This paper presents a numerical algorithm for determining the minimum dwell time constraint for switched linear ℋ∞ fault detection filters. When applying switched systems, ensuring the stability is a crucial target, which can be guaranteed, when we switch slowly enough between the subsystems, more precisely when the intervals between two consecutive switching instants, called dwell time, are large enough. The problem formulation is based on multiple Lyapunov functions and is expressed through a special form of linear matrix inequalities (LMIs), which include a nonlinear term of the dwell time. This represents a multivariable, time dependent optimization problem. As a result, the task cannot be treated as a simple feasibility problem involving a LMI solver as it is widely used in applications of the linear control. To solve these special LMIs, we propose a numerical algorithm, called 𝒯d-iteration, which combines the procedure of interval halving with an LMI solver. The algorithm implemented in MATLAB shows its applicability as well as suggest further benefits for the switched linear control and filter synthesis.
Ahmed Ramdane, Abdelaziz Lakehal, Ridha Kelaiaia and Salah Saad
The approach adopted in this paper focuses on the faults prediction in asynchronous machines. The main goal is to explore interesting information regarding the diagnosis and prediction of electrical machines failures by the use of a Bayesian graphical model. The Bayesian forecasting model developed in this paper provides a posteriori probability for faults in each hierarchical level related to the breakdowns process. It has the advantage that it can give needed information’s for maintenance planning. A real industrial case study is presented in which the maintenance staff expertise has been used to identify the structure of the Bayesian network and completed by the parameters definition of the Bayesian network using historical file data of an induction motor. The robustness of the proposed methodology has also been tested. The results showed that the Bayesian network can be used for safety, reliability and planning applications.
These days a lot can be heard about special weapons which accelerate the projectile not based on the traditional, chemical energy release, but providing the muzzle velocity with the help of electromagnets. In English terminology, many descriptions can be read about these devices, referred to as “coilgun”. There are so many hobbyist and amateurs who make these devices [1,2] and publish their results on the internet [3,4]. The purpose of the project is dual. On one hand, features, advantages, disadvantages and the limits of the electromagnetically accelerated weapons can be found by building an experimental tool. On the other hand, it was intended to point out the fact that anybody can build such a tool using commercially available commercial components. Although the muzzle energy of the device presented in this paper is not more than 6.8J, but it can cause serious injury. The paper also points out that in a similar way, still not using special components, a weapon can be made with a larger (10-20J) muzzle energy.
In gas turbine process, the axial compressor is subjected to aerodynamic instabilities because of rotating stall and surge associated with bifurcation nonlinear behaviour. This paper presents a Genetic Algorithm and Particle Swarm Optimization (GA/PSO) of robust sliding mode controller in order to deal with this transaction between compressor characteristics, uncertainties and bifurcation behaviour. Firstly, robust theory based equivalent sliding mode control is developed via linear matrix inequality approach to achieve a robust sliding surface, then the GA/PSO optimization is introduced to find the optimal switching controller parameters with the aim of driving the variable speed axial compressor (VSAC) to the optimal operating point with minimum control effort. Since the impossibility of finding the model uncertainties and system characteristics, the adaptive design widely considered to be the most used strategy to deal with these problems. Simulation tests were conducted to confirm the effectiveness of the proposed controllers.
Samir Bouzoualegh, El-Hadi Guechi and Ridha Kelaiaia
This paper presents a model predictive control (MPC) for a differential-drive mobile robot (DDMR) based on the dynamic model. The robot’s mathematical model is nonlinear, which is why an input–output linearization technique is used, and, based on the obtained linear model, an MPC was developed. The predictive control law gains were acquired by minimizing a quadratic criterion. In addition, to enable better tuning of the obtained predictive controller gains, torques and settling time graphs were used. To show the efficiency of the proposed approach, some simulation results are provided.
In this paper, we introduce a three-dimensional lattice-based computational model in which every lattice point can be occupied by an agent of various types (e.g. cancer cell, blood vessel cell or extracellular matrix). The behavior of agents can be associated to different chemical compounds that obey mass-transfer laws such as diffusion and decay in the surrounding environment. Furthermore, agents are also able to produce and consume chemical compounds. After a detailed description, the capabilities of the model are demonstrated by presenting and discussing a simulation of a biological experiment available in the literature.
Gear hobs are the most widely and frequently used gear cutting tools. During the time passed between the moment of invention (Schiele, 1876) and the present, gear hobs reached a considerable evolution regarding the geometry, the profile of the edge, the relieving technologies finalizing in the latest constructive and design solutions. This paper deals with the calculus of the edge profile in the case the basic worm of the hob has involute helicoid surfaces. In order to obtain a constant grinding allowance on the relief faces of the gear hob teeth it is necessary to compute the edge of the roughing relieving cutter. The equations are deduced considering that the provenience involute worm is a one teethed helical gear with shifted profile. The presented mathematical model proves that linearizing the relieving cutter profile is not an adequate solution if aspiring to higher precision.
The goal of Radio Resource Management (RRM) mechanisms is to allocate the transmission resources to the users such that the transmission requests are satisfied while several constraints are fulfilled. These constraints refer to low complexity and power consumption and high spectral efficiency and can be met by multidimensional optimization. This paper proposes a Game Theory (GT) based suboptimal solution to this multidimensional optimization problem. The results obtained by computer simulations show that the proposed RRM algorithm brings significant improvement in what concerns the average delay and the throughput, compared to other RRM algorithms, at the expense of somewhat increased complexity.
In this work, an explicit Model Predictive Control algorithm is devised and compared to classical control algorithms applied to a series resonant DC/DC converter circuit. In the first part, a model of the converter as a hybrid system is created and studied. In the second part, the predictive algorithm is applied and tested on the model. Finally, the designed control algorithm is compared to classical PI and sliding mode controllers.