This paper presents an attitude control algorithm for a Reusable Launch Vehicle (RLV) with a low lift/drag ratio (L/D < 0. 5), in presence of external disturbances, model uncertainties, control output constraints and the thruster model. The main novelty of the proposed control strategy is a new combination of the attitude control methods including backstepping, dynamic inversion, and adaptive control methods which will be called Backstepping-dynamic inversion-Adaptive (B. D. A) method. In the proposed method, a single control variable is considered as the bank angle while the angle of the attack and the side slip angle will be stabilized in their inherent value. The purpose of this control is the attitude control of the vehicle to track the commanded bank angle and keep the vehicle in the desired trajectory. Lyapunov stability analysis of the closed-loop system will be performed to guaranty the stability of the vehicle in the presence of constraints. Performance of the controller will be evaluated based on six Degrees of Freedom (6-DOF) model of the re-entry capsule. Also, the results of the proposed control algorithm will be compared with the Backstepping dynamic inversion (B. D) control method

This paper discusses the flight control strategy based on Adaptive dynamic inversion (ADI) with two-time-scale separation for airplane. Because of nonlinear behavior of flight dynamics, the flight control problem is a rather important and complex issue. In addition to traditional methods, e.g., classic controller, dynamic inversion methods, fuzzy and neural networks have been used. After reviewing these methods, Combination Adaptive dynamic inversion method is designed. In this structure, the control system attempts to track the angle of attack, the sideslip angle and the roll angle in wind frame (m, a, b). Six degree-of-freedom nonlinear equations of motion are considered for tracking the input commands. dynamic inversion technique needs an exact and accurate flight dynamics, and to avoid this problem, we use an adaptive controller. Two-timescale based system dynamics are divided into slow outer variables and fast inner variables. Adaptive controller and dynamic inversion algorithm are used in inner and outer loops, respectively. The performance of the proposed method compares with Full dynamic inversion (DI) method. Simulation results demonstrated the efficacy of Adaptive dynamic inversion controller.

Today, Ducted Fan micro aerial vehicle has attracted much attention in the field of business and research due to the duct and, thus, the ability to be safe in enclosed environments. In order to real identify and practical help to control and implement the vehicle in various maneuvers, the experimental example of this VTOL MAV was built by Aerospace Engineering Department of Amirkabir University of Technology. In this research, in the first step, the modeling of the ducted fan is considered. In this way, after obtaining the dynamic model of the ducted fan, the parameters in this model are calculated, using empirical methods. In this regard, the aerodynamic coefficients of the control levels and the inertia of the ducted fan can be mentioned. In the second step, the controller design of the ducted fan is discussed. Ducted-Fan MAV control is one of the important issues in designing this fan due to inherent instability. The study of vehicle that reported shows that nonlinear dynamic inversion is an appropriate choice among control methods due to its successful empirical implementation on ducted fan. Thus, by choosing this method, the control system was designed to follow the desired command of the vehicle in the Simulink simulation environment. In this process, the position command is first applied to the ducted fan and converted by the controller to the command of state control actuators, after which these commands by changing the angles of the control levels of the ducted fan lead to the change in the angles of the ducted fan’ s side, the pitch, and the roll, and, thus, achieved a desired position. The results indicated that the desired command was correctly followed; also, the stability of the closed loop system was successfully accomplished by using dynamic inversion method for the Ducted Fan MAV.

In this paper, a continuous globally stable tracking control algorithm is proposed for spacecraft in the presence of unknown actuator failure. The design method is based on nonlinear dynamic inversion and in contrast to traditional fault-tolerant control methods, the proposed controller does not require knowledge of the actuator faults and is implemented without explicit fault detection and isolation processes. The stability proof is based on a Lyapunov analysis and the properties of the singularity free quaternion representation of spacecraft dynamics. Results of numerical simulations state that the proposed controller is successful in achieving high attitude performance in the presence of external disturbances and actuator failures.

