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Advances in Space Research (02731177) 75(7)pp. 5656-5668
This study focuses on the boundary control of flexible satellites equipped with honeycomb panels using Lyapunov's direct method. The panels are modeled as Euler–Bernoulli beams, and the govern ing dynamic equations are derived through Hamilton's principle. A novel Lyapunov function candidate is introduced, and asymptotic stability is rigorously established through the extended LaSalle's invariance principle. Control input laws are strategically developed to handle actuator failures while ensuring stability with minimal sensor utilization. Numerical simulations, performed using the assumed mode method, validate the theoretical findings. The results underscore key contributions, including guaranteed asymptotic stability, large-angle maneuvering capabilities, robustness to actuator failures, and the prevention of spillover instability phenomena. © 2025 COSPAR
Journal of Vibration Engineering and Technologies (25233920) 12(Suppl 1)pp. 985-996
Objective: In this paper, the nonlinear flutter of the wing is investigated under the influence of aerodynamic control surfaces. Methods: The wing aerodynamic loads are determined using Peter’s unsteady aerodynamic model, and the aerodynamic loads of the control surface are added with quasi-steady relations in the interior of the equations. The governing aeroelastic equations are presented in the structure of fully intrinsic and these equations are discretized using the finite difference method. Results: The effects of the presence of an aerodynamic control surface have been investigated based on the analytical-experimental relationships and considering the nonlinear effects of high control surface deflections. Furthermore, investigation of the effects of some important parameters such as deflections, location, chord size, and length of the control surface on the speed and frequency of flutter instability, is another achievement of this article. Conclusions: The results show that based on aeroelastic considerations, the deflection angle of the control surface has an important effect on the aeroelastic stability. Also, by bringing the control surface closer to the wing tip, increasing the thickness ratio and the chord ratio in accordance with other effective parameters, flutter suppression can be caused. © Springer Nature Singapore Pte Ltd. 2024.
Applied Soft Computing (15684946) 165
The use of multiple fixed-wing unmanned aerial vehicles in search and rescue missions after natural disasters has become of great interest as they can search large areas and find survivors as quickly as possible. This paper discusses a minimum time cooperative search scheme that utilizes ant colony optimization and new heuristic functions to tackle various constraints in a dynamic environment. The study makes novel use of Dubins curves in the heuristic functions to consider the kinematic limitations of fixed-wing UAVs when planning tangent continuity paths. Furthermore, a novel probabilistic approach is introduced to model the uncertainties induced by dynamic obstacles and determine optimal search paths that are safe and practical in a grid search environment. The performance of the proposed search algorithm is tested through two-dimensional and three-dimensional simulations, statistical analysis, and comparison with other well-known optimization algorithms. To randomize the simulated cooperative search, different search scenarios with static and dynamic obstacles are run several times. © 2024 Elsevier B.V.
Soft Computing (14327643) 28(17-18)pp. 10601-10628
A real-time wind velocity vector and parameters estimation and wind model identification approach using a machine learning technique is addressed in this paper. The proposed method uses only the state measurements of an aircraft and does not require control commands, air data systems, or satellite-based data. Small unmanned aerial vehicles (UAVs) can benefit from this method, since it relies solely on measurement results from the common sensors as an attitude and heading reference system. The independence of external sources of information made estimations resistant to intentional errors. This algorithm uses long short-term memory neural networks (LSTM NNs) in a two-step deep learning process involving classification and regression. A classification NN was trained with four different labeled wind models, while individual regression NNs were trained to estimate the velocity vector and parameters of each wind model. The linear acceleration, angular velocity, and Euler angle measurements were used as the inputs of trained networks. The algorithm suggests in its first step identifying the exact wind model, and in its second step estimating the wind velocity vector and parameters using a properly assigned estimation from a trained network. A nonlinear six-degree-of-freedom simulation of straightforward and level turn maneuvers of a fixed-wing UAV in the presence of different wind models served as the dataset in the learning process. Monte Carlo simulations proved the accuracy and rapidity of the proposed algorithm in identifying the wind model and estimating three-dimensional wind velocity vector and parameters. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
Journal of Navigation (03734633) 76(6)pp. 709-730
This paper proposes a switched model to improve the estimation of Euler angles and decrease the inertial navigation system (INS) error, when the centrifugal acceleration occurs. Depending on the situation, one of the subsystems of the proposed switched model is activated for the estimation procedure. During global positioning system (GPS) outages, an extended Kalman filter (EKF) operates in the prediction mode and corrects the INS information, based on the system error model. Compared with previous works, the main advantages of the proposed switched-based adaptive EKF (SAEKF) method are (i) elimination of INS error, during the centrifugal acceleration, and (ii) high accuracy in estimating the attitude and positioning, particularly during GPS outages. To validate the efficiency of the proposed method in various trajectories, an experimental flight test is performed and discussed, involving a microelectromechanical (MEMS)-based INS. The comparative study shows that the proposed method considerably improves the accuracy in various scenarios. © The Author(s), 2024. Published by Cambridge University Press on behalf of The Royal Institute of Navigation.
