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This study was done to investigate the effect of perturbations of both oblate massive primaries on the periodic orbit-attitude behavior of satellites at the three-body problem. Governing equations of the perturbed coupled orbit-attitude were derived using principles of the Lagrangian mechanics. As initial conditions of the coupled model play a key role in finding periodic responses of both orbit and attitude, so the perturbed circular restricted three-body problem (P-CR3BP) coupled orbit-attitude correction algorithm was proposed to correct the initial guess of the coupled model. The correction algorithm requires an appropriate initial guess vector as the number of periodic solutions is limited. This initial guess vector consists of two parts: orbital and attitude dynamic state parameters. A suitable initial guess was suggested for orbital parameters considering a small error at the initial conditions of the unperturbed periodic orbits. These initial conditions were extracted by another orbital correction algorithm to correct orbital states of periodic orbits in the unperturbed circular restricted three-body problem (U-CR3BP). Furthermore, the initial guess of attitude dynamic parameters was identified by the Poincare mapping method. Considering oblate perturbations would change the initial conditions of periodic orbits at P-CR3BP relative to U-CR3BP. Also, because the proposed model of the coupled orbit-attitude governing equations is such that only orbital state parameters influence attitude dynamic parameters, so the behavior of attitude dynamics will also change by altering orbital motion. Comparison of patterns in Poincare maps, satellites angular velocity, and initial conditions of periodic coupled orbit-attitude response in U-CR3BP and P-CR3BP indicated the effect of both oblate primaries perturbations. Consideration of these perturbations resulted in a more accurate simulation of the study environment that helps to understand the natural motion of the satellite.

Remote sensing satellites propulsion subsystem preliminary design based on performance sizing is discussed. The process is a fairly complicated one considering a variety of operational as well as performance requirements. This research aims to reduce the number of iterations required by the current practice to reach a compromise. It is based on a rapid-sizing method that helps lessen lengthy iteration cycles. The proposed technique, similar to such class of preliminary design processes, leads to an acceptable approximation that must be properly conveyed to other disciplines involved for further elaborations. With proper selection of design and performance parameters together with fundamental relations, design boundaries are established; while the role of boundaries is to limit the design space, and to minimize the number of design cycles as a result. With the help of a suitable case-study, we demonstrate the effectiveness of the approach. Further investigations might be necessary for the cases where a greater number of uncertainties are involved.

In this paper, a new methodology was proposed for finding optimal robust-reliable parameter values of a space-based earth observation (SEO) mission based on a predefined multi-purpose platform considering influential uncertainties and human judgment. A deterministic optimization was performed using an evolutionary algorithm on the basis of platform capability and mission-required performance, simulated by a dynamic simulation-based model. A set of non-dominated solutions with different behaviors emerged. Then, samples of these solutions were selected and an uncertainty analysis was carried out. After that, a multiple attribute decision-making (MADM) problem was formed with two groups of attributes related to constraints violations and variation of objective function value having unknown weights. Dominant ranking of the non-dominated solutions was obtained by simulating this MADM problem for adequate times with different random weights (human judgment effects). In the next step, given the outputs obtained from solving MADM problem, an optimal robust-reliable solution could be determined utilizing two approaches, response surface methodology (RSM) and forming a new uncertainty-based multidisciplinary design optimization (UMDO) problem. The results showed that utilizing MADM approach not only brings the effects of human judgment into design problem but also accelerates convergence to optimal robust-reliable solution in multimodal problems by bounding the search space without any risk regarding getting stuck in local optimal regions.

