Showing 21 results for Optimization
Payam Haghighi Tajvar, S M Mehdi Dehghan Banadaki, Mohammad Javad Rajabi,
Volume 0, Issue 0 (8-2023)
Abstract
Abstract: This paper is concerned with the in-motion alignment for low-cost strap-down inertial navigation system (SINS) using an optimization method. The proposed method utilizes GPS position/velocity, along with the outputs from inertial sensors to generate the observation vectors. By introducing a two-sample approximation approach, a recursive velocity/position optimization algorithm is developed by discretizing the integral terms of the observation vectors to estimate the initial attitude matrix. Compared with the traditional velocity integration formulae for constructing the vector observations, the proposed method maintains approximately the same convergence speed while exhibiting more robustness against bias and measurement noise present in the inertial sensors outputs due to the use of position observations. The proposed algorithm provides accurate estimates in applications involving rapid changes in the angular velocities measured by gyroscopes and the forces measured by accelerometers. Simulation results in various scenarios indicate that after 100s, the root mean square of estimation error for the low-cost ADIS16488 INS is less than 0.6°
for yaw angle, less than 0.1°
for pitch angle, and less than 0.3°
for roll angle. This accuracy in the initial Euler angle estimates is sufficient for the coarse in-motion alignment.
Mrs. Reihaneh Kardehi Moghaddam, Dr. Naser Pariz, Dr. Hasan Modir Shanechi, Dr. Ali Vahidian Kamyad,
Volume 4, Issue 2 (9-2010)
Abstract
In this work we use directional enlargement of domain of attraction to increase critical clearing time of nonlinear systems. To find the optimal control parameters, we use measure theory which converts the enlargement problem to a linear programming problem. At first step we find the critical directions of system, along them the system instability happens. After that we find optimal controlling parameters to extend domain of attraction along directions of interest. The efficiency of the proposed method is verified in simulation part for increasing critical clearing time of a power system with four machines.
Mr. Vahid Aeinfar, Dr. Hamidreza Momeni,
Volume 5, Issue 2 (9-2011)
Abstract
An important shortcoming about designing robust control for models generated from prediction error identification is that very few control design methods directly match the ellipsoidal parametric uncertainty regions that are generated by such identification methods. Models obtained from identification experiments in prediction error framework lie in an ellipsoid uncertainty region. In this contribution we present a joint robust control/input design procedure which guaranties stability and prescribed closed-loop performance using models identified from experimental data. Finite dimensional parameterization of the input spectrum is used to represent the input design problem as a convex optimization. A method for fixed-order controller design for systems with ellipsoidal uncertainty is used and given specifications on the closed-loop transfer function are translated into sufficient requirements on the input signal used to identify the system.
Mr. Ali Lari, Dr. Alireza Khosravi,
Volume 5, Issue 2 (9-2011)
Abstract
The µ synthesis problem has not been completely solved, and this is attributed to existing challenges and issues in calculation of the structure singular value. The most common solution for µ Synthesis problem is called D-K Iteration. Even though this specific method is not the complete solution for the µ Synthesis, but the controllers obtained through this method have proven to be one of the most complete forms of robust control technique, based on robust stability and performance. One of the major disadvantages with the D-K Iteration is a high order controller. In this paper an evolutionary algorithm called PSO has been used to design a robust controller. The main objective is to find a solution for µ Synthesis that can better improve the robust stability and performance compared to same order (reduced order) controller obtained through D-K Iteration. To evaluate the proposed algorithm, it has been used on the mass-spring-damper benchmark system. The simulation results from the proposed algorithm show that this method has a more robust stability and performance for closed loop systems than the same order controllers obtained through D-K Iteration.