Advances in Space Research (02731177) 71(12)pp. 5337-5359
In this paper, the previous work of the authors on Subspace Predictive Control (SPC) in the context of spacecraft roto-translational relative motion is pursued, and the introduced novel SPC-based approach to fault-tolerant control (FTC) of nonlinear time-variant systems is further investigated. Firstly, the effectiveness of the proposed SPC-based framework for adaptive control allocation is exploited. Subsequently, separating nonlinear control and control allocation blocks yields a new layered SPC-based control scheme. Furthermore, designating a dedicated controller for each of the coupled motions leads to a novel distributed SPC-based architecture. Accordingly, three complete model-free fault-tolerant controllers for coupled nonlinear time-variant plants are developed, which solely need the knowledge of the occurrence time of faults as prerequisite. An internal fault diagnosis capability is also introduced, which makes the framework completely self-sufficient. Finally, the proposed framework is verified by challenging simulated scenarios. © 2023
Aut Journal Of Mechanical Engineering (25882937) 7(3)pp. 297-316
Planning the flight path for a fleet of fixed-wing unmanned aerial vehicles during search and rescue operations poses a significant challenge as it requires minimizing search time and optimizing the formation of the unmanned aerial vehicles. This paper proposes a novel integration of a leader-follower formation flight technique for multiple fixed-wing unmanned aerial vehicles with a minimum-time search path planning algorithm. In the first step, the proposed algorithm, based on continuous ant colony optimization, plans a sequence of safe and feasible waypoints for the leader while determining appropriate azimuth angles for the followers. In the next step, the algorithm utilizes a nonlinear three-degree-of-freedom model, developed based on a leader-follower formation flight technique, to plan the followers’ flight paths. Applying Dubins curves based on kinematic constraints of the unmanned aerial vehicles not only reduces computational time but also ensures the feasibility of the best search paths between planned waypoints. Furthermore, in the presence of static obstacles, a developed function in the planning process addresses collision and obstacle avoidance constraints. The effectiveness and performance of the suggested method in detecting targets in minimum-time search missions and the ability of the planner to reconfigure the formation of unmanned aerial vehicles in cluttered environments are demonstrated through comprehensive simulation studies and Monte Carlo analysis. © 2023, Amirkabir University of Technology. All rights reserved.
Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering (09544100) 236(7)pp. 1295-1303
For more than a decade, the multi-state constraint Kalman filter is used for visual-inertial navigation. Its advantages are the light-weight calculations, consistency, and similarity to the current mature GPS/INS Kalman filters. For using it in an airborne platform, an important deficiency exists. It diverges while the object stops moving. In this work, this deficiency is accounted for, by changing the state augmentation and measurement update policy from a time-based to horizontal travel-based scheme, and by reusing the oldest tracked point over and over. Besides the computational savings, it works infinitely with no extra errors in full-stops and with minor error build up in very low speeds. © IMechE 2022.
Journal of Navigation (03734633) 74(4)pp. 801-821
This paper describes a camera simulation framework for validating machine vision algorithms under general airborne camera imperfections. Lens distortion, image delay, rolling shutter, motion blur, interlacing, vignetting, image noise, and light level are modelled. This is the first simulation that considers all temporal distortions jointly, along with static lens distortions in an online manner. Several innovations are proposed including a motion tracking system allowing the camera to follow the flight log with eligible derivatives. A reverse pipeline, relating each pixel in the output image to pixels in the ideal input image, is developed. It is shown that the inverse lens distortion model and the inverse temporal distortion models are decoupled in this way. A short-time pixel displacement model is proposed to solve for temporal distortions (i.e. delay, rolling shutter, motion blur, and interlacing). Evaluation is done by several means including regenerating an airborne dataset, regenerating the camera path on a calibration pattern, and evaluating the ability of the time displacement model to predict other frames. Qualitative evaluations are also made. Copyright © The Royal Institute of Navigation 2021.
Journal of Aerospace Engineering (08931321) 34(6)
The problem of controlling coupled six-degrees-of-freedom (6-DOF) relative motion of an integrated-actuation spacecraft in the presence of actuator faults, and especially failures, is investigated in this paper. Considering the complications and limitations of the common control paradigm (which requires a mathematical model of a plant), a novel data-driven control framework is proposed. Specifically, the subspace predictive control (SPC) approach (which is an elegant data-driven framework based on a special combination of the model predictive control method and the subspace system identification technique) is developed and extended to control nonlinear plants and to tolerate abrupt and severe faults. Accordingly, a model-free fault-tolerant control method for coupled nonlinear time-variant plants is provided, such that its only fault diagnostic requirement is detection of the occurrence time of faults. Furthermore, a fully decentralized multispacecraft strategy is proposed and formulated that is quite suitable for a data-driven cooperative control approach. The effectiveness of the developed framework is demonstrated via simulation. © 2021 American Society of Civil Engineers.
