales | Morcego B.,Advanced Control Systems Group SAC | Puig V.,Advanced Control Systems Group SAC
IFAC Proceedings Volumes (IFAC-PapersOnline) | Year: 2010

In the present work, distributed control and artificial intelligence are combined in a control architecture for Large Scale Systems (LSS). The aim of this architecture is to provide a general structure and methodology to perform optimal control in networked distributed environments where multiple dependencies between sub-systems are found. Often these dependencies or connections represent control variables so the distributed control has to be consistent for both subsystems and the optimal value of these variables has to accomplish a common goal. The aim of the research described in this paper is to exploit the attractive features of MPC (meaningful objective functions and constraints) in a distributed implementation combining learning techniques to perform the negotiation of these variables in a cooperative Multi Agent environment and over a Multi Agent platform to provide speed, scalability, and computational effort reduction. This approach is based on negotiation, cooperation and learning. Results of the application of this architecture to a small drinking water network show that the resulting trajectories of the levels in tanks (control variables) can be acceptable compared to the centralized solution. The application to a real network (the Barcelona case) is currently under development. Source


Moreno-Sarmiento M.,University of Sonora | Penalba M.C.,University of Sonora | Belmonte J.,Autonomous University of Barcelona | Rosas I.,National Autonomous University of Mexico | And 4 more authors.
Aerobiologia | Year: 2015

The present investigation was conducted to determine the pollen types and their quantities in the atmosphere of Obregón City (a semiarid region) and establish the relationship with meteorological parameters in 2008 and 2011. A bimodal pattern with peaks in dry warm (spring) and late rainy (autumn) seasons was observed. The highest monthly pollen indexes were observed in October 2008 and September 2011. Precipitation in 2008 was 2.6 times higher than in 2011, beginning in June in both years, and ending in November (2008) and September (2011). Main pollen types were Poaceae, Chenopodiaceae/Amaranthaceae, Asteraceae and Parkinsonia (the latter was dominant in the dry warm season). Statistical correlations (Spearman’s rank-order correlation p < 0.05) with meteorological parameters were performed. In both sampling years, relative humidity caused adverse effects on the atmospheric pollen content, while temperature, solar radiation and wind speed in the dry season were associated with increased pollen indexes. Compared to other studies of semiarid areas, the pollen index at Obregón is low, which is attributed to a relatively high humidity and to the large area of grain crops surrounding the city. © 2015 Springer Science+Business Media Dordrecht Source


Munoz F.,Technological Institute of Nogales | Munoz F.,CINVESTAV | Sanchez E.N.,CINVESTAV | Deng S.,Hong Kong Polytechnic University
Proceedings of the American Control Conference | Year: 2015

This paper presents a discrete-time inverse optimal control scheme for trajectory tracking of a direct expansion (DX) air conditioning (A/C) system. A recurrent high order neural network (RHONN) is used to identify the plant model, and based on this model, a discrete-time inverse optimal control law is derived. The neural network learning is performed on-line by Kalman filtering. The proposed scheme has a structure in which the trajectories can be defined hierarchical by a building energy management system. This novel scheme is tested via simulation. The obtained results for trajectory tracking illustrate the effectiveness of the proposed approach. © 2015 American Automatic Control Council. Source


Technological Institute of Nogales | Entity website

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