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Real-time Design and Implementation of Automatic Landing Algorithm of a Quadrotor under the Ground Effect
, M.Sc. Thesis Sharif University of Technology ; Nobahari, Hadi (Supervisor)
Abstract
In this thesis an algorithm has been implemented for automatic landing of a quadrotor under the ground effect. In this regard the six degrees of freedom equations of motion using the Newton-Euler method has been designed. Then, the ground effect has been modeled by inspiring from the similar available models in the literature. The proposed models and proportional-integral-derivative attitude control loops have been simulated in MATLAB/Simulink environment. Also, two control strategies, a classical proportional-integral-derivative controller and a sliding mode controller have been utilized for height control loop.Since sliding mode controller requires all state variables to generate control...
Transition Flight Control of a Tilt Tri-Rotor UAV Using Robust Model Predictive Control
, M.Sc. Thesis Sharif University of Technology ; Nobahari, Hadi (Supervisor) ; Emami, Ali (Co-Supervisor)
Abstract
The purpose of this thesis is to control altitude and attitude of a Tilt Tri-Rotor UAV in rotorcraft mode and control of altitude, attitude and forward velocity in transition mode (from rotorcraft mode to cruise) by using model predictive control. First, the detailed mathematical model is obtained by the Newton-Euler equations of motion, in both rotorcraft and transition modes. In the next step, a MIMO model predictive controller has been designed, considering input constraints. Then, since, robustness of the model-based control approach is an important challenge, the performance of the controller is presented in the presence of model uncertainties and wind disturbances. The results indicate...
Advances in heuristic signal processing and applications
, Book ; Chatterjee, Amitava ; Nobahari, Hadi ; Siarry, Patrick
Springer
2013
Abstract
There have been significant developments in the design and application of algorithms for both one-dimensional signal processing and multidimensional signal processing, namely image and video processing, with the recent focus changing from a step-by-step procedure of designing the algorithm first and following up with in-depth analysis and performance improvement to instead applying heuristic-based methods to solve signal-processing problems. In this book the contributing authors demonstrate both general-purpose algorithms and those aimed at solving specialized application problems, with a special emphasis on heuristic iterative optimization methods employing modern evolutionary and swarm...
Experimental Test for Active Control of Vibration in a Cantilever Beam Using Piezoelectric Patches Applicable in Aerospace Structures
, M.Sc. Thesis Sharif University of Technology ; Hosseini Kordkheili, Ali (Supervisor) ; Nobahari, Hadi (Co-Advisor)
Abstract
Structural vibrations can be reduced using an opposite force generated from an active controller. Nowadays implementation of smart materials for active controlling of vibration is growing increasingly specially in aircrafts, spacecrafts and other state of art manmade machines. In aerospace structures as the weight is extremely a limiting factor, the lightweight structural control strategies are more demanded. Considering these motivations, in recent decades, engineers have done many researches to implement active structural damping methods in industrials applications. Among all active vibration approaches, using piezoelectric materials is one of the most attractive ways because of their...
A novel heuristic filter based on ant colony optimization for non-linear systems state estimation
, Article Communications in Computer and Information Science, 27 October 2012 through 28 October 2012 ; Volume 316 CCIS , October , 2012 , Pages 20-29 ; 18650929 (ISSN) ; 9783642342882 (ISBN) ; Sharifi, A ; Sharif University of Technology
2012
Abstract
A new heuristic filter, called Continuous Ant Colony Filter, is proposed for non-linear systems state estimation. The new filter formulates the states estimation problem as a stochastic dynamic optimization problem and utilizes a colony of ants to find and track the best estimation. The ants search the state space dynamically in a similar scheme to the optimization algorithm, known as Continuous Ant Colony System. The performance of the new filter is evaluated for a nonlinear benchmark and the results are compared with those of Extended Kalman Filter and Particle Filter, showing improvements in terms of estimation accuracy
Multiple-input describing function technique applied to design a single channel ON-OFF controller for a spinning flight vehicle
, Article Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering ; Volume 226, Issue 6 , 2012 , Pages 631-645 ; 09544100 (ISSN) ; Mohammadkarimi, H ; Sharif University of Technology
2012
Abstract
In this study, the guidance and control problem of a single-channel spinning missile is investigated. The missile utilizes a single ON-OFF actuator to drive a pair of control surfaces (e.g. elevators) and consequently to perform all required lateral maneouvers. An approximated linear response of the so-called non-rotating frame to ON-OFF input, applied to the rotating frame, is derived using the multiple-input describing function technique. It is shown that there is a relationship between the response of the non-rotating frame and that of the equivalent non-rotating body. It is also shown that the two-channel flight controller, designed for the equivalent non-rotating body, can be reduced to...