Proceedings of the Institution of Mechanical Engineers Part M: Journal of Engineering for the Maritime Environment (14750902) 233(3)pp. 918-936
The design process of an autonomous underwater vehicle requires mathematical model of subsystems or disciplines such as guidance and control, payload, hydrodynamic, propulsion, structure, trajectory and performance and their interactions. In early phases of design, an autonomous underwater vehicle is often encountered with a high degree of uncertainty in the design variables and parameters of system. These uncertainties present challenges to the design process and have a direct effect on the autonomous underwater vehicle performance. Multidisciplinary design optimization is an approach to find both optimum and feasible design, and robust design is an approach to make the system performance insensitive to variations of design variables and parameters. It is significant to integrate the robust design and the multidisciplinary design optimization for designing complex engineering systems in optimal, feasible and robust senses. In this article, we present an improved multidisciplinary design optimization methodology for conceptual design of an autonomous underwater vehicle in both engineering and tactic aspects under uncertainty. In this methodology, uncertain multidisciplinary feasible is introduced as uncertain multidisciplinary design optimization framework. The results of this research illustrate that the new proposed robust multidisciplinary design optimization framework can carefully set a robust design for an autonomous underwater vehicle with coupled uncertain disciplines. © IMechE 2018.
Ocean Engineering (00298018) 147pp. 517-530
Optimal design of an Autonomous Underwater Vehicle (AUV) consists of various subsystems and disciplines such as guidance and control, payload, hydrodynamics, power and propulsion, sizing, structure, trajectory and performance. The designed vehicle is also employed in an operational environment with tactical parameters such as distance to target, uncertainty in estimation of target position and target velocity. Multidisciplinary Design Optimization (MDO) is the best way for finding both optimum and feasible designs. In this paper, a new optimization design framework is proposed in which Multidisciplinary Feasible (MDF) as MDO framework and Particle Swarm Optimization (PSO) as optimizer were combined together for optimal and feasible conceptual design of an AUV. Initially, we found an optimal system design by using MDF-PSO methodology in engineering space for any single tactical situation (locally tactical parameters). Then the optimal off-design AUVs in tactical subspaces were found by minimizing the difference between the locally optimized objective function and sub-optimal objective function. In this framework, we have shown that not only is the tactical situation affected by AUV design parameters, but an optimal AUV for each tactical regions are also found. © 2017 Elsevier Ltd
Journal of Aerospace Technology and Management (21759146) 9(1)pp. 71-82
The optimum design of a solid propulsion system consists of optimization of various disciplines including structure, aerothermodynamics, heat transfer, and grain geometry. In this paper, an efficient model of every discipline has been developed, and a suitable framework is introduced for these hard-coupled disciplines. Hybrid optimization algorithm is used to find the global optimum point including genetic algorithm and sequential quadratic programing. To show the performance of the proposed algorithm, the required correction factor values have been carefully derived using comparison between more than 10 real solid propulsion systems and the proposed algorithm results. According to the results, the derived correction factors are close to 1, with scattering level better than 0.97. In addition, it is shown that the proposed algorithm (errors < 8%) is more accurate in comparison with the conventional approach (errors < 17%). Then, for a case study, multidisciplinary analysis has been done based on 3 general objectives including dry mass, total mass, and specific impulse. It means that the optimum specific impulse is not the maximum value and the optimum dry mass is not the minimum value. Finally, the proposed algorithm can be used to directly derive the optimum configuration for every mission requirement. © 2017, Journal of Aerospace Technology and Management. All rights reserved.
International Journal Of Technology (20869614) 8(3)pp. 376-386
In this study, a method was developed for tuning moments of inertia for a free-flying dynamically similar/scaled model of an aircraft. For this method, the simulated annealing optimization algorithm was used to obtain similar mass-inertial properties of the model and the full-scale aircraft utilizing ballast weights. For a scaled model of a Su-27 fighter, the ballast arrangement were designed and weights were determined to achieve the required center of gravity position and the moments of inertia based on the similitude requirements. A computer code was developed, and the task of tuning inertia properties was performed. The results showed that the proposed optimization approach was successfully used to determine a feasible ballast weight and position. Moreover, the ballast weight reduced from 8.66 kg to 4.86 kg using the proposed technique, and the inertia characteristics' non-similarity was minimized. © 2017 IJTech.
Journal of Aerospace Engineering (08931321) 30(5)
This research is aimed at developing an efficient online path planner for unmanned air vehicle guidance in completely unknown three-dimensional (3D) rough terrain environments. A novel algorithm is proposed that directly incorporates the vehicle dynamics in the guidance strategy. A suitable point mass dynamic model is also developed. The flight path forms gradually as a result of applying the guidance commands to the vehicle dynamics. A key feature of this approach is real-time assessment of terrain characteristics and using this information in the guidance procedure. The problem is considered within a fuzzy behavior-based framework. The guidance algorithm uses acquired information from the onboard sensors and rapidly issues commands that will guide the vehicle safely to an intermediate position within the sensor range. Two behaviors are introduced: go to target and 3D terrain following/terrain avoidance. The issued commands are then integrated with adjustable weighting factors. Simulation results demonstrate a significant enhancement in vehicle autonomy level. Intelligent decision-making capability afforded by this approach allows for autonomous and safe low-level flight in mountainous areas. © 2017 American Society of Civil Engineers.