Purpose Attitude determination and control subsystem (ADCS) is a vital part of earth observation satellites (EO-Satellites) that governs the satellite’s rotational motion and pointing. In designing such a complicated sub-system, many parameters including mission, system and performance requirements (PRs), as well as system design parameters (DPs), should be considered. Design cycles which prolong the time-duration and consequently increase the cost of the design process are due to the dependence of these parameters to each other. This paper aims to describe a rapid-sizing method based on the design for performance strategy, which could minimize the design cycles imposed by conventional methods. Design/methodology/approach The proposed technique is an adaptation from that used in the aircraft industries for aircraft design and provides a ball-park figure with little engineering man-hours. The authors have shown how such a design technique could be generalized to cover the EO-satellites platform ADCS. The authors divided the system requirements into five categories, including maneuverability, agility, accuracy, stability and durability. These requirements have been formulated as functions of spatial resolution that is the highest level of EO-missions PRs. To size, the ADCS main components, parametric characteristics of the matching diagram were determined by means of the design drivers. Findings Integrating the design boundaries based on the PRs in critical phases of the mission allowed selecting the best point in the design space as the baseline design with only two iterations. The ADCS of an operational agile EO-satellite is sized using the proposed method. The results show that the proposed method can significantly reduce the complexity and time duration of the performance sizing process of ADCS in EO-satellites with an acceptable level of accuracy. Originality/value Rapid performance sizing of EO-satellites ADCS using matching diagram technique and consequently, a drastic reduction in design time via minimization of design cycles makes this study novel and represents a valuable contribution in this field.

In order to achieve the best possible performance for an artificial satellite, its position and attitude must be maintained within predetermined limits. This is of great importance, especially in the presence of environmental disturbances and system uncertainties. Therefore, performing correction maneuvers is inevitable. This article puts forward a new hierarchal strategy based on fuzzy logic and optimal control theories for correcting the orbital parameters of a geostationary satellite affected by environmental disturbances. The strategy consists of two successive systems referred to as decision-making and optimizing systems. These systems, based on the assessments performed on position and velocity deviations from the nominal reference trajectory, determine the appropriate moments to fire thrusters and their action durations. In this research, the first task, i.e., determining the firing moments of thrusters was accomplished using a fuzzy logic supervisory system. On the other hand, the second task was performed with a direct optimization method based on minimum-fuel station-keeping criterion while considering the constraints of station-keeping problem. The proposed strategy was applied on a geostationary satellite and for a period of 30 days of its motion, and both north–south and east–west orbital corrections were performed. The results obtained from the simulation analysis indicated that the proposed strategy effectively improved the orbital correction procedure of the satellite in the station-keeping mode.

Extensive research in recent years has focused on improving the current loss-of-control prevention systems and developing new strategies for safe path planning of the impaired aircraft. Success in developing such systems requires a comprehensive perception of the influence of damage on the aircraft’s dynamic behaviour and performance, and the effect of various failure degrees on the flight envelope confinement and the remaining safe maneuvers. This paper comprehensively describes the effects of lateral control surface failure on the NASA Generic Transport Model (GTM) flight envelope, defined by a set of attainable steady-state maneuvers herein referred to as trim points. The study utilises a large database of high-fidelity maneuvering flight envelopes computed for the unimpaired case and wide ranges of the aileron and rudder failure cases at different flight conditions. Flight envelope boundary is rigorously investigated, and the key parameters confining the trim points at different boundary sections are identified. Trend analyses of the impaired flight envelopes and the corresponding limiting factors demonstrate the effect of various failure degrees on the remaining feasible trim points. Results can be employed in emergency path planning with potential uses in the development of aircraft resilient control and upset recovery systems.

In this study, the three-dimensional optimal trajectory planning of an unmanned fixed-wing aerial vehicle was investigated for Terrain Following – Terrain Avoidance (TF-TA) purposes using the Direct Collocation method. For this purpose, firstly, the appropriate equations representing the translational movement of the aircraft were described. The three-dimensional optimal trajectory planning of the flying vehicle was formulated in the TF-TA manoeuvre as an optimal control problem. The terrain profile, as the main allowable height constraint was modelled using the Fractal Generation Method. The resulting optimal control problem was discretized by applying the Direct Collocation numerical technique and then, was transformed into a Nonlinear Programming Problem (NLP). The efficacy of the proposed method was demonstrated by extensive simulations, and it was particularly verified that the purposed approach can produce a solution satisfying almost all the performance and environmental constraints encountering in a low -altitude flight.

