Mohammed Tarbouchi

Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Real-Time UAV Path Planning

The development of autonomous unmanned aerial vehicles (UAVs) is of high interest to many governmental and military organizations around the world. An essential aspect of UAV autonomy is the ability for automatic path planning. In this paper, we use the genetic algorithm (GA) and the particle swarm optimization algorithm (PSO) to cope with the complexity of the problem and compute feasible and quasi-optimal trajectories for fixed wing UAVs in a complex 3D environment, while considering the dynamic properties of the vehicle. The characteristics of the optimal path are represented in the form of a multiobjective cost function that we developed. The paths produced are composed of line segments, circular arcs and vertical helices. We reduce the execution time of our solutions by using the “single-program, multiple-data” parallel programming paradigm and we achieve real-time performance on standard commercial off-the-shelf multicore CPUs. After achieving a quasi-linear speedup of 7.3 on 8 cores and an execution time of 10 s for both algorithms, we conclude that by using a parallel implementation on standard multicore CPUs, real-time path planning for UAVs is possible. Moreover, our rigorous comparison of the two algorithms shows, with statistical significance, that the GA produces superior trajectories to the PSO.

Strategies to Accelerate Harmonic Minimization in Multilevel Inverters Using a Parallel Genetic Algorithm on Graphical Processing Unit

Multilevel inverters form a popular class of high-power inverters due to their high-voltage operation, high efficiency, low switching losses, and low electromagnetic interference. Metaheuristics, such as the genetic algorithm (GA), have been used with success to compute optimal switching angles for multilevel inverters with many dc sources while minimizing several harmonics. However, these methods are computationally demanding and cannot easily be used for real-time control. In this letter, a parallel implementation of the GA on graphical processing unit (GPU) is proposed in order to accelerate the computation of the optimal switching angles for multilevel inverters with varying dc sources. Four approaches to parallelize and speed up the computation of the total harmonic distortion are presented and compared. By exploiting the massively parallel architecture of GPUs, the computation of optimal angles is accelerated by a factor of 469× compared to a sequential execution on CPU. The proposed solution optimizes multilevel inverters with 100 variable dc sources while minimizing the first 100 harmonics in 164 ms.

Merits and limitations of using fuzzy inference system for temporal integration of INS/GPS in vehicular navigation

Most of the present vehicular navigation systems rely on global positioning system (GPS) combined with inertial navigation system (INS) for reliable determination of the vehicle position and heading. Integrating both systems provide several advantages and eliminate their individual shortcomings. Kalman filter (KF) has been widely used to fuse data from both systems. However, KF-based integration techniques suffer from several limitations related to its immunity to noise, observability and the necessity of accurate stochastic models of sensor random errors. This article investigates the potential use of adaptive neuro-fuzzy inference system (ANFIS) for temporal integration of INS/GPS in vehicular navigation. An ANFIS-based module named “P–δP” is designed, developed, implemented and tested for fusing INS and GPS position information. The fusion process aims at providing continuous correction of INS position to prevent its long-term growth using GPS position updates. In addition, it provides reliable prediction of the vehicle position during GPS outages. The P–δP module was examined using real navigation system data compromising an Ashtech Z12 GPS receiver and a Honeywell LRF-III INS. The proposed module proved to be successful as a modeless and platform independent module that does not require a priori knowledge of the navigation equipment utilized. Limitations of the ANFIS module are also discussed.

Enhanced mobile robot outdoor localization using INS/GPS integration

An unprecedented surge of developments in mobile robot outdoor navigation was witnessed after the US government removed selective availability of the global positioning system (GPS). However, in certain situations GPS becomes unreliable or unavailable due to obstructions such as buildings and trees. During GPS outages, a positioning solution with a minimum cost is preferred for small wheeled robots. A low-cost inertial measurement unit (IMU) is a good choice to provide such a solution; however, low-cost MEMS-based inertial sensors suffer from several errors that are stochastic in nature. These errors accumulate and cause a rapid deterioration in the quality of position estimate. The purpose of this paper is to describe an enhanced low-cost 3-D navigation system using a Kalman filter (KF) that integrates odometry from wheel encoders, low cost MEMS-based inertial sensors, and GPS. The proposed technique uses reduced inertial sensor system (RISS). The RISS used here includes three accelerometers and one gyroscope aligned with the vertical axis of the body frame of the robot. The benefits of eliminating the two other gyroscopes normally used are decreasing the cost further, and improving the performance by having less inertial sensors and thus less contribution of these sensors errors towards positional errors. These two eliminated gyroscopes were used to calculate pitch and roll which are now calculated using the two horizontal accelerometers. The experimental results show that, during GPS outages, this KF with velocity update derived from the forward speed from wheel encoders is a good technique for greatly reducing localization errors. Real localization data from one trajectory is presented. This data is post-processed and some simulated GPS outages are introduced to assess the effectiveness of the proposed technique.

Speed sensorless estimation of AC induction motors using the fast orthogonal search algorithm

This paper presents a method of estimating the speed of an induction motor using a measurement of the stator current. Speed-induced current harmonics are identified in the stator current using the fast orthogonal search (FOS) algorithm. The frequencies of these estimated harmonics are in turn used to estimate the speed of the motor given the number of rotor slots in the motor. Several optimizations of the FOS algorithm are presented to allow for real-time performance on an embedded digital signal processor. Experimental results of speed estimates on a 1/4 horsepower motor are presented to verify this approach.

Trends in naval ship propulsion drive motor technology

Electric drive propulsion system for naval ships is a very active and fast-growing research area driven by the rapid growth in power electronics and advancement in machine design. Propulsion motors and associated drive control systems form the heart of modern all-electric ships (AES). This paper presents the technology trends in propulsion drive motors for AES propulsion systems. The induction motor (IM), permanent magnet synchronous motor (PMSM), the high temperature superconducting synchronous motor (HTSSM) and the superconducting homopolar DC motor (SHDCM) are examined. These machines are preferred over others mainly because of their higher power densities and efficiencies, allowing a more compact and efficient propulsion system design.

Parallel Power Flow on Graphics Processing Units for Concurrent Evaluation of Many Networks

The power flow (PF) analysis provides the steady state of the power system and is key to the simulation of transmission networks. It is a tool commonly used by system operators to visualize the effect of generator settings on the network prior to making a change. In situations involving large networks, hundreds or even thousands of PF analysis may have to be run on the network before finding the optimal power dispatch. This process requires significant computation time and does not allow for rapid control of the network. To address this problem, this paper presents two parallel PF solvers that exploit the massively parallel architecture of graphics processing units (GPU) in a hybrid GPU-central processing unit (CPU) computing environment using compute unified device architecture and OpenMP in order to significantly speedup the concurrent analysis of many instances of a network. Both implementations use sparse matrices, double precision operations, and enforce the reactive power limit of generators. The parallel Gauss-Seidel (G-S) and Newton-Raphson (N-R) PF algorithms are tested on networks ranging from 4 to 2383 buses. The accuracy is validated using MATPOWER and the maximum speedup achieved, compared with a sequential execution on CPU, is

An optimum speed MPPT controller for variable speed PMSG wind energy conversion systems

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COMPARISON OF PARALLEL PARTICLE SWARM OPTIMIZERS FOR GRAPHICAL PROCESSING UNITS AND MULTICORE PROCESSORS

for G-S and

A method of tracking maximum power points in variable speed wind energy conversion systems

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