In this paper, the trajectory tracking problem for a wheeled mobile robot in the presence of kinematic and dynamic uncertainties has been addressed. Uncertainties are modeled as lumped disturbances. A kinematic controller based on feedback linearization approach and a dynamic controller based on model reference adaptive control are designed in the presence of disturbances. In order to ensure both robustness and implementability of the controllers, the disturbances are estimated by a generalized linear matrix inequality-based disturbance observer. Simulation results show the effectiveness of the proposed method.

No abstract

No abstract

No abstract

Alarm systems are essential in safe operation of industrial plants. Since many process variables are interacting with each other, so in this paper, an approximate method is introduced to design and analysis of a multivariate alarm system. In this method, the alarm system is designed base on joint indices. The Joint FAR and Joint MAR are defined for a m-variable alarm system thanks to multivariate Markov scheme. In proposed method, the alarm joint indices are defined by solving a Linear Programing (LP) optimization problem. By defining joint indices, tuning of the alarm parameters (like, threshold and etc.) can be done by these indices instead of correlation analysis. In this paper, penalty scenario and Genetic algorithm are used for alarm generation, and parameter optimization in Tennessee Eastman (TE) Process. The results of proposed method are compared with other methods.