A nonlinear estimation and control algorithm based on ant colony optimization
, Article 2016 IEEE Congress on Evolutionary Computation, CEC 2016, 24 July 2016 through 29 July 2016 ; 2016 , Pages 5120-5127 ; 9781509006229 (ISBN) ; Nasrollahi, S ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2016
Abstract
A new heuristic controller, called Continuous Ant Colony Controller, is proposed for nonlinear stochastic systems. The new controller formulates the states estimation and model predictive control problems as a single stochastic dynamic optimization problem and utilizes a colony of virtual ants to find and track the best state estimation and the best control signal. For this purpose an augmented state space is defined. An integrated cost function is also defined to evaluate the ants within the state space. This function minimizes simultaneously the state estimation error, tracking error, control effort and control smoothness. Ants search the augmented state space dynamically in a similar...
A new adaptive real-coded memetic algorithm
, Article 2009 International Conference on Artificial Intelligence and Computational Intelligence, AICI 2009 ; Volume 1 , 2009 , Pages 368-372 ; 9780769538167 (ISBN) ; Darabi, D ; Sharif University of Technology
2009
Abstract
A new adaptive real-coded memetic algorithm has been developed for continuous optimization problems. The proposed algorithm utilizes an adaptive variant of Continuous Ant Colony System for local search. Here new adaptive strategies are utilized for online tuning of the number of local search steps and the width of the search interval over each dimension of the search space. A new crossover scheme is also developed and utilized. The new algorithm has been examined with CEC 2005 benchmarks and compared with three other state of the art works in the field. The comparisons have showed relatively better results. © 2009 IEEE
Accuracy analysis of an integrated inertial navigation system in slow maneuvers
, Article Navigation, Journal of the Institute of Navigation ; 2017 ; 00281522 (ISSN) ; Mohammadkarimi, H ; Sharif University of Technology
Wiley-Blackwell
2017
Abstract
In-motion alignment of a strapdown inertial navigation system, during slow maneuvers, is studied. Terrestrial velocity is fed back to the navigation system to estimate and compensate for the navigation errors. Observability of the errors is analyzed since the integrated navigation system is not fully observable. Then, the accuracy bounds of the navigation system in different motion scenarios are obtained analytically. Also, in order to minimize the errors of the navigation system, special maneuvers are designed based on the analytical derivations. The analytical results, obtained using the linearized error model, are verified through nonlinear simulation of different maneuvering and...
MOCSA: a multi-objective crow search algorithm for multi-objective optimization
, Article 2nd Conference on Swarm Intelligence and Evolutionary Computation, CSIEC 2017, 7 March 2017 through 9 March 2017 ; 2017 , Pages 60-65 ; 9781509043293 (ISBN) ; Bighashdel, A ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2017
Abstract
In this paper, an extension of the recently developed Crow Search Algorithm (CSA) to multi-objective optimization problems is presented. The proposed algorithm, called Multi-Objective Crow Search Algorithm (MOCSA), defines the fitness function using a set of determined weight vectors, employing the max-min strategy. In order to improve the efficiency of the search space, the performance space is regionalized using specific control points. A new chasing operator is also employed in order to improve the convergence process. Numerical results show that MOCSA is closely comparable to well-known multi-objective algorithms. © 2017 IEEE
A heuristic predictive LOS guidance law based on trajectory learning, ant colony optimization and tabu search
, Article Proceedings - 6th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2016, 25 November 2016 through 27 November 2016 ; 2017 , Pages 163-168 ; 9781509011780 (ISBN) ; Haeri, A ; Sharif University of Technology
2017
Abstract
A heuristic predictive line-of-sight (LOS) guidance law is introduced to intercept a high-speed maneuvering target. A combination of continuous ant colony system and tabu search optimization algorithms is proposed to generate the optimal predictive commands of LOS guidance law. Prediction is driven by the previous positions of the target to estimate the next positions of it. Thus, the guidance system is continually solving a dynamic optimization problem in order to determine the acceleration commands by minimizing a cost function subject to actuators saturation. This innovation distinguishes the proposed guidance law from the classic LOS guidance, described by a simple relation between the...