Extensive research in recent years has focused on developing flight envelope estimation methods to improve current loss of control prevention and recovery systems. Such methods are practically efficient only if they are able to evaluate in real time the new flight envelope of damaged aircraft based on the altered dynamics. Due to nonlinear dynamics of aircraft, common approaches to estimate the entire flight envelope of high-fidelity models are numerically intensive and their real time implementation is computationally impossible. So current methods are based on reduced complexity models or flight envelopes are determined locally. This paper presents a novel method to estimate the global flight envelope of impaired aircraft in real-time for any unknown failure degree. In the proposed method, first, numerous flight envelopes are evaluated using a high fidelity model at various failure degrees and different flight conditions and prepared as training data. Then multiple feedforward neural networks are trained offline by a Bayesian regularization backpropagation algorithm. Finally, the trained networks are used to estimate flight envelopes in real time. The method is applied to rudder and aileron failure cases of the NASA Generic Transport Model. Results show that the estimated flight envelopes are good approximations of the high fidelity global flight envelopes.

This paper presents an efficient metamodel-based multi-objective multidisciplinary design optimization (MDO) architecture for solving multi-objective high fidelity MDO problems. One of the important features of the proposed method is the development of an efficient surrogate model-based multi-objective particle swarm optimization (EMOPSO) algorithm, which is integrated with a computationally efficient metamodel-based MDO architecture. The proposed EMOPSO algorithm is based on sorted Pareto front crowding distance, utilizing star topology. In addition, a constraint-handling mechanism in non-domination appointment and fuzzy logic is also introduced to overcome feasibility complexity and rapid identification of optimum design point on the Pareto front. The proposed algorithm is implemented on a metamodel-based collaborative optimization architecture. The proposed method is evaluated and compared with existing multi-objective optimization algorithms such as multi-objective particle swarm optimization (MOPSO) and non-dominated sorting genetic algorithm II (NSGA-II), using a number of well-known benchmark problems. One of the important results observed is that the proposed EMOPSO algorithm provides high diversity with fast convergence speed as compared to other algorithms. The proposed method is also applied to a multi-objective collaborative optimization of unmanned aerial vehicle wing based on high fidelity models involving structures and aerodynamics disciplines. The results obtained show that the proposed method provides an effective way of solving multi-objective multidisciplinary design optimization problem using high fidelity models.

In this paper a new methodology has been present for decentralized trajectory design problem of the aircrafts in a dynamic environment. Multiphase trajectory planning consists of the normal mode or the optimal trajectory planning through the static obstacles, the cooperative conflict resolution mode, and non-cooperative conflict resolution mode under low altitude flying rules. The normal mode changes if the conflict occurs between two or more aircrafts during their mission. It is considered that all the aircrafts being in a conflict zone could communicate with each other. In the conflict resolution modes, the radius of the conflict zone could be adapted from the definition of the minimum time of the aircraft conflict. The aggregated nonlinear point mass equations of the aircrafts motion with some operational constraints were utilized for modeling explanation of the problem. The effectiveness of the introduced method was investigated via a case study including cooperative and non-cooperative conflict resolution for the aircrafts which may fly in a obstacle-laden environment.

In this paper a path planning technique will be introduced for the unmanned aerial vehicles (UAV) fly at low altitude using a synthetic approach based on game theory and artificial neural networks. The low altitude pursuit-evasion maneuver of two UAVs - is defined based on an optimal control approach. Moreover, it has been sought to utilize optimal control rules and Differential Games theory to calculate the most favorable trajectories for both UAVs – one as the pursuer and the other as an evader. Since producing the optimal trajectories through solving the related equations may be a time-consuming trend, an artificial neural network is utilized to predict the flyable trajectories. The multilayer perceptron networks are trained using a set of trajectories obtained based on the differential game theory approach and could locate the position where the evader is captured. Hence, choices could made in real times. Consequently, the comparison of neural network results with accurate data obtained previously in the optimal control section confirms the accuracy and performance of the proposed method.