Mr. Mohammad Taghi Ghorbani, Dr. Hasan Salarieh, Dr. Nima Assadian,
Volume 5, Issue 3 (12-2011)
Abstract
In this paper, the problem of trajectory planning for a high speed planing boat with the aim of time optimization under nonlinear equality and inequality constraints is addressed. First, a nonlinear mathematical model of the craft dynamic and then the Hamiltonian boundary value problem (HBVP) equations are derived. The problem is solved using nolinear programming by discretizing the control time history and adjoining the constraints to the cost function via Linear Extended Penalty Function (LEPF) method. The Steepest Descent (SD) approach is used to solve this nonlinear programming. Some examples of boat minimum time maneuver are presented to demonstrate the effectiveness of the approach for designing optimal maneuvers
Dr. Masoud Aliakbar Golkar, Mr. Ali Ahmadian, Mr. Amin Safari,
Volume 5, Issue 3 (12-2011)
Abstract
Damping of low-frequency electro-mechanical oscillation is very important for the system secure operation. The fast acting, FACTS device which is capable of improving both steady state and dynamic performance permit newer opproaches to system stabilization. In this paper, presents a novel approach for designing of damping controller for STATCOM in order to enhance the damping of power system low frequency oscillations(LFO). Based on Phillips-heffron model linearization, problem of damping controller design considered as an optimizing problem of multi purpose with criterion function and it is solved with utilizing honey bee mating optimization algotithm. To validate the accuracy of results a comparison with GA has been made. This controller is designed in order to transmit unstable electromechanical modes to specific area of complex plane. The proposed controller performance is confirmed by analysis of eigenvalue and nonlinear time-domain simulation under various disturbances with both control parameter of STATCOM( capacitor voltage control and terminal voltage control). Simulation results illustrate that design of controller based of capacitor voltage control in comparison with terminal voltage control has better low frequency oscillation damping and it increases dynamical stability of power system.
Dr. Mahdi Mirzaei, Mr. Hossein Mirzaeinejad, Mr. Siavash Vahidi, Mr. Davood Heidarian, Dr. Mohammad Javad Khosrowjerdi,
Volume 5, Issue 4 (3-2012)
Abstract
Anti lock braking system (ABS) is one of the most important safety devises in vehicles during the severe braking. In this system, by regulating the wheel longitudinal slip at its optimum value, the maximum braking force is generated and therefore the minimum stopping distance for the vehicle is achieved. The hard nonlinearity due to the saturation of tire forces and modeling uncertainties are the main difficulties arising in the design of ABS. Also, the system states including the longitudinal speed and the wheel slip are not directly measurable and have to be estimated. In this paper, a nonlinear optimization based controller is analytically designed for ABS and is combined with a nonlinear estimator based on Unscented Kalman Filter (UKF). This estimation algorithm directly uses nonlinear equations of the system and does not require the linearization and differentiation. Here, the performance of the designed controller in the presence of states estimation and parametric uncertainties is analytically investigated. The simulation results indicate the efficiency of the proposed controller in tracking the optimal longitudinal wheel slip.
Dr. Seyed Ahmad Khalilpour, Dr. Hamid Reza Taghirad, Dr. Mahdi Tale Masouleh, Dr. Mahdi Aliyari Shoorehdeli,
Volume 7, Issue 2 (9-2013)
Abstract
This paper investigates the multi objective optimization of 6-degree of freedom cable-driven parallel robots by using the evolutionary optimization algorithm. In this regard, the determination of cable-driven parallel robots workspace is reviewed as the most important challenge in the design of space cable-driven parallel robots and among various definitions, controllable workspace is selected as a general definition of the cable-driven parallel robots workspace, in which the robot cables remain in tension for any applied forces and wrenches to the end-effector. In order to evaluate the dexterity of the under study robot, the condition number index is used as an effective criterion to measure the distance from singularity. Moreover, the worst kinematic sensitivity is introduced as a presentable accuracy index. Furthermore, by taking the advantages of multi-objective optimization methods such as the non-sorting genetic algorithm, the optimal pareto front for the design parameters of the robot is obtained such that simultaneously, all of the robot design’s objectives are satisfied.