Aeronautical Journal (20596464) 121(1244)pp. 1561-1577
This paper attempts to develop an efficient online algorithm for terrain following in completely unknown rough terrain environments while incorporating aircraft dynamics in the guidance strategy. Unlike most existing works, the proposed algorithm does not generate the flight path directly. The algorithm employs acquired information from the vehicle onboard sensors and rapidly issues appropriate Guidance Commands (GCs) at every point along the way. A suitable dynamic model is developed which takes the lags in the vehicle dynamics into account. The flight path forms gradually as a result of applying the GCs to the vehicle dynamics. Terrain-conforming capability afforded by this approach allows for autonomous and safe low-level flight in unknown mountainous areas. It considerably enhances the autonomy level of the vehicle and in the case of manned aircraft could significantly lead to pilot workload reduction. The proposed scheme is proven to be promising for online applications. Copyright © Royal Aeronautical Society 2017.
In this paper the attitude determination of a micro-satellite is handled applying two different state identification methods. The point-by-point or deterministic method solves the so-called Wahba's problem analytically by means of Davenport's q-method and the recursive or stochastic method estimates the attitude mean and covariance of the satellite's states using Extended Kalman Filter (EKF). The comparative results of two methods for the attitude determination of a micro-satellite in Low Earth Orbit (LEO) show the proficiency of the study. © 2017 IEEE.
Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering (09544100) 230(6)pp. 1103-1113
This paper presents continuous curvature paths for unmanned vehicles such as robots and unmanned aerial vehicles. The importance of these paths is that both upper-bounded curvature and upper-bounded curvature derivatives are included in the path. The approach is based on replacement of the Dubins line with the quintic PH Bezier curves by computing a shape parameter by considering the kinematic constraints of the path. Since these paths are Dubins-based paths, their lengths are close to the minimum length. The effectiveness and sub-optimality of the proposed paths are demonstrated through fully nonlinear simulation. © 2015 Institution of Mechanical Engineers.
Sadhana - Academy Proceedings in Engineering Sciences (09737677) 41(1)pp. 87-96
Reduction of costs is a main consideration in every space mission, and propulsion system is an important subsystem of those missions where orbital maneuvers are considered. Lighter propulsions with higher performance are necessary to reduce the mission costs. Bipropellant propulsions have been widely used in launch vehicles and upper-stages as well as deorbit modules because of better performances in comparison with other propulsion systems. Unfortunately heat transfer and thermal control limit bipropellant propulsion performance and maximum performance cannot be achieved. Well-known cooling methods such as regenerative and film cooling increase the cost using extra equipment and high temperature materials. In this paper, a new approach for cooling is presented based on combined ablative and radiative cooling. Governing equations are derived for two or three layers of thermal protection system (TPS) to optimize the TPS mass. The first layer is used as an ablative layer to control the temperature where the second and third layers are used as an insulator to control the heat fluxes. Proposed cooling method has been applied for two real bipropellant thrusters. According to the results, the presented algorithm can suitably predict the heat fluxes and satisfy the wall temperature constraint. Then, the algorithm has been used to minimize the wall temperatures as low as possible and replace high temperature materials (platinum alloy) with common materials (composite or steel). It is shown that selection of TPS materials affects the TPS mass and Isp simultaneously, but conversely. Best solution should be derived by trading off between structure temperature (cost), Isp (performance), and TPS thicknesses (geometry). Multidisciplinary approach to TPS and structure material selection of a bipropellant thruster is presented for a case study. It has been shown that mass and performance penalties of using TPS are acceptable, considering the advantages of using steel alloy instead of platinum alloy. © 2016, Indian Academy of Sciences.
Acta Astronautica (00945765) 114pp. 174-183
Abstract Optimum design of an upper-stage with bipropellant propulsion system consists of optimization of three major subsystems including thruster, feeding subsystem, and propellant tanks. Optimization of such a complex system involved in optimization of many disciplines including structure, heat transfer, aerothermodynamics, guidance and control, trajectory and propulsion. Hard coupling of the disciplines increase the optimization processing times. Multidisciplinary design optimization algorithm can derive the optimum configuration but more elapsed time is needed for single-level methods such as all at once (AAO) and lower feasibility occurred in multi-level methods such as collaborative optimization (CO). In this paper, a new multidisciplinary design optimization framework is proposed for such coupled disciplines with concentrating on the propulsion system. The proposed framework uses Combined Single-level and Bi-level Optimizations (CSBO) frameworks to minimize numbers of design variables and system constraints when feasibility is increased. For this goal, modeling of every discipline is introduced and the design algorithm validated by redesigning of two real bipropellant thrusters. Three MDO frameworks are applied for our problem including AAO, CO and CSBO. Comparisons between the results show that CSBO can find the optimum solution in shorter elapsed time with lower F-count. Therefore, CSBO is more efficient for complex systems with coupled disciplines. © 2015 IAA.