This article proposes a new approach for centralized path planning of multiple aircraft in presence of the obstacle-laden environment under low flying rules. The problem considers as a unified nonlinear constraint optimization problem. The minimum time and control investigate as the cost functions and the maximum velocity and power consider as the constraints. The pseudospectral method applies as a popular and fast direct method to solve the constrained path planning problem. The three-degree-of-freedom nonlinear point mass equations of motion with realistic operational aircraft constraints consider through the simplified mathematical model. The fixed obstacle considers as a combination of spheres with different radius. Also, the moving obstacles consider as a sphere with a known radius and fly at a constant speed. The effectiveness of the proposed concept will be demonstrated by presenting four case studies with a different number of aircraft along with the static and moving obstacles in various scenarios to ensure safe and effective flights.

Avoiding collision with obstacles and terrain has always been a critical issue for aircraft during low altitude flight landing and approach maneuvers, especially in airports located in mountainous areas. In low altitude flight, it is desirable to find a safe trajectory that is compatible with the aircraft capabilities and at the same time optimizes flight efficiencies. Safety is a fundamental concern when in terrain following (TF) and/or terrain avoidance (TA) flight modes, and aircraft design for these types of missions emphasizes the inclusion of terrain and obstacle avoidance flight (TOAF) capabilities.

With growing interest in low-cost, high performance and short time-to-flight satellites, the idea of using multi-purpose satellite platforms has drawn many attentions. However, under certain conditions in which the required capability of a mission is slightly more than the capabilities of a specified variant of a multi-purpose platform, it must jump to the next higher capability variant. Correspondingly, lots of unnecessary performance or capability might be imposed. In this paper, the main goal is to minimize the wasted performance in the design of an Earth Observation (EO) satellite based on a multi-purpose platform. To this end, a dynamic simulation-based model has been developed with the capability of simulating the satellite performance characteristics, EO mission requirements and supportable performance by the platform under various circumstances and throughout the satellite lifetime. To minimize the wasted performance we face with an optimization problem containing a complicated non-linear, non-convex and multi-modal design space which is enveloped by constraints of the mission requirements and supportable performance of the platform. A novel criterion as an objective function has been introduced aiming at the incompatibility reduction between the capabilities of a certain multi-purpose platform variant and the EO mission requirements. This objective function considers all the performance variations during the entire mission lifetime rather than solely the worst case. Through this, the mission and orbital characteristics have been determined by which there would be no need to jump to the higher performance variant in order to satisfy the EO mission requirements.

Some fundamental and unique characteristics of space are (1) global perspective, (2) above the atmosphere, (3) gravity-free environment, (4) abundant resources, and (5) exploration of space itself. If the mission doesn't rely on some fundamental and unique characteristics of space, it will likely cost more to do in space than in air or on Earth [1]. On the other hand, design of complex systems like satellites involves selecting design parameters (DPs) to satisfy the required constraints while meeting desired performance objectives. These parameters are often coupled, and their relationships not easily understood, and it makes the design an iterative process with high complexity [2]. But in spite of high costs and complexities, demands for space missions are increasing, so space missions inevitably must be engineered in such a way that the users' needs be met better, cheaper, faster, and with less risk than before. That is the reason why more research is required to improve the space missions engineering process.

In this paper, a new method for controlling and guidance of guided gliding vehicle (GGV) is provided. In this regard, a hybrid structure of nonlinear optimal control has been proposed to minimize control effort. The inner loop has a regulating function that guarantees the stability of motion and rotation equations and reduces the effect of external disturbances. The outer loop provides optimal tracking with a straight line scroll criterion for the GGV. The use of state dependent Riccati equation control involves determining the appropriate state dependent coefficient (SDC) form, which requires a complete understanding of the dynamics of the system. One of the methods for determining the relative dynamics of a nonlinear time-varying system and calculating SDC is the online identification method. The advantage of this method in determination of the SDC is more evident when it is not possible to fully understand the dynamics of the system or external factors and disturbance affect the system. This method also eliminates the problem of uncertainty and accurate measurement of parameters. In other words, it provides an adaptive model for different conditions. In this paper, the ANFIS network has been used for the first time for continuous and online identification of the system dynamics and calculating SDC. In order to ensure the stability of identification, the identifier will be trained first with the particle swarm optimization and the online back-propagation learning algorithm. The stability of the closed loop system with a proposed hybrid structure is investigated with the help of the Lyapunov function and the concept of passivity. The optimal and robust performance of the proposed framework has been investigated in terms of the ability to simultaneously pursue the target in the optimal path despite disturbance and optimize the control effort with multiple simulations.