Dr Behnam Dadashzadeh, Mr Heidar Shaban, Dr Mohammad-Reza S. Noorani, Dr Behrooz Koohestani,
Volume 9, Issue 1 (6-2015)
Abstract
In this paper we investigate generating an optimal running gait for the planar model of ATRIAS bipedal robot. ATRIAS is a robotic prototype implemented in Oregon State University with the aim of high speed running. Gait generation for ATRIAS has been done based on SLIP model. Although this passive model is a good base for gait generation, it does not necessarily yield to the best energy efficient solution. So, in this paper via the gradient based method starting from an initial point given by SLIP based control, we search for an optimal pattern for the running gait that minimizes cost of transport (COT) during one complete step. Equations of motion for each continuous time phases, called stance and flight, and models for take-off and touch-down events are derived. Then by parameterization of motors torque profiles using polynomials and solving the direct dynamic model, optimization problem is solved to minimize COT. The optimization is repeated three times by performing the parameterization in terms of polynomials of degrees 3, 4, and 5, to obtain the most efficient torque profiles. The results indicate that for all three shapes of polynomials COT is reduced compared with SLIP based running gait. Moreover, the minimal COT is achieved by torque profiles of degree 4.
Hooman Razavi, Abdorasoul Ghasemi,
Volume 11, Issue 1 (6-2017)
Abstract
In this paper the QoS-aware channel allocation problem formulated as an optimization problem with two conflicting objectives; spectrum utilization and fairness among secondary users (SUs) subject to channel availabilities constraints. Any possible channel allocation which could be a solution of the optimization problem, encoded as a binary chromosome. By having coded available spectrum opportunities instead of all channel-user combinations, the search space is significantly reduced. Designing the QoS-aware channel assignment scheme is based on NSGA-II Algorithm to find the optimum allocation of these two objectives jointly and finally the set of Pareto optimal solutions achieved by proposed algorithm in discrete space of feasible solutions. Simulation results demonstrate the trade-off between spectrum utilization and fairness and the Pareto optimum points. Binary Integer Programming (BIP) confirms the results of the proposed evolutionary scheme in small-scale instances while our scheme outperforms BIP method significantly in computational for large-scale ones.
Hadi Moghadas-Dastjerdi, Mohammad Reza Ahmadzadeh, Mehdi Karami, Farzin Ghiasi, Abbas Samani,
Volume 11, Issue 1 (6-2017)
Abstract
Lung’s air volume estimation is of great importance in lung disease diagnosis. In this paper a fully automatic algorithm, which we presented recently to estimate the lung’s air volume from CT-images, is more developed. In this algorithm, first a suitable cost function is introduced based on the long parenchyma physics to determine the voxels of lung’s air region. In this paper, a fully automatic framework is proposed to calculate the initial guess for the solution of the optimization problem. Moreover, a 3D model reconstruction technique is utilized to determine spatial localization of the lung’s air region in 3D CT-images. Furthermore, the performance of the whole-lung-volume-based methods and direct lung’s air volume measurment methods are compared and investigated. In order to evaluate the accuracy, porcine’s lung images and clinical human’s lung images from reliable databases are fed to the proposed algorithm. The significant accuracy and robust performance of the proposed algorithm is illustrated with respect to the resolution reduction of CT-images.
Behzad Behnam, Payman Hajihosseini, Mohammad Mousavi Anzehaee,
Volume 13, Issue 1 (7-2019)
Abstract
The main goal of this research is to design a new digital controller for a boost-switching converter in Continuous Conduction Mode (CCM), which operates in Voltage Mode Control (VMC). Boost converter transfer function in the mentioned modes have one Right Half Plane (RHP) zero and a pair of complex poles while their places are severely dependent on input voltage, reference voltage and converter elements. Parameters of the proposed controller have been obtained by using a meta-heuristic multi-objective optimization method to gain an improved converter behavior in startup, load change and reference voltage change conditions along with appropriate gain and phase margins. The proposed controller is a combination of a standard typeIII controller and a PID controller with complex zeros. Simulation and experimental results illustrate that converter performance based on the proposed controller has a significant improvement rather than typeIII controller. Main benefits of the proposed controller includes achieving a suitable response in whole range of converter operation, not to need a current feedback, appropriate stability and simple and cost-effective implementation.