International Journal of Intelligent Unmanned Systems (20496427) 3(2-3)pp. 156-170
Purpose – For complex engineering problems, multidisciplinary design optimization (MDO) techniques use some disciplines that need to be run several times in different modules. In addition, mathematical modeling of a discipline can be improved for each module. The purpose of this paper is to show that multi-modular design optimization (MMO) improves the design performances in comparison with MDO technique for complex systems. Design/methodology/approach – MDO framework and MMO framework are developed to optimum design of a complex system. The nonlinear equality and inequality constrains are considered. The system optimizers included Genetic Algorithm and Sequential Quadratic Programming. Findings – As shown, fewer design variables (optimization variables) are needed at the system level for MMO. Unshared variables are optimized in the related module when shared variables are optimized at the system level. The results of this research show that MMO has lower elapsed times (14 percent) with lower F-count (16 percent). Practical implications – The monopropellant propulsion upper-stage is selected as a case study. In this paper, the efficient model of the monopropellant propulsion system is proposed. According to the results, the proposed model has acceptable accuracy in mass model (error <2 percent), performance estimation (error <6 percent) and geometry estimation (error <10 percent). Originality/value – The monopropellant propulsion system is broken down into the three important modules including propellant tank (tank and propellant), pressurized feeding (tank and gas) and thruster (catalyst, nozzle and catalysts bed) when chemical decomposition, aerothermodynamics, mass and configuration, catalyst and structure have been considered as the disciplines. The both MMO and MDO frameworks are developed for the monopropellant propulsion system. © 2015, © Emerald Group Publishing Limited.
Adami, A. ,
Mortazavi, M. ,
Nosratollahi, M. ,
Taheri, M. ,
Sajadi, J. International Journal Of Aerospace Engineering (16875974) 2015
Monopropellant propulsion systems are widely used especially for low cost attitude control or orbit correction (orbit maintenance). To optimize the total propulsion system, subsystems should be optimized. Chemical decomposition, aerothermodynamics, and structure disciplines demand different optimum condition such as tank pressure, catalyst bed length and diameter, catalyst bed pressure, and nozzle geometry. Subsystem conflicts can be solved by multidisciplinary design optimization (MDO) technique with simultaneous optimization of all subsystems with respect to any criteria and limitations. In this paper, monopropellant propulsion system design algorithm is presented and the results of the proposed algorithm are validated. Then, multidisciplinary design optimization of hydrazine propulsion system is proposed. The goal of optimization can be selected as minimizing the total mass (including propellant), minimizing the propellant mass (maximizing the Isp), or minimizing the dry mass. Minimum total mass, minimum propellant mass, and minimum dry mass are derived using MDO technique. It is shown that minimum total mass, minimum dry mass, and minimum propellant mass take place in different conditions. The optimum parameters include bed-loading, inlet pressure, mass flow, nozzle geometry, catalyst bed length and diameter, propellant tank mass, specific impulse (Isp), and feeding mass which are derived using genetic algorithm (GA). © 2015 Amirhossein Adami et al.
Journal of Aerospace Engineering (08931321) 28(1)
In this paper, a new formation flying approach based on virtual structure is presented. In this architecture, formation control strategies are appropriate when a large number of unmanned aerial vehicles (UAVs) are involved. Control laws for formation control are designed based on both classical theory and inverse dynamics, and then a comparison study is performed. The effectiveness of the proposed control strategies is demonstrated through nonlinear 6-degrees of freedom (DOF) simulation. © 2014 American Society of Civil Engineers.
Mathematical Problems In Engineering (1024123X) 2013
This paper presents a new concept for atmospheric reentry online optimal guidance and control using a method called MARE G&C that exploits the different time scale featured by reentry dynamics. The new technique reaches a quasi-analytical solution and simplified computations, even considering both lift-to-drag ratio and aerodynamic roll as control variables; in addition, the paper offers a solution for the challenging path constraints issue, getting inspiration from the inverse problem methodology. The final resulting algorithm seems suitable for onboard predictive guidance, a new need for future space missions. © 2013 Davood Abbasi and Mahdi Mortazavi.
Journal of Intelligent and Fuzzy Systems (18758967) 24(3)pp. 499-509
The fuzzy sliding mode control based on the multi-objective genetic algorithm is proposed to design the altitude autopilot of a UAV. This case presents an interesting challenge due to non-minimum phase characteristic, nonlinearities and uncertainties of the altitude to elevator relation. The response of this autopilot is investigated through various criteria such as time response characteristics, robustness with respect to parametric uncertainties, and robustness with respect to unmodeled dynamics. The parametric robustness is investigated with reduction in significant longitudinal stability coefficients. Also, a nonlinear model in presence of the coupling terms is used to investigate the robustness with respect to unmodeled dynamics. In spite of a designed classic autopilot, it is shown by simulation that combining of the sliding mode control robustness and the fuzzy logic control independence of system model can guarantee the acceptable robust performance and stability with respect to unmodeled dynamics and parametric uncertainty, while the number of FSMC rules is smaller than that for the conventional fuzzy logic control. © 2013 - IOS Press and the authors. All rights reserved.