Flow separation, as an aerodynamic phenomenon, occurs in specific conditions. The conditions are studied in a wind tunnel on different airfoils. The phenomenon can be delayed or suppressed by exerting an external momentum to the flow. Dielectric barrier discharge actuators arranged in a row of 8 and perpendicular to the flow direction can delay flow separation by exerting the momentum. In this study, a mathematical model is developed to predict a parameter, which is utilized to represent flow separation on an NACA0012 airfoil. The model is based on the neuro-fuzzy method applied to experimental datasets. The neuro model is trained in different flow conditions and the parameter is measured by pressure sensors.

In this paper, a new path planning method is proposed to resolve the problem of two-dimensional terrain following flight of flying robots in mountainous regions. The performance criteria considered for this mission design could include either the minimum vertical acceleration or the minimum flying time. To impose the terrain following/terrain avoidance constraints, various approaches such as least square method, Fourier series method, Gaussian estimation method, and Chebyshev orthogonal polynomial are explored. The resulting optimal control problem is discretized by employing a numerical technique namely direct collocation and then transformed into a nonlinear programming problem. The efficacy of the proposed method is demonstrated by extensive simulations, and particularly, it has been verified that this method is able to produce a solution that satisfies all hard constraints of the underlying problem.

This paper presents an operational approach for decentralized agent-based path planning for autonomous flying vehicles such as electrical vertical takeoff and landing that can perform in the obstacle-laden environment. This concept is considered as a hybrid model with three modes consist of, the optimal path planning through obstacles, cooperative conflict resolution, and non-cooperative conflict resolution. In the development of this concept, the direct Radau-pseudospectral method has been employed. The nonlinear point mass equations of motion with the realistic operational constraints of the flying vehicle in three-dimensional space are utilized for path planning. The effectiveness of the proposed approach has been demonstrated via presenting a case study in minimum time including changing modes and forced to fly in the obstacle-laden environment.

Weapon-Target Assignment (WTA) as an important part of aerial defense cycle has long been ‎studied. Challenges are usually finding fast-computing methods to search optimal or near-‎optimal solution in cases of a large number of weapons and targets. This viewpoint is more ‎mathematically considerable but practically has limited usage in the mentioned context. A ‎real-time search algorithm is proposed which decomposes the WTA problem and by ‎decreasing the size of solution space and deleting impossible solutions, enables real-time ‎exhaustive search algorithm. Implementation of the algorithm for three typical scenarios shows ‎excellent real-time performance and the possibility of finding exact solutions for large-scale ‎problems.‎

In this paper, a new methodology has been proposed to enhance the conformal mapping applications in the process of optimum trajectory planning in Terrain Following (TF) and Terrain Avoidance (TA) Flights. The new approach uses the conformal mapping concept as a flattener tool to transform the constrained trajectory-planning problem with flight altitude restrictions due to the presence of obstacles into a regenerated problem with no obstacle and minimal height constraints. In this regard, the Schwarz–Christoffel theorem has been utilized to incorporate the height constraints into the aircraft dynamic equations of motion. The regenerated optimal control problem then is solved employing a numerical method namely the direct Legendre-Gauss-Radau pseudospectral algorithm. A composite performance index of flight time, terrain masking, and aerodynamic control effort is optimized. Furthermore, to obtain realistic trajectories, the aircraft maximum climb and descent rates are imposed as inequality constraints in the solution algorithm. Several case studies for two-dimensional flight scenarios show the applicability of this approach in TF/TA trajectory-planning. Extensive simulations confirm the efficiency of the proposed approach and verify the feasibility of solutions satisfying all of the constraints underlying the problem