Sadegh Kamali, Tooraj Amraee,
Volume 14, Issue 1 (5-2020)
Abstract
in this paper a new algorithm has been proposed to predict unplanned islanding. To predict unplanned islanding, rotor angle and speed time series have been used. Times of fault occurrence and clearance are estimated using monitoring system response based on the variation of phasor measure data. The estimated times of fault occurrence and clearance are used to determine suitable calculation window for uncontrolled islanding prediction. The proposed algorithm is applied to IEEE 39 bus test system. The result shows that the objective function can be used to estimate fault occurring and clearance time to predict unplanned islanding.
Behrooz Yonesie, Ashkan Sebghati, Saeed Shamaghdari,
Volume 14, Issue 1 (5-2020)
Abstract
In this paper, an algorithm is proposed to improve the constrained PID control design based on the convex-concave optimization. The control system is designed by optimizing a performance cost function, taking into account the stability and efficiency constraints with frequency domain analysis in which the sensitivity and complementary sensitivity concepts are used. It is shown, using a counter example, the previous methods are not effective for some systems, the optimization problem becomes unbounded and interrupted. To solve the problem, conditions where the optimization problem fails to have a response are analyzed and the previous limitations are eliminated by representing a new designing method. The performance of the proposed scheme is shown by applying it to the counter example. Moreover, the control system is designed for an unstable system.
Tohid Rahimi, Gholam Ali Alizadeh, Mohsen Hasan Babayi Nozadia,
Volume 14, Issue 4 (1-2021)
Abstract
Micro grids are regarded to be crucial due to the development of communication facilities, production equipment, power storage and high utilization of renewable energy. However, micro grids are faced with the problem of frequency fluctuations in island mode because of high fluctuations in the production power of renewable sources. In this paper, the super capacitor has been used to increase the virtual inertia of the network along with the control system for energy storage and production systems in order to overcome the challenge mentioned earlier. Meanwhile, the optimal value of communication systems delay is also considered in the frequency settings of the micro grid.
The system frequency behavior is simulated against load and power generation variations to evaluate the performance of the proposed strategy. Given the increased cost of the system due to increased super capacitor capacity and the reduction of communication delay against the improvement of micro grid frequency fluctuations, a multi-objective optimization method is used to regulate the load frequency (LFC) controllers parameters and achieve minimum cost. The simulations of the network in question have been carried out in MATLAB / SIMULINK software. In this article, the cost reduction of operation of the microgrid due to the low capacity of the installed supercapacitor unit and communication systems with acceptable delay achievement are the most important innovational aspects of the current research. Furthermore, no battery units are required in the current paper thanks due to the presence of the supercapacitor. Batteries may cause problems for the network due to their low life and high maintenance costs. Simulation results have demonstrated that the system with optimal control parameters values, super capacitor capacity and communication s system delay has been able to overcome load and power generation disturbances, and the system frequency behavior is significantly improved in comparison with the non-optimal state.
Mehrdad Rohani, Hassan Farsi, Seyyed Hamid Zahiri Mamghani,
Volume 14, Issue 4 (1-2021)
Abstract
Nowadays, one of the most fundamental processes for realization video of contents is the object tracking, in which the process of location the moving object is performed in each video frame. In tracking process, the target must be described by a feature. In this paper, for the purpose of describing the target and removing the appearance sensitivity, the weighted color histogram is used as a target feature in order to reduce the effect of edge pixels on the target feature. This reduces the sensitivity of the algorithm to change deformation, scale variation and rotation, as well as the occlusion on the description of target feature. In the proposed method, particle swarm optimization algorithm has been used for search process. Maximization of the similarity function and calculating the minimum Bhattacharyya distance are used to determine target location. The fuzzy control parameters are used for the particle swarm optimization algorithm, which provides a novel method, which can regulate each control parameter and update according to the different states of each particle in each generation. The improved particle swarm algorithm is evaluated with 11 benchmark functions. The obtained results by improved algorithm show that appropriate convergence in a low number of iterations. The proposed method compared to state-of-the-art methods provides high performance in the success and precision rate on the OTB50 dataset.
