Jairo Jose Espinosa

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This work deals with the formulation of a distributed model predictive control scheme as a decision problem in which the decisions of each subsystem affect the decisions of the other subsystems and the performance of the whole system. This decision problem is formulated as a bargaining game. This formulation allows each subsystem to decide whether to(More)
In a Hierarchical Model Predictive Control (H-MPC) framework, this paper explores suitable time-variant structures for the hierarchies of different local MPC controllers. The idea is to adapt to different operational conditions by changing the importance of the local controllers. This is done by defining the level of the hierarchy they belong to, and(More)
The problems associated with vehicular traffic are becoming more noticeable in large cities and highways, mainly because it is becoming less feasible to build new roads or expand the existing ones. This issue is beginning to be treated as a control systems optimization problem, where the objective is to maximize the use of the existing infrastructure.(More)
This paper studies the application of Infinite Horizon Model predictive Control (MPC) and model reduction by means of Hankel norm to chemical process of interest in the field of control of large, complex and networked systems. In this paper we first describes the model of the complete process which is composed by three reactors and three distillation(More)
  • J. Espinosa, B. De Schutterc, F. Valenciad, J. Espinosad
  • 2012
Recently, there has been a renewed interest in the development of distributed model predictive control (MPC) techniques capable of inheriting the properties of centralized predictive controllers, such as constraint satisfaction, optimal control, closed-loop stability, etc. The objective of this paper is to design and implement in a four-tank process several(More)
This paper presents a performance comparison of both the Levenverg-Marquardt and Extended Kalman Filter methods for neural network training. As a testbed, an indoor localization problem was solved by the neural network from the RSSI data obtained through a experimental measurement. Both methods were used to train the network, and the MSE (mean squared(More)
In this paper a methodology that combines a classical PID control strategy and new simulation scenarios is proposed for controlling intersections in the city of Medellín. There are several advantages of PID controllers. The simple computation of the PID control is a remarkable advantage in real time applications, which motivates its application in(More)