The flight dynamics of Wing in Ground Effect (WIG) crafts over sea waves in the layer of Strong Ground Effects (SGE) are complex phenomena. In this article, the longitudinal flight dynamics of a small WIG are investigated, thereby showing a nonlinear dynamic behaviour due to variable aerodynamic coefficients that could be affected by the vehicle flight altitude and other environmental parameters such as flying over short, moderate, and long regular sea waves. Instability regions are analysed and compared to traditional simplified equations of motion for different geometries of waves. Simulations are arranged for variations in both manoeuvre requirements and flying vehicle parameters (e.g. cruise speed and mass moment of inertia). A sensitivity analysis is carried out to evaluate the magnitude of a vehicle's lift coefficient as a function of sea wave length and amplitude in terms of some real-world uncertainty sources. The efficacy of the proposed method is demonstrated through the application of simulations. In particular, it is verified that this method is able to produce more reliable results in satisfying constraints and requirements of the underlying problem. Dynamics of a WIG craft in SGE layer.

Nowadays, as more systems are regarded to be complex, the processes to develop them are becoming more complex too. This increasing trend makes more effective analysis methods inevitable necessities. Meanwhile, matriarchal tools are becoming the most prominent, this paper has tried to study a work transformation matrix (WTM) at the level of coupled blocks of activities (CBAs). The neighboring matrices of such a WTM act as its information flow interfaces with other activities. It is shown how these are equivalent to some state-space representation matrices. The equivalency helps us to improve the analysis method for a set of CBAs constituting the whole WTM. Additionally, the concepts of powers of adjacency matrices and elementary dynamic properties of process as a system in modern control theory are brought together to analyze the behavior of activities in coupled blocks. By analyzing these properties in a case study, five heuristic are derived and proposed for improving the process through better preference and ordering of activities. Effectiveness of these heuristics is tested by a simple discrete-time simulation for which results show 17% of time reduction for the case study.

In contrast to the case of known environments, path planning in unknown environments, mostly for humanoid robots, is yet to be opened for further development. This is mainly attributed to the fact that obtaining thorough sensory information about an unknown environment is not functionally or economically applicable. This study alleviates the latter problem by resorting to a novel approach through which the decision is made according to fuzzy Markov decision processes (FMDP), with regard to the pace. The experimental results show the efficiency of the proposed method.

In case of technical failures or external events such as control surface defects or icing, aircraft dynamics and parameters are changed. Due to nonlinear dynamics of aircraft, usually the exact new altered dynamics cannot be determined by the pilot. Therefore the pilot who tries to plan a safe landing trajectory as soon as possible may implement a maneuver which is not feasible anymore according to the altered dynamics of the impaired aircraft and leads to aircraft loss of control (LOC). Therefore, the main challenge in the prevention of LOC-led-accidents is to increase the pilot's situational awareness and develop better control systems which both require post-failure dynamics identification and modeling. Both neural networks and neuro-fuzzy systems can be used for high-fidelity modeling of the aircraft nonlinear dynamics, however, the one with better generalization capability should be chosen. In this paper, several neural networks and local model networks are developed to model the nonlinear dynamics of an impaired aircraft with damaged rudder. These networks are trained using different training algorithms and their generalizations to the new cases of rudder failure are compared. Results show that both network types have good performance but neural networks generalize better to the new data than local model networks.

This paper proposed a new methodology for optimal decentralized trajectory planning of multiple flying robots, in an obstacle-laden environment by incorporating obstacle avoidance and conflict resolution into one unified optimal control trajectory generation framework. In other words, an optimal trajectory that; firstly, passes through all obstacles, secondly maintains the safe flying zone for each of flying robot could be determined. In development of this path planner a Radau-pseudo spectral optimization method has been employed. The nonlinear point mass equations of motion of the flying vehicle in three dimensional space are utilized and realistic operational constraints influencing on flying vehicle state and control variables are considered via simplified mathematical models. The effectiveness of the proposed approach for solving centralized trajectory optimization problems, has been demonstrated via presenting a case study including two robots forced to fly in a hazardous area with fixed obstacles

The design structure matrix (DSM) is a potent tool in the management of product design processes. Although the compactness and ability to represent design cycles are the main advantages of DSMs over existing traditional tools, the intact whole DSM is not always an understandable piece of information. To overcome this shortcoming, certain analyses have been proposed for a better understanding of the matrix in which partitioning and tearing have significant importance. There are several algorithms for these two analyses that mainly focus on a few rules of thumb. Although partitioning and tearing were originally developed for binary DSMs, they can be extended to numerical variants in which the work transformation matrix (WTM) is of the highest fame and application. In this paper, the authors have proposed an algorithm inspired by the formation of sugar crystals in saturated syrup for reordering the activities in a coupled block of activities (CBAs) based on their level of coupling. To implement this approach, a code was developed to achieve pseudo-optimum solutions. By using a discrete-time simulation, which was applied to an aerospace case study, it was demonstrated that the method produces restructured schemes of the WTM that are comparable/superior to the classical methods.