Application of model aided inertial navigation for precise altimetry of unmanned aerial vehicles in ground proximity
, Article Aerospace Science and Technology ; Volume 69 , 2017 , Pages 650-658 ; 12709638 (ISSN) ; Mohammadkarimi, H ; Sharif University of Technology
2017
Abstract
In this research, Model Aided Inertial Navigation (MAIN) is used during the automatic landing of an Unmanned Aerial Vehicle (UAV). A new MAIN algorithm is proposed which is fast and accurate enough to be used in this phase. In this algorithm, the six Degree of Freedom (6DoF) flight simulation of the UAV is integrated with the Inertial Navigation System (INS) such that the 6DoF model acts as an aiding system for the INS. In the last parts of the landing phase, when the UAV flies in proximity of the ground surface, the proposed integrated navigation system can estimate the altitude of UAV utilizing the “ground effect” phenomenon. Therefore, the method does not have the drawbacks of active...
A terminal guidance algorithm based on ant colony optimization
, Article Computers and Electrical Engineering ; Volume 77 , 2019 , Pages 128-146 ; 00457906 (ISSN) ; Nasrollahi, S ; Sharif University of Technology
Elsevier Ltd
2019
Abstract
In this paper, terminal engagement of a maneuvering target and a pursuer is investigated. A heuristic nonlinear model predictive guidance algorithm is presented. Nonlinear kinematics of the pursuer and the target is utilized to formulate the guidance problem. Also, the target maneuver is assumed to be unknown. The proposed heuristic guidance algorithm uses an ant-based optimization algorithm to estimate simultaneously the states of the pursuer, the maneuver of the target, and the optimal guidance commands. Performance of the new guidance algorithm against maneuvering and non-maneuvering targets is evaluated using numerical simulations. Also, the results of the guidance algorithm are compared...
A non-linear estimation and model predictive control algorithm based on ant colony optimization
, Article Transactions of the Institute of Measurement and Control ; Volume 41, Issue 4 , 2019 , Pages 1123-1138 ; 01423312 (ISSN) ; Nasrollahi, S ; Sharif University of Technology
SAGE Publications Ltd
2019
Abstract
A new heuristic controller, called the continuous ant colony controller, is proposed for non-linear stochastic Gaussian/non-Gaussian systems. The new controller formulates the state estimation and the model predictive control problems as a single stochastic dynamic optimization problem, and utilizes a colony of virtual ants to find and track the best estimated state and the best control signal. For this purpose, an augmented state space is defined. An integrated cost function is also defined to evaluate the points of the augmented state space, explored by the ants. This function minimizes simultaneously the state estimation error, tracking error, control effort and control smoothness. Ants...
A hybridization of extended Kalman filter and Ant Colony Optimization for state estimation of nonlinear systems
, Article Applied Soft Computing Journal ; Volume 74 , 2019 , Pages 411-423 ; 15684946 (ISSN) ; Sharifi, A ; Sharif University of Technology
Elsevier Ltd
2019
Abstract
In this paper, a new nonlinear heuristic filter based on the hybridization of an extended Kalman filter and an ant colony estimator is proposed to estimate the states of a nonlinear system. In this filter, a group of virtual ants searches the state space stochastically and dynamically to find and track the best state estimation while the position of each ant is updated at the measurement time using the extended Kalman filter. The performance of the proposed filter is compared with well-known heuristic filters using a nonlinear benchmark problem. The statistical results show that this algorithm is able to provide promising and competitive results. Then, the new filter is tested on a nonlinear...