Alireza Sadeghi Hesar, Seyed Reza Kamel Tabakh, Mahboobeh Houshmand,
Volume 15, Issue 2 (7-2021)
Abstract
Optimal Task Scheduling is one of the most important challenges for achieving high performance in distributed environments such as cloud computing. The primary purpose of task scheduling is to allocate tasks to resources so that some of the system performance metrics will be optimized such as runtime or parallelism. Task scheduling is an NP-complete problem, so heuristic or metha-heuristic algorithms are used to solve it. Because cloud providers offer computing resources based on the pay-as-you-go model, the scheduling algorithm affects the users cost of the cloud. In this paper, a new cloud task scheduling algorithm based on particle swarm optimization as a metha-heuristic method is proposed that assigns users tasks to free resources in cloud computing environments. To enhance the convergence rate of the particle swarm optimization method, the intelligent water drops algorithm is applied. The results of this algorithm on random graphs showed a significant improvement in the performance of the proposed method compared to other task scheduling algorithms.
Amin Ebrahimi Fini, Ali Mohammadi, Abdorreza Kashaninia,
Volume 15, Issue 2 (7-2021)
Abstract
In this paper, generalized model predictive spread control methodis developed to consider the intermediate constraints on system states and system inputs. Because of using the orthogonal basis functions and thus reducing the computational burden, this new method which is named constrained generalized model predictive spread control can be used in online implementation of a finite-time constrained optimal control problem. For demonstrating the performance of the proposed technique, in this paper an interceptor midcourse guidance problem is formulated to reach a desired point in space. Several constraints are considered such as: hard and soft intermediate and terminal constraints on system states and different constraints on input acceleration command to the interceptor in different time intervals of the trajectory. It is shown that in all the above situations, the proposed method could produce the optimal guidance commands such that all the interceptor trajectory constraints are satisfied.
Mohammadreza Zamani Behbahani, Zahra Rahmani, Behrooz Rezaie,
Volume 16, Issue 1 (5-2022)
Abstract
This paper proposes a novel structure of model predictive control algorithm for piecewise affine systems as a particular class of hybrid systems. Due to the time consuming and computational complexity of online optimization problem in MPC algorithm, the explicit form of MPC which is called Explicit MPC (EMPC) is applied in order to control of buck converter. Since the EMPC solves the optimization problem only once and in offline manner, this strategy is suitable for hybrid systems with fast dynamics. As opposed to typical EMPC that is uses only the first element of optimal input vector, the proposed strategy uses all entries of the control sequence with optimal weighting factors. In proposed EMPC, two separate optimization problems are solved at each algorithm step. The first one is related to EMPC optimization problem and the second optimization problem is concerned to finding optimal weighting factors so as to minimize the error signal at each step. The convergence property of the proposed EMPC towards to the desired value has been proved and the simulation results shows the better performance of the proposed EMPC strategy than the typical one, if the weighting factors and control horizons are adjusted properly.
Mr Erfan Nejabat, Dr Mohammad Reza Homaeinezhad,
Volume 16, Issue 3 (12-2022)
Abstract
In the present paper, a class of hybrid, nonlinear and non linearizable dynamic systems is considered. The noted dynamic system is generalized to a multi-agent configuration. The interaction of agents is presented based on graph theory and finally, an interaction tensor defines the multi-agent system in leader-follower consensus in order to design a desirable controller for the noted system. A general undirected, simple and connected graph topology is proposed for the system. Next, a nonlinear controller is designed for the multi-agent system to track a predefined reference trajectory and maintain the formation topology. An optimal controller, based on quasi-Newton optimization method is proposed in order to minimize a nonlinear cost function with indefinite variable sign hessian matrix. The convergence of previous optimization algorithms, namely the Newton optimization algorithm, regarding to variable sign hessian matrices fails. Thus, in the present paper, a quasi-newton optimization method is proposed based on eigenvalue modification to design a controller for the system. Afterward, the controller generalized for the multi-agent system and the performance of the controller is examined in a specific scenario of indefinite, variable hessian matrix problem. Consequently, the innovation of the present paper is proposed by considering the quasi-newton optimization method in order to overcome the disadvantages of traditional optimization methods in the problem of undefined hessian cost function. An example is provided in order to illustrate aforementioned claims and declared propositions.