In this work, a new systematic method is proposed to enhance the process of optimum trajectory planning in Terrain / Obstacle Avoidance Flights (TOAF) with use of conformal mappings as a facilitator of the effect of the flight altitude restriction on the route selection. The use of Conformal Mapping concept could help to transform a constrained trajectory planning problem, in presence of obstacles or any height constraint, into a simple pseudo obstacle-free optimal trajectory-planning problem. Suitable case studies show that the proposed approach is more consistent with basic flight concepts as well as real world applications. Applicability of the introduced approach is demonstrated by solving a 2-D obstacle-avoidance motion using direct pseudo-spectral method.

This paper studies the use of a fuzzy proportional integral derivative (PID) controller based on a genetic algorithm (GA) in a docking maneuver of two spacecraft in the space environment. T...

This paper is concerned with the problem of the two-dimensional optimal online trajectory generation for a flying robot for terrain following/terrain avoidance purposes using neural network. To this end, the terrain is modeled as a terrain following constraint. In the next step, the inverse dynamics method is utilized for offline trajectory generation. Since the main objective of this research is online trajectory generation, the neural network method is employed for this purpose. To train the neural network, various approaches such as steepest descent, conjugate gradient, resilient back propagation, and Levenberg–Marquardt are explored in detail, and finally, the Levenberg–Marquardt method is selected on account of some merits. The efficacy of the neural network method is demonstrated by extensive simulations, and in particular, it is verified that this method is able to produce a solution satisfying all hard constraints of the underlying problem.

Article history: Received March 6, 2014 Accepted 23 August 2014 Available online 24 August 2014 In this paper, we have provided hands-on experience in systematic design, implementation and flight test of an atmospheric data acquisition flying vehicle as a standard CanSat telemetry mission. This system is designed for launching from a rocket at a separation altitude about 1000-meter. During its flight, the reusable flying vehicle collects environmental data and transmits it directly to the ground station. The ground station, which is implemented at a predefined radio frequency band receives data and plots the respective graphs. The design performs based on a systematic approach, in which the first step is set aside to mission and objectives definition. In the next step, the system requirements are identified and the required main subsystems and elements with their technical requirements will be extracted. The structure analyses were also performed by ABAQUS software to obtain the natural frequency and the mode shape. The wireless communications, onboard microcontroller programming, sensor interfacing and analog to digital conversion describe the basic technologies employed in the system implementation. This flying vehicle in comparison with the other similar ones is more lightweight, has few interface circuits and high precision sensors. According to the flight test outputs, low power consumption, high transmit line up to 2Km despite of limitation in TX power and up to 10g normal acceleration withstanding are important specific characteristics of the implemented flying system. © 2014 Growing Science Ltd. All rights reserved.

In this work we present a systematic approach to incorporating the dynamic capabilities of an aircraft via suitable cost functions while planning for terrain following-terrain avoidance trajectories. We further show that use of nondimensional parameters related to the dynamics of an aircraft within the cost function provides a better understanding of how they could affect the resulting trajectories. Different case studies for arbitrary three-dimensional flights show that the current approach provides more realistic trajectories and so helps alleviate the design process of relevant controllers as well as tracking systems over the desired terrain. We further introduce two new parameters of "required in terrain maneuvering capability" and "available in terrain maneuvering capability," which evaluate an aircraft for terrain-following missions in the early stages of its design process. They can also help categorize existing aircraft for such capabilities.

