Journal of Majlesi Journal of Mechatronic Systems
Volume:1 Issue: 4, Dec 2012

In This paper a fuzzy controller based on Feedback Error Learning (FEL) has been used to control the velocity of an Electro Hydraulic Servo System (EHSS). In this novel approach, a classic PD controller and intelligent fuzzy controller have been combined to achieve suitable performance and stability. All the simulation and result have been performed in matlab.

This paper describes an experimental investigation of the mixing performance in three different geometries: T-micromixer, O-micromixer and a new type of passive microfluidic mixer called the “H-micromixer”. This type of passive micromixer is based on the SAR process, meaning that the two fluids to be mixed are split and recombined to optimize the diffusion process due to the H-shaped sub-channel. The paper also describes a numerical simulation of the pressure drop in three different geometries. The laminar flow regime (0.08

Abstract Original: English

This work deals with a control and speed estimation of a field oriented control of a doubly fed induction motor without mechanical sensor, particularly when the motor operates at low speed. In the first step, a model of the doubly fed induction motor fed by two PWM inverters with separate DC bus link is developed. In a second phase and considering the model of the motor is strongly coupled, we chose a vector control by field oriented and a particular importance was given to the speed regulation. In the final step, a new fuzzy speed estimation of a doubly fed induction motor based on MRAS strategy was presented. Simulation results show clearly the effectiveness and the performances of the suggested algorithm.

Optimization of flywheel geometry to maximize its energy storage/deliver capability per unit mass, further defined as Specific Energy is an important research topic extensively worked. For this purpose, different methods are available which any of them has limitations such as mechanical and thermal stresses calculation for irregular profiles, modeling of Heterogeneous objects, low speed of optimization procedure and etc. In this paper, a new optimization method proposed which uses genetic algorithm and commercial finite element software simultaneously to optimization of flywheels. Mainly, the performance of a flywheel can be attributed to three factors, i. e., material strength, geometry (cross-section) and rotational speed. This study focuses on exploring the effects of flywheel geometry on specific energy. Finally, three numerical examples are presented to illustrate the merits of the method presented in this paper. The results are then compared with those obtained from «ANSYS» optimization toolbox and in [21]. The results show that the model converges to a very efficient solution without any engineer intervention.

This paper presents a control strategy for micro-electro-mechanical systems (MEMS) z-axis gyroscope, based on the coupling of the fuzzy logic control with the so-called sliding mode control (SMC) approach and sliding mode observer. An adaptive model reference state tracking controller which can estimate the angular velocity vector, and the damping and stiffness model coefficients in real-time is proposed. The tracking performance of pure adaptive SMC is affected by high range chattering due to modeling uncertainties and exogenous inputs. In this paper, this problem is suitably circumvented by adopting an observer-based adaptive fuzzy sliding mode control (OAFSMC) approach which results in chattering free tracking and improved estimation accuracy. For this proposed approach, we have used a fuzzy logic control to generate the reaching control signal. Therefore, the simple and intelligent Mamdani-type controller is resulted with high robustness against parametric uncertainties and exogenous disturbances. The stability of the system is guaranteed in the sense of the Lyapunov stability theorem. Numerical simulations using the nonlinear dynamic model of a MEMS z-axis gyroscope with matched and unmatched uncertainties show the effectiveness of the approach in trajectory tracking problems.

The optimal design of human lower limb knee prostheses is fundamental in order to restore the lost functionality and aesthetic aspect of the amputee’s locomotion. The four-bar linkage is the most widespread mechanism, since, despite its simplicity, it allows the prosthesis to be sufficiently stable and, to replicate the natural motion of the joint with a sufficient accuracy. This work presents an optimization procedure for the synthesis of a six-bar linkage for knee prosthesis and its comparison with four-bar linkage counterpart. Different form of six bar linkages, such as Stephenson and Watt mechanisms are considered and optimized.

Today photovoltaic energy attracts the researchers as a clean and renewable source. Photovoltaic systems have an obvious objection. The efficiency of a photovoltaic panel is low and requires maximum power point tracking (MPPT) technique to obtain the maximum available power. In this paper, the simulation result of the P&O algorithm and the conventional maximum power point tracking technique is compared with the modern control using the fuzzy logic controller. In fact, the target of this manuscript studied the effect of the environmental conditions like variation of solar intensity and temperature on output power of photovoltaic module and the manner of controllers’ response.