Sharif Digital Repository

Application of fractional order PID (FOPID) controller to an automatic voltage regulator (AVR) is presented and studied in this paper. An FOPID is a PID whose derivative and integral orders are fractional numbers rather than integers. Design stage of such a controller consists of determining five parameters. This paper employs particle swarm optimization (PSO) algorithm to carry out the aforementioned design procedure. PSO is an advanced search procedure that has proved to have very high efficiency. A novel cost function is defined to facilitate the control strategy over both the time-domain and the frequency-domain specifications. Comparisons are made with a PID controller and it is shown... 

This paper proposes a novel method to designing an H∞ PID controller with robust stability and disturbance attenuation. This method uses particle swarm optimization algorithm to minimize a cost function subject to-norm to design robust performance PID controller. We propose two cost functions to design of a multiple-input, multiple-output (MIMO) and single-input, single-output (SISO) robust performance PID controller. We apply this method to a SISO flexible-link manipulator and a MIMO super maneuverable F18/HARV fighter aircraft system as two challenging examples to illustrate the design procedure and to verify performance of the proposed PID controller design methodology. It is shown with... 

In this paper, a multiproduct inventory control problem is considered in which the periods between two replenishments of the products are assumed independent random variables, and increasing and decreasing functions are assumed to model the dynamic demands of each product. Furthermore, the quantities of the orders are assumed integer-type, space and budget are constraints, the service-level is a chance-constraint, and that the partial back-ordering policy is taken into account for the shortages. The costs of the problem are holding, purchasing, and shortage. We show the model of this problem is an integer nonlinear programming type and to solve it, a harmony search approach is used. At the... 

Disease association study is of great importance among various fields of study in bioinformatics. Computational methods happen to be advantageous specifically when experimental approaches fail to obtain accurate results. Haplotypes are believed to be the most responsible biological data for genetic diseases. In this paper, the problem of reconstructing haplotypes from error-containing SNP fragments is discussed For this purpose, two new methods have been proposed by a combination of k-means clustering and particle swarm optimization algorithm. The methods and their implementation results on real biological and simulation datasets are represented which shows that they outperform the methods... 

Optimizing highway alignment requires a versatile set of cost functions and an efficient search method to achieve the best design. Because of numerous highway design considerations, this issue is classified as a constrained problem. Moreover, because of the infinite number of possible solutions for the problem and the continuous search space, highway alignment optimization is a complex problem. In this study, a customized particle swarm optimization algorithm was used to search for a near-optimal highway alignment, which is a compound of several tangents, consisting of circular (for horizontal design) and parabolic (for vertical alignment) curves. The selected highway alignment should meet... 

Particle Swarm Optimization (PSO) algorithm is a nature-inspired meta-heuristic that has been utilized as a powerful optimization tool in a wide range of applications since its inception in 1995. Due to the flexibility of its parameters and concepts, PSO has appeared in many variants, probably more than any other meta-heuristic algorithm. This paper introduces the Generalized Particle Swarm Optimization (GEPSO) algorithm as a new version of the PSO algorithm for continuous space optimization, which enriches the original PSO by incorporating two new terms into the velocity updating equation. These terms aim to deepen the interrelations of particles and their knowledge sharing, increase... 

New gains tuning and impedance control method were addressed for flexible base moving manipulators. Slow and fast dynamics of robot are decoupled using singular perturbation method. Then, using sliding mode control method, an impedance control law was derived for the slow dynamics. Combined control law was proposed comprising the impedance control law and a feedback control law for the fast dynamics. As fist time, we proposed a new online particle swarm optimization algorithm for gain tuning of impedance control at the contact moments of end effector and unknown environments. This proposed Sliding Mode Impedance Controller and online PSO were simulated for a Flexible Base Moving Manipulator.... 

Vendor-managed inventory (VMI) is a popular policy in supply chain management (SCM) to decrease bullwhip effect. Since the transportation cost plays an important role in VMI and because the demands are often fuzzy, this paper develops a VMI model in a multi-retailer single-vendor SCM under the consignment stock policy. The aim is to find optimal retailers' order quantities so that the total inventory and transportation cost are minimized while several constraints are satisfied. Because of the NP-hardness of the problem, an algorithm based on particle swarm optimization (PSO) is proposed to find a near optimum solution, where the centroid defuzzification method is employed for... 

Particle Swarm Optimization (PSO) algorithms recently invented as intelligent optimizers with several highly desirable attributes. In this paper, two new hybrid Particle Swam Optimization schemes are proposed. The proposed hybrid algorithms are based on using the Particle Swarm Optimization techniques in conjunction with the Simulated Annealing (SA) approach. By simulating three different test functions, it is shown how the proposed hybrid algorithms offer the capability of converging toward the global minimum or maximum points. More importantly, the simulation results indicate that the proposed hybrid particle swarm-based simulated annealing approaches have much superior convergence... 

The method for modeling switching converters plays a key role in real-time simulators. Associate discrete circuit (ADC) modeling technique is a commonly used method for modeling the switching converter. However, the optimal selection of the ADC-based switch model parameters has great importance in the accuracy of the real-time simulator. In this paper, the design of a real-time simulator for a switching power converter has been done, in which a novel method for detecting optimum values of the switch model parameters has been expressed. Particle swarm optimization (PSO) algorithm is used to find these optimum values using state-space analysis of the modeled circuit in the z-domain. The... 

