J. Humberto Pérez-Cruz

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In this paper, the modelling problem of nonlinear systems with dead-zone input is considered. To solve this problem, an evolving intelligent system is proposed. The uniform stability of the modelling error for the aforementioned system is guaranteed by means of a Lyapunov-like analysis. The effectiveness of the proposed technique is verified by simulations.(More)
A novel control scheme for power regulation in a research nuclear reactor has been developed. The scheme combines different techniques such as state variable feedback, first order numerical integration for estimation, and fuzzy logic to attain a stable power regulation in the reactor. The proposed control strategy attains, in short time and without(More)
Complete modeling of a nuclear reactor is a difficult task because dynamic behavior of this system depends on many factors. So, a complete description of the reactor dynamics implies necessarily the employment of high order nonlinear models. To overcome this problem, in this paper, we propose to use a low order differential neural network for the(More)
In this paper, an asymptotically stable optimal control is proposed for the trajectory tracking of a cylindrical robotic arm. The proposed controller uses the linear quadratic regulator method and its Riccati equation is considered as an adaptive function. The tracking error of the proposed controller is guaranteed to be asymptotically stable. A simulation(More)
In this paper, the structural, sensor, and actuator mathematical models of a transelevator are presented; after, the mathematical model with sensor and actuator of the transelevator is obtained by using the combination of the above mentioned mathematical models. The proposed mathematical model is validated comparing the simulation results against the(More)
ABSTR4CT-The purpose of tliis paper is to prcsellt a solution to the n~ininlization prohlenl of the transient time to accomplish the switching hetween different levels of power in a nuclear research reactor satislying the inverse period cons~rairtt and avoiding to use any physical model of the plant. 'l'be strategy liere proposed consists of two stazes.(More)
A very successful scheme to accomplish trajectory tracking of unknown nonlinear systems consists of identifying the unknown dynamics using differential neural networks and on the basis of the so obtained mathematical model to develop an appropriate control law. The purpose of this paper is to present some new results in this sense. In particular, for the(More)
In this study, the problem of controlling an unknown SISO nonlinear system in Brunovsky canonical form with unknown deadzone input in such a way that the system output follows a specified bounded reference trajectory is considered. Based on universal approximation property of the neural networks, two schemes are proposed to handle this problem. The first(More)
This paper deals with the problem of state observation by means of a continuous-time recurrent neural network for a broad class of MIMO unknown nonlinear systems subject to unknown but bounded disturbances and with an unknown deadzone at each input. With respect to previous works, the main contribution of this study is twofold. On the one hand, the need of(More)
In previous works, a learning law with a dead zone function was developed for multilayer differential neural networks. This scheme requires strictly a priori knowledge of an upper bound for the unmodeled dynamics. In this paper, the learning law is modified in such a way that this condition is relaxed. By this modification, the tuning process is simpler and(More)