Aeronautical Journal (20596464) 117(1194)pp. 839-859
The purpose of this paper is developing an efficient flight control strategy in terms of time response characteristics, robustness with respect to both parametric uncertainties and un-modeled nonlinear terms, number of required measurements, and computational burden. The proposed method is based on combination of a classic controller as principal section of the autopilot and a multi-objective genetic algorithm-based fuzzy output sliding mode control (FOSMC). FOSMC not only modifies robustness of the classic controller against uncertainties and external disturbances, but also modifies its time response for wide range of commands. FOSMC is a single input-single output controller that is based on the system output instead of the system states. In this situation, the proposed autopilot does not require measurement of other variables and observer, and also it is practicable because of considerable reduction in rule inferences then computational burden. As a critical application, the proposed method is applied to design the altitude hold mode autopilot for an UAV which is non-minimum phase, uncertain, and nonlinear.
Applied Mechanics and Materials (discontinued) (16627482) 225pp. 323-328
A new methodology has been proposed to design a dynamically similar/scaled model (DSM) of aircraft. This method uses the simulated annealing (SA) optimization algorithm to get the maximum similarity between model and full-scale aircraft with help of systems movement and using minimum ballast weight. For the 1/2 model of an unmanned aerial vehicle (UAV), internal arrangement is designed to achieve the desired model center of gravity position and moments of inertia. A computer code is developed, and model suitable arrangement is obtained. Results show that the proposed optimization approach to design of DSM was successfully used to find adequate model systems arrangement and minimizing ballast weight to access more capacities for dataacquisition systems or fuels. In this problem, ballast weight reduced about 0.6 kgf for a 55 kgf model, in addition of simplicity of DSM design for various configuration and flight regimes. © (2012) Trans Tech Publications, Switzerland.
Applied Mechanics and Materials (discontinued) (16627482) 110pp. 2504-2512
Environmental simulation has an essential contribution in attitude determination and control verification tests of satellites. Specifically, real-time modeling of space environment can provide more precise and adapted simulation of real world in order to enable satellite attitude determination system by online outputs of sensors. Design and manufacturing of a moving mechanism which simulates the motion of real Sun relative to the satellite is proposed in this paper. Indeed, an artificial Sun carried by the mechanism will sensitize Sun Sensors mounted on a 3DOF model of satellite and finally the outputs of sensors are used to determine the attitude of the model satellite. The procedure of designing and manufacturing such a mechanism is described as follows. Firstly, the motion of Sun relative to the satellite on a specific orbit was ascertained. Next, considering the constraints such as laboratory space and its equipments, an appropriate mechanism was designed conceptually to satisfy the requirements. Then, the detailed characteristics of the mechanism were determined in the preliminary design phase and approved in the detailed design phase of the project. Finally, in order to verify the designed mechanism, a scaled down prototype was fabricated. Developmental tests on the prototype proved the ability of the model to simulate the Sun motion relative to the satellite properly. © (2012) Trans Tech Publications, Switzerland.
Journal of Aerospace Engineering (08931321) 25(1)pp. 1-9
In this paper, the authors tried to design the altitude hold mode autopilot for unmanned aerial vehicles. This case presents an interesting challenge attributable to the nonminimum phase characteristic, nonlinearities, and uncertainties of the altitude to elevator relationship. A fuzzy logic autopilot in a single-loop scheme is proposed for the design of this autopilot. The multiobjective genetic algorithm is used to mechanize the optimal determination of fuzzy logic autopilot parameters on the basis of an efficient cost function that comprises undershoot, overshoot, rise time, settling time, steady state error, and stability. Simulation results show that the proposed strategy not only has a simple structure, but also has desirable performances in time response characteristics, robustness (against the unmodeled nonlinear terms and parametric uncertainties), and the adaptation of itself than the large commands. © 2012 American Society of Civil Engineers.
Applied Soft Computing (15684946) 11(1)pp. 365-372
In this paper, an efficient strategy is proposed to design the altitude hold mode autopilot for a UAV which is non-minimum phase, and its model includes both parametric uncertainties and unmodeled nonlinear dynamics. This work has been motivated by the challenge of developing and implementing an autopilot that is robust with respect to these uncertainties. By combination of classic controller as the principal section of the autopilot and the fuzzy logic controller to increase the robustness in a single loop scheme, it is tried to exploit both methods advantages. The multi-objective genetic algorithm is used to mechanize the optimal determination of fuzzy logic controller parameters based on an efficient cost function that comprises undershoot, overshoot, rise time, settling time, steady state error and stability. Simulation results show that the proposed strategy performances are desirable in terms of the time response characteristics for both phugoid mode and short period mode, the robustness, and the adaptation of itself with respect to the large commands. © 2010 Elsevier B.V. All rights reserved.
