Random polyesteramides based on ε‐caprolactone and glycine

A series of random polyesteramides (PEAs) based on ε‐caprolactone and glycine were synthesized by a direct melt polycondensation method. Their structure was fully characterized by NMR spectroscopy. High molar mass PEAs were obtained for glycine contents lower than 15 mol‐%. The resulting copolymers are semi‐crystalline and present increasing glass transition temperatures but decreasing melting points at increasing glycine contents. Some of these PEAs exhibit better thermal stability and higher Young's modulus and ultimate tensile strength than PCL homopolymer. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014 , 131, 40573.     

Outage analysis of blind cooperative diversity

Mobile users with single antennas can still take advantage of spatial diversity through cooperative space–time‐encoded transmission. In this paper, we considered a scheme in which a relay chooses to cooperate only if its source–relay channel is of an acceptable quality, and we evaluate the usefulness of relaying when the source acts blindly and ignores the decision of the relays whether they may cooperate or not. In our study, we consider the regenerative relays in which the decisions to cooperate are based on a targeted end‐to‐end data rate R. We derived the end‐to‐end outage probability for a transmission rate R and a code rate ρ and look at a power allocation strategy between the source and the relays in order to minimize the end‐to‐end outage probability at the destination for high signal‐to‐noise ratio, by using the golden section search method. Performance results show that the computer simulations‐based results coincide with our analytical results. Copyright © 2011 John Wiley & Sons, Ltd.     

Transient response of a rotor-AMBs system connected by a flexible mechanical coupling

In this paper, the effect of angular misalignment on the transient dynamics of a rotor-Active Magnetic Bearings (AMBs) system was examined with numerical analysis. A spatial model of a misaligned rotor mounted in two identical AMBs is setup to account angular misalignment with a flexible coupling having torsional and lateral flexibility. The driving rotor was powered by an electric motor in transient regime. Three models of AMBs were used with four; six and eight magnets. They were powered by bias and control current. The AMBs dynamics support parameters were modeled by stiffness and damping matrices. These were computed through an electromagnetic theory and derivation of the magnetic forces. The general equations of motion of the driven rotor were derived and solved using the Newmark time integration method. Simulation results were carried out to determine the frequency and transient responses in term of misalignment magnitude. Dynamic responses come to confirm a significant influence of the transient regime on the dynamic behavior of misaligned rotor. Simulated results also reveal that vibrations induced by angular misalignment were more important in the case of AMBs with four magnets. Spectral components of the system response were investigated, in sort to present diagnostic information about the signature of this kind of fault in transient regime.     

Efficient metaheuristics for pick and place robotic systems optimization

This paper deals with a pick and place robotic system design problem. The objective is to present an efficient method which is able to optimize the performances of the robotic system. By defining the suitable combination of scheduling rules, our method allows each robot to perform the assigned pick and place operations in real time in order to maximize the throughput rate. For that, we have developed different resolution methods which define the scheduling rule for each robot in order to seize the products from the first side of the system and to place them on the second side. We suggest three metaheuristics which are the ant colony optimization, the particle swarm optimization and the genetic algorithm. Then, we try to select the best algorithm which is able to get the best solutions with the lowest execution times. This is the main advantage of our methods compared to exact methods. This fact represents a great interest taking in consideration that our methods must respect a strong industrial constraint regarding the functioning of a real industrial robotic system. This constraint states that the answer time to manage the seizing strategies of the robots must be less than 1 second. Numerical results show that the different algorithms perform optimally for the tested instances in a reasonable computational time.     

N4SID and MOESP algorithms to highlight the ill-conditioning into subspace identification

In this paper,an analysis for ill conditioning problem in subspace identifcation method is provided.The subspace identifcation technique presents a satisfactory robustness in the parameter estimation of process model which performs control.As a frst step,the main geometric and mathematical tools used in subspace identifcation are briefly presented.In the second step,the problem of analyzing ill-conditioning matrices in the subspace identifcation method is considered.To illustrate this situation,a simulation study of an example is introduced to show the ill-conditioning in subspace identifcation.Algorithms numerical subspace state space system identifcation(N4SID)and multivariable output error state space model identifcation(MOESP)are considered to study,the parameters estimation while using the induction motor model,in simulation(Matlab environment).Finally,we show the inadequacy of the oblique projection and validate the efectiveness of the orthogonal projection approach which is needed in ill-conditioning;a real application dealing with induction motor parameters estimation has been experimented.The obtained results proved that the algorithm based on orthogonal projection MOESP,overcomes the situation of ill-conditioning in the Hankel s block,and thereby improving the estimation of parameters.     

Observer based adaptive neuro-sliding mode control for MIMO nonlinear systems

In this paper, a stable adaptive neural sliding mode controller is developed for a class of multivariable uncertain nonlinear systems. For these systems not all state variables are available for measurements. By designing a state observer, adaptive neural systems, which are used to model unknown functions, can be constructed using the state estimations. Based on Lyapunov stability theorem, the proposed adaptive neural control system can guarantee the stability of the whole closed loop system and obtain good tracking performances. Adaptive laws are proposed to adjust the free parameters of the neural models. Simulation results illustrate the design procedure and demonstrate the tracking performances of the proposed controller.