In current study, an integrated guidance and control system is assessed for a projectile having dual spin motion in the cascade control structure. In this way, a nonlinear seven degrees of freedom model is used and coupling effects of pitch and yaw channels is taken into consideration. Based on cascaded control structure, a three-loops controller is designed, which in inner and middle loops the dynamic inversion method is used, and in the outer loop, an adaptive model based on PID controller is designed. The dynamic pressure contribution in acceleration command is also taken into account. The performance of the designed guidance and control system is assessed in presence of uncertainties via Monte Carlo statistical simulations. The results of suggested algorithm are compared with that of a cascade control having a classic PID controller in its outer loop. The results show the superior performance of the proposed algorithm in achieving the target and control effort.

The theoretical development of the direct adaptive tracking control architecture using neural networks is presented. Emphasis is placed on utilization of neural networks in a flight control architecture based on feedback linearization of the aircraft dynamics. Neural networks are used to represent the nonlinear inverse transformation needed for feedback linearization. Neural networks capable of on-line learning are required to compensate for inversion error, which may arise from imperfect modeling, approximate inversion, or sudden change in aircraft dynamics. A stable weights adjustment rule for on-line neural network is training derived.Under mild assumptions on the nonlinearities representing the inversion error, the adaptation algorithm ensures that all of the signals in the loop are uniformly bounded and that the weights of the on-line neural network parameters tend to constant values. The main objective of the control design is to demonstrate adaptation to aerodynamic uncertainty in the form of both unmodeled parameter variations and unmodeled dynamics not present in the nominal inverting control design. Finally, nonlinear six-degree-of-freedom simulation results for an F-18 aircraft model are presented to demonstrate the effectiveness of the proposed control law.

Nonlinear dynamic inversion is one of the well-known methods in the field of dynamic flight control, which its development refers to the Incremental Nonlinear dynamic inversion (INDI). Based on this, in this paper, the non-linear slow and fast modes of the aircraft is separated into two parts. Then, a distinct control is designed for each part. In the outer and inner loops, the PID and the sliding mode controller are designed, respectively. In addition stability analysis, the simulation results for Boeing 747 are presented and compared with the reference model modes and the conventional INDI. Nonlinear dynamic inversion is one of the well-known methods in the field of dynamic flight control, which its development refers to the Incremental Nonlinear dynamic inversion (INDI). Based on this, in this paper, the non-linear slow and fast modes of the aircraft is separated into two parts. Then, a distinct control is designed for each part. In the outer and inner loops, the PID and the sliding mode controller are designed, respectively. In addition stability analysis, the simulation results for Boeing 747 are presented and compared with the reference model modes and the conventional INDI.

The conventional velocity analysis sums the amplitudes of events along hyperbolic trajectories and converges the energy in the corresponding intercept time and slowness or velocity. This makes the velocity analysis as one of the most time consuming seismic data processing steps. On the other hand, this algorithm suffers from low resolution due to several reasons. In this paper, we use the Butterfly algorithm to calculate the forward and adjoint operators of the hyperbolic Radon transform in a much faster way, compared to the conventional integration in the time domain. Moreover, by applying it to fast iterative shrinkage-thresholding algorithm (FISTA), a high-resolution velocity panel is obtained.

Applying 2D algorithms for inverting the potential field data is more useful and efficient than their 3D counterparts, whenever the geologic situation permits. This is because the computation time is less and modeling the subsurface is easier. In this paper we present a 2D inversion algorithm for interpreting gravity data by employing a set of constraints including minimum distance, smoothness, and compactness. Using different combination of these constraints provide either smooth images of the underground geological structures or models with sharp geological boundaries. We model the study area by a large number of infinitely long horizontal prisms with square cross-sections and unknown densities. The final density distribution is obtained by minimizing an objective function that is composed of the model objective function and equality constraints, which are combined using a Lagrangian multipliers. Each block's weight depends on depth, a priori information on density and the allowed density ranges for the specified area. A MATLAB code has been developed and tested on a synthetic model consists of vertical and dipping dikes. The algorithm is applied with different combinations of constraints and the practical aspects are discussed. Results indicate that when a combination of constraints is used, the geometry and density distribution of both structures can be reconstructed. The method is applied on Zereshlu Mining Camp in Zanjan - Iran, which is well known for the Manganese ores. Result represents a high density distribution with the horizontal extension of about 30 m, and the vertical extension shows a trend in the E - W direction with a depth interval between 7 to 22 m in the east and 15 to 35 m in the west.