The purpose of this research is the optimal design of a reentry capsule configuration to minimize the mission cost which is usually modeled by minimizing reentry module mass (thermal protection system mass, propellant mass and structural mass). Multidisciplinary design optimization (MDO) is an important approach for the conceptual design of reentry capsule, because they are characterized by various disciplines that interact with one another. In this paper Trajectory, Aerodynamics, Structure, Thermal Protection System (TPS) and Deorbit Propulsion disciplines are modeled to optimize bi-conic configuration parameters. All At Once (AAO) frame work is developed and Genetic Algorithm (GA) is used to multidisciplinary conceptual design optimization of reentry mission with nonlinear constrains. © 2011 IEEE.
Kermanshahi, F. ,
Mortazavi, M. ,
Mohagheghi, M. ,
Sajedi, M.S. ,
Ziazi, R.M. ,
Sadati, S. ,
Pourzand, H. ,
Goudarzi, N. IEEE Aerospace Conference Proceedings (1095323X)
In this paper, a full applicable procedure for design, optimization and manufacturing of an operational unmanned helicopter with deep and detailed research basis is presented. 12The proposed process deals with challenging aspects of manufacturing of the mentioned type of aircraft such as cost, weight, operation ability, reliability, mission justification, stability, performance, etc. To show the applicability of the proposed procedure, a realization of the process in production of an operational unmanned helicopter named as Parvan is described in this paper. Indeed, Parvan is a Remotely Piloted Helicopter (RPH) with 9 kilograms takeoff weight, and main rotor diameter of 1.54 meters which can lift up a 2 kilograms payload and fly in 120 kilometer radius of action for about 1.5 hours. ©2010 IEEE.
Aircraft Engineering and Aerospace Technology (17488842) 82(2)pp. 107-115
Purpose - The purpose of this paper is to propose an efficient algorithm for trajectory planning of unmanned aerial vehicles (UAVs) in 2D spaces. This paper has been motivated by the challenge to develop a fast trajectory planning algorithm for autonomous UAVs through mid-course waypoints (WPs). It is assumed that there is no prior knowledge of these WPs, and their configuration is computed as in-flight procedure. Design/methodology/approach - Since the off-line techniques cannot be applied, it is required to apply an online trajectory planning algorithm. For this reason, based on the optimal control and the geometry, each segment of trajectory is designed with respect to a local frame. The algorithm is implemented as a real-time manner in terms of the down-range variable. Findings - The proposed algorithm tries to find not only a feasible trajectory (the constraint includes the maximum heading angle rate) but also an optimal trajectory (the objective locally is to minimize the length of the path). This online trajectory planning algorithm gradually produces a smooth 2D trajectory aiming at reaching the mid-course WPs and the final target so that they are smoothly connected with each other. The mid-course WPs are described through the given down-range, cross-range, and heading angle. Originality/value - Based on geometrical principles, this algorithm is capable of re-planning the trajectory as in-flight manner, and the computational burden approaches the online capabilities for UAVs with high velocity. © 2010 Emerald Group Publishing Limited.
Aircraft Engineering and Aerospace Technology (17488842) 82(3)pp. 194-203
Purpose - The purpose of this paper is the optimal design of a reentry vehicle configuration tominimize the mission costwhich is equal tominimize the heat absorbed (thermal protection system mass) and structural mass and to maximize the drag coefficient (trajectory errors and minimum final velocity). Design/methodology/approach - There are two optimization approaches for solving this problem: multiobjective optimization (lead to Pareto optimal solutions); and single-objective optimization (lead to one optimal solution). Single-objective genetic algorithms (GA) and multiobjective Genetic Algorithms (MOGA) are employed for optimization. In second approach, if there are n objectives (n + 1) GA run is needed to find nearest point (optimum point), which leads to increase the time processing. Thus, a modified GA called single run GA (SRGA) is presented as third approach to avoid increasing design time. It means if there are n objectives, just one GA run is enough. Findings - Two multi module function - Ackley and bump function - are selected for examination the third approach. Results of MOGA, GA and SRGA are presented which show SRGA approach can find the nearest point in much shorter time with acceptable accuracy. Originality/value - GA, MOGA and SRGA approaches are applied to multidisciplinary design optimization of a reentry vehicle configuration and results show the efficiency of SRGA in complex design optimization problem. © Emerald Group Publishing Limited.