A hybridization of extended Kalman filter and Ant Colony Optimization for state estimation of nonlinear systems
, Article Applied Soft Computing Journal ; Volume 74 , 2019 , Pages 411-423 ; 15684946 (ISSN) ; Sharifi, A ; Sharif University of Technology
Elsevier Ltd
2019
Abstract
In this paper, a new nonlinear heuristic filter based on the hybridization of an extended Kalman filter and an ant colony estimator is proposed to estimate the states of a nonlinear system. In this filter, a group of virtual ants searches the state space stochastically and dynamically to find and track the best state estimation while the position of each ant is updated at the measurement time using the extended Kalman filter. The performance of the proposed filter is compared with well-known heuristic filters using a nonlinear benchmark problem. The statistical results show that this algorithm is able to provide promising and competitive results. Then, the new filter is tested on a nonlinear...
A nonlinear robust model predictive differential game guidance algorithm based on the particle swarm optimization
, Article Journal of the Franklin Institute ; Volume 357, Issue 15 , 2020 , Pages 11042-11071 ; Nasrollahi, S ; Sharif University of Technology
Elsevier Ltd
2020
Abstract
Two-dimensional engagement of a pursuer and a maneuvering target, affected by matched uncertainties, is formulated as a nonlinear differential game. The uncertain guidance problem is converted into a nonlinear model predictive control problem by introducing an appropriate cost function. The objective is to calculate the best guidance commands of the pursuer and the worst possible target maneuvers simultaneously, over a receding horizon. The proposed cost function penalizes the line-of-sight rate, the pursuer acceleration, and the uncertainties. It also rewards the target maneuver. A particle swarm-based dynamic optimization algorithm is developed to solve the nonlinear model predictive...
Multiple model extended continuous ant colony filter applied to real-time wind estimation in a fixed-wing UAV
, Article Engineering Applications of Artificial Intelligence ; Volume 92 , 2020 ; Sharifi, A ; Sharif University of Technology
Elsevier Ltd
2020
Abstract
In this study, a new heuristic multiple model filter, called Multiple Model Extended Continuous Ant Colony Filter, is proposed to solve a nonlinear multiple model state estimation problem. In this filter, a bank of extended continuous ant colony filters are run in parallel to solve the multiple model estimation problem. The probability of each model is continually updated and consequently both the true model and the states of the nonlinear system are updated based on the weighted sum of the filters. The new multiple model filter is tested on an engineering problem. The problem is to estimate simultaneously the states of a fixed-wing unmanned aerial vehicle as well as the wind model, applied...
A fuzzy-plos guidance law for precise trajectory tracking of a UAV in the presence of wind
, Article Journal of Intelligent and Robotic Systems: Theory and Applications ; Volume 105, Issue 1 , 2022 ; 09210296 (ISSN) ; Asghari, J ; Sharif University of Technology
Springer Science and Business Media B.V
2022
Abstract
The combination of pursuit and line of sight guidance laws, called PLOS, is used to steer an unmanned aerial vehicle along a desired path. In the previous studies, the parameters of this guidance law are tuned by trial and error and are constant, during the flight. In this research, it will be shown that the optimal value of these parameters depends on the initial conditions of the problem and the wind conditions. For this reason, a fuzzy system is proposed to generate the instantaneous optimal value of these parameters, in such a way that the flying vehicle converges to the desired path in less time and follows it more accurately, in the presence of wind. For this purpose, a cost function...
Development of an Evolutionary Algorithm Based on Surrogate Models to be used in Multi-disciplinary Design Optimization of a Flying Vehicle
, M.Sc. Thesis Sharif University of Technology ; Nobahari, Hadi (Supervisor)
Abstract
In this research, multi-disciplinary design optimization (MDO) of a flying vehicle has been done based on the flight simulation. A meta-heuristic algorithm called Multi-objective Adaptive Real-coded Memetic Algorithm (MARCOMA) has been used for optimization. Since solving a MDO problem is a time consuming process, a RBF neural network has been used in the optimization algorithm as a surrogate model. The new algorithm, called MARCOMA+NN, has been tested with some standard benchmarks. MDO problem has six disciplines consists structure, aerodynamic, propulsion, guidance, control, and fire control. The MDO problem has 31 design variables and two objective functions. The objective functions are...