This paper focuses on multi-objective reliability optimization of a two-stage launch vehicle using a hybridized Genetic Algorithm-Particle Swarm Optimization with provisions of relative weighting between the objectives. In this respect, the launch vehicle key subsystems as well as their functions are initially introduced. Subsequently, the system reliability block diagram is constructed using the launch vehicle working order of the subsystems augmented with the requirements for a robust fault/failure tolerant design and performance. Next, based on the proposed reliability block diagram arrangement a bi-objective optimization is formulated to maximize the system reliability while minimizing... 

A Timetabling problem is usually defined as assigning a set of events to a number of rooms and timeslots such that they satisfy a number of constraints. Particle swarm optimization (PSO) is a stochastic, population-based computer problem-solving algorithm; it is a kind of swarm intelligence that is based on social-psychological principles and provides insights into social behavior, as well as contributing to engineering applications. This paper applies the Particle Swarm Optimization algorithm to the classic Timetabling problem. This is inspired by similar attempts belonging to the evolutionary paradigm in which the metaheuristic involved is tweaked to suit the grouping nature of problems... 

This paper presents an effective voltage control strategy for the autonomous operation of a medium voltage (MV) microgrid under nonlinear and unbalanced load conditions. The main objectives of this strategy are to effectively compensate the harmonic and negative-sequence currents of nonlinear and unbalanced loads using distributed generation (DG) units. The proposed control strategy consists of a multi-proportional resonant controller (MPRC) whose parameters are assigned with particle swarm optimization (PSO) algorithm. The optimization function is defined to minimize the tracking error at the specific harmonics considering the stability limitations. In this paper the performance of the... 

The main core of HMM-based speech recognition systems is Viterbi algorithm. Viterbi algorithm uses dynamic programming to find out the best alignment between the input speech and a given speech model. In this paper, dynamic programming is replaced by a search method which is based on particle swarm optimization algorithm. The major idea is focused on generating an initial population of segmentation vectors in the solution search space and improving the location of segments by an updating algorithm. Several methods are introduced and evaluated for the representation of particles and their corresponding movement structures. In addition, two segmentation strategies are explored. The first... 

The main core of HMM-based speech recognition systems is the Viterbi Algorithm. Viterbi is performed using dynamic programming to find out the best alignment between input speech and given speech model. In this paper, dynamic programming is replaced by a search method which is based on particle swarm optimization algorithm. The major idea is focused on generating an initial population of segmentation vectors in the solution search space and improving the location of segments by an updating algorithm. Two methods are introduced for representation of each particle and movement structure. The results show that the effect of these factors is noticeable in finding the global optimum while... 

In this paper, a seasonal multi-product multi-period inventory control problem is modeled in which the inventory costs are obtained under inflation and all-unit discount policy. Furthermore, the products are delivered in boxes of known number of items, and in case of shortage, a fraction of demand is considered backorder and a fraction lost sale. Besides, the total storage space and total available budget are limited. The objective is to find the optimal number of boxes of the products in different periods to minimize the total inventory cost (including ordering, holding, shortage, and purchasing costs). Since the integer nonlinear model of the problem is hard to solve using exact methods, a... 

One of the most important challenges in target parameters estimation via model-free methods is selection of the most effective input parameters namely features selection (FS). Indeed, irrelevant features can degrade the estimation performance. In the current study, the challenge of choosing among the several plant parameters is tackled by means of the innovative FS algorithm named ranking of features with minimum deviation from the target parameter (RFMD). The selected features accompanied with the stable and the fast learning algorithm of multilayer perceptron (MLP) neural network (i.e. Levenberg-Marquardt algorithm) which is a combination of gradient descent and Gauss-newton learning... 

In this paper, a new algorithm for solving constrained nonlinear programming problems is presented. The basis of our proposed algorithm is none other than the necessary and sufficient conditions that one deals within a discrete constrained local optimum in the context of the discrete Lagrange multipliers theory. We adopt a revised particle swarm optimization algorithm and extend it toward solving nonlinear programming problems with continuous decision variables. To measure the merits of our algorithm, we provide numerical experiments for several renowned benchmark problems and compare the outcome against the best results reported in the literature. The empirical assessments demonstrate that... 

Hydrogen is a flexible energy carrier and storage medium and can be generated by electrolysis of water. In this research, hydrogen generation is maximized by optimizing the optimal sizing and operating condition of an electrolyzer directly connected to a PV module. The method presented here is based on Particle swarm optimization algorithm (PSO). The hydrogen, in this study, was produced using a proton exchange membrane (PEM) electrolyzer. The required power was supplied by a photovoltaic module rated at 80 watt. In order to optimize Hydrogen generation, the cell number of the electrolyser and its activity must be 9 and 3, respectively. As a result, it is possible to closely match the... 

This paper proposes a novel method to design an H∞-optimal Fractional Order PLD (FOPLD) controller with ability to control the transient, steady-state response and stability margins characteristics. The method uses particle swarm optimization algorithm and operates based on minimizing a general cost function. Minimization of the cost function is carried out subject to the H∞-norm; this norm is also included in the cost function to achieve its lower value. The method is applied to a phase-locked-loop motor speed system and an electromagnetic suspension system as two examples to illustrate the design procedure and verify performance of the proposed controller. The results show that the...