Journal of Aircraft (00218669) 47(4)pp. 1391-1398
This paper proposes an efficient algorithm with a novel procedure for trajectory planning of unmanned aerial vehicles in three-dimensional space. This work has been motivated by a challenge to develop a fast trajectory planning algorithm for autonomous unmanned aerial vehicles through the midcourse waypoints. The waypoints that are defined as a preflight or in-flight procedure are described in a five-dimensional configuration: the position in three dimensions plus desired crossing heading and flight-path angles. For achieving the waypoints, the Dubins path is extended to three-dimensional applications by using the geometrical concepts. In addition, the trajectory planning algorithm is represented as a set of ordinary differential equations called optimal-constrained-trajectory kinematics by applying the differential geometry concepts. Optimal-constrained-trajectory kinematic is a closed-loop guidance law that generates the guidance commands based on the waypoint configuration and minimum turning radius and is solved in a real-time manner. The proposed algorithm includes an operational framework that leads to gradually generating the smooth three-dimensional trajectory, aimed at reaching the midcourse targets and final target so that they are smoothly connected to each other. Finally, the simulation results show the capability of the algorithm in dynamic trajectory planning in low computational burden. Copyright © 2010 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.
Transactions of the Japan Society for Aeronautical and Space Sciences (05493811) 50(170)pp. 225-230
An explicit guidance law is developed for a reentry vehicle. Motion is constrained to a three-dimensional Bezier curve. Acceleration commands are derived by solving an inverse problem related to Bezier parameters. A comparison with pure proportional navigation shows the same accuracy, but a higher capability for optimal trajectory to some degree. Other advantages such as trajectory representation with minimum parameters, applicability to any reentry vehicle configuration and any control scheme, and Time-to-Go independency make this guidance approach more favorable. © 2008 The Japan Society for Aeronautical and Space Sciences.
This paper presents the conceptual design of a sun-synchronous LEO Earth Observation microsatellite incorporated with Multidisciplinary Design Optimization (MDO) approach. The objective is to develop a structured system level for the design process including mission design parameters such as required Revisit Time (RT), and accessible Ground Sampling Distance (GSD). In order to apply MDO with the design process a sizing tool has been developed based on both mission and system design-estimating relationships. In addition, the conceptual design data and concepts of the microsatellites with the similar mission have been used to achieve a reliable sizing tool. The objective is to minimize the total mass of the satellite. A Genetic Algorithm (GA) is coupled with the developed sizing tool to obtain optimized design parameters for the microsatellite. © 2008 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.
Aerospace Science and Technology (12709638) 12(3)pp. 241-247
An explicit guidance law that maximizes terminal velocity is developed for a reentry vehicle to a fixed target. Motion is constrained to an optimal, three-dimensional Bezier curve. Acceleration commands are derived by solving an inverse problem related to Bezier parameters. An optimal Bezier curve is determined by solving a real-coded genetic algorithm. For online trajectory generation, optimal trajectory is approximated by fixing the second control point of the Bezier curve. The approximated trajectory is compared with the pure proportional navigation, genetic algorithm and direct transcription's solutions. The near optimal terminal velocity solution compares very well with these solutions. The approach robustness is examined by Monte Carlo simulation. © 2007 Elsevier Masson SAS. All rights reserved.
Inverse Problems in Science and Engineering (17415985) 16(2)pp. 187-198
An explicit guidance law that maximizes terminal velocity is developed for a re-entry vehicle to a fixed target. Motion is constrained to an optimal, 3D Bezier curve. Acceleration commands are derived by solving an inverse problem related to Bezier parameters. An optimal Bezier curve is determined by solving a real-coded genetic algorithm. For online trajectory generation, optimal trajectory is approximated by fixing the second control point of the Bezier curve. The near optimal trajectory is compared with the genetic solution and with a form of proportional navigation. The near optimal terminal velocity solution compares very well with the genetic solution and is superior to the proportional navigation one. The approach robustness is examined by Monte Carlo simulation.
Proceedings of the IASTED International Conference on Modelling, Identification, and Control, MIC (22935126) pp. 247-252
In this paper, a 3-axis motion simulator, as a three degreeof-freedom test stand for aircraft instrument testing and calibrating within a Hardware-In-The-Loop Environment, is studied for control analyses. A mathematical model of the simulator mechanical structure is derived and then linearized using Taylor series expansion around the instantaneous equilibrium point which is the aircraft timedependant Euler angles and their rates. Also, the aircraft, earth and atmosphere are modeled in Matlab using Aerosim blocksets. A linear quadratic regulator (LQR) control law is developed to track the attitude, angular rates and angular acceleration of the Navion aircraft in a complicated maneuver. The control law is shown to be efficient in the presence of atmospheric turbulence, and robust to unknown bounded disturbances. The accuracy and correctness of the proposed control system is verified by the simulation.
WSEAS Transactions on Systems (11092777) 5(12)pp. 2875-2880
Although direct and indirect methods have been widely applied to trajectory optimization problems, optimization results for these methods are sensitive to initial solutions in some cases. For the purpose of finding an appropriate initial solution of rendezvous problem to calculus-of-variations-based trajectory optimization, a numerical trajectory optimization method using a real-coded genetic algorithm is considered. The genetic algorithms are not hampered by ill-behaved gradients and are relatively insensitive to problems with a small radius of convergence. Those have been successfully applied to numerical optimization problems. The use of calculus of variations within the genetic algorithm optimization routine increases the precision of the final solution to levels uncommon for a genetic algorithm alone.
