This paper is dedicated to present the newly developed evolutionary algorithm: Biogeography based optimization (BBO). It is based on the migration of information between habitats like in Biogeography. The BBO is then used to tune a PID controller of nonlinear systems where the parameters are optimized. Simulations of the proposed algorithm are carried out over an inverted pendulum and second on mass-spring damper system. Performances of the BBO are compared to those of genetic algorithm in PID tuning problem and the BBO gives acceptable results even best then GA.

A self-tuning fuzzy inference sliding mode control method is presented for single inverted pendulum position tracking control. Sliding mode control is a special nonlinear control method which has a quick response, is insensitive to parameters’ variation and disturbance; and is very suitable for nonlinear system control. Neuro-fuzzy logic systems are used to directly generate the "equivalent control term". In this case, a neuro-fuzzy system was described as a self-tuning fuzzy inference system optimized online using Takagi-Sygeno type of rules and a back-propagation algorithm to minimize a cost function. The cost function is made up of a quadratic error term and a weight decay term that prevents an excessive growth of parameters. The definition of sliding mode control was presented, and on the basis of the inverted pendulum system the sliding mode controller was designed. Stability of the proposed control scheme is proved by the Lyapunov theorem and the control scheme is applied to an inverted pendulum system. Simulation studies show that the method is effective and can be applied to a nonlinear control system.

This paper presents a design procedure for a reduced-order observer-based controller dedicated to n-joint robot manipulators. It is assumed that only the joint angular positions are measured. The joint angular velocities are estimated via an exponential reduced-order observer. Two types of control laws based on this observer are studied: point-to-point control with gravity compensation and trajectory control. Sufficient conditions to ensure the closed-loop stability are given. Performances of the reduced-order observer used with these two control laws are illustrated in a simulation study of a two-degrees-of-freedom robot manipulator.

This paper presents a design procedure for a reduced-order observer-based controller dedicated to n-joint robot manipulators. It is assumed that only the joint angular positions are measured. The joint angular velocities are estimated via an exponential reduced-order observer. Two types of control laws based on this observer are studied: point-to-point control with gravity compensation and trajectory control. Sufficient conditions to ensure the closed-loop stability are given. Performances of the reduced-order observer used with these two control laws are illustrated in a simulation study of a two-degrees-of-freedom robot manipulator.

Emergency department (ED) is one of the most complex systems, due to its unstable environment and the stochastic characteristics of patient flows, pathologies, and limited resources, which lead to a long waiting time. Effective management of patient flows for 'ED' services has become a critical matter for most hospital supervisors. Many authors have tried to solve those issues based on different methodologies, with shared aims represented in the optimisation of patient care, by minimising patient waiting time at different levels in 'ED'. Modern studies mainly focus on limiting these issues by proposing a solution based on extending 'ED' resources. The case study was realised at the 'ED' of the hospital Chalabi Abdelkader, Mascara, Algeria. Then, the system was modelled by a coloured Petri net framework. Afterward, we performed simulations of numerous improvement scenarios. As a result, patient waiting times encountered in 'ED' were reduced, thus patient length of stay.