A new methodology has been proposed to enhance inverse dynamics applications in the process of trajectory planning and optimization in terrain following flights (TFFs). The new approach uses a least square scheme to solve a general two-dimensional (2-D) TFF in a vertical plane. In the mathematical process, Chebyshev polynomials are used to model the geographical data of the terrain in a given route in a manner suitable for the aircraft at hand. The aircraft then follows the modeled terrain with sufficient clearance. In this approach the terrain following (TF) problem is effectively converted to an optimal tracking problem. Results show that this method provides a flexible approach to solve the TFF problem especially in conditions where the existing terrain to be flown over is not mathematically well behaved

Fuzzy gain-scheduling is one of the prominent procedures of fuzzy controller design which is employed to control a nonlinear model of a Quadrotor in this article. For this purpose, a set of intuitively designed linguistic rules is developed for the Quadrotor to track an optimal path obtained through direct collocation method. The fuzzy controller is shown to exhibit excellent tracking performance. In addition, the effect of importing drag force as a nonlinear term in dynamics equations in model accuracy and performance of fuzzy controller has been investigated.

This paper presents intelligent lighting control system for smart home. The AC power source is converted down with an AC to DC switching power supply, then is delivered to some LEDs with an LED driver. This paper proposes two methods of lighting control. In the manual control method, LED is controlled with a PWM signal. In fact, according to the user command, it generates the proper duty cycle in a PWM signal. In the automatic control method, the LED is controlled with a signal generated by the PID control algorithm. In this method, the photocell measures the ambient light level and with reference to the input command by the user, proper PWM signal is generated. Circuit schematics and the implementation details are presented. A laboratory prototype is also designed and tested to verify the feasibility, and the experimental results are demonstrated.

Nowadays, optimal trajectory planning and trajectory tracking have become the main subject of numerous research projects, and have a wide range of applicability in planning missions for a lot of marine vehicle, such as unmanned submarines, buoyant vehicles, ships, landing crafts, etc. This paper investigates the problem of path planning of a submarine, with simultaneous utilization of the direct collocation method and Pontryagin's Minimum Principle, intending to minimize its detection probability. Moreover, tracking of the path obtained from the direct collocation method is provided through model predictive control, with the two-dimensional motion relations of the submarine as predicting model comparison of the results obtained from implementing these two methods reveals that the cost associated with the direct collocation method almost closely matches the one corresponding to the indirect method. There is a negligible difference between the costs which, in fact, is due to the utilization of a numerical solution procedure in the former and an analytical one in the latter. Nevertheless, when it comes to the duration of solution iterations, the collocation method is faster, and as a result, despite the other approach, practicable enough to be able to solve complex problems. Furthermore, the results confirm the capability of the model predictive control for real-time path tracking.

In this paper, LQG/LTR controller is designed for attitude control of the geostationary satellite at nominal mode. Usage actuator is the reaction wheel and control torque is determined by the LQR regulator. LQR controller signal has good performance, if all states are considered in feedback, but does not include model and sensors noises. Usage sensors are sun and earth sensors and EKF is used for estimation of noisy states. Then, LQG and LQG/LTR controllers are designed based on the estimated states, and are compared with LQR controller. The results show that robustness and performance of LQG/LTR are better than LQG and its control overshoot is smaller than LQR. The term that is provided in designs, is avoiding of saturation of actuator.

In this work we consider a new technique for the control allocation problem for an over-actuated GEO Communications satellite. Control allocator system computes the required commands that should be applied to the optimum distributed thrusters on the outer surface of the satellite so that a certain set of torques would be generated using this actuators. The number of actuators is greater than the number of torque necessary to be compensated, and the controlling ranges of the actuators are constrained to the certain pre-specified linear active domains. Control allocation problem is formulated as an optimization problem so that all of the available degrees of freedom can be utilized. When sufficient control power exists, secondary performance objectives could be achieved. The proposed methodology enables reconfiguration control in the presence of actuator failures, while the controller keeps being unchanged. The differential evolution optimization method is used to allocate control commends. The performance and accuracy of the proposed method have been verified using pseudo-inverse and linear programming methods. The case study results applied to a typical GEO satellite control subsystem design, demonstrates the effectiveness of the differential evolution method in comparison with the other algorithms.