This paper presents an optimized controller around the longitudinal axis of multivariable system in one of the aircraft flight conditions. The controller is introduced in order to control the angle of attack from the pitch attitude angle independently (that is required for designing a set of direct force-modes for the longitudinal axis) based on particle swarm optimization (PSO) algorithm. The autopilot system for military or civil aircraft is an essential component and in this paper، the autopilot system via 6 degree of freedom model for the control and guidance of aircraft in which the autopilot design will perform based on defining the longitudinal and the lateral-directional axes are supposed. The effectiveness of the proposed controller is illustrated by considering HIMAT aircraft. The simulation results verify merits of the proposed controller.

In this paper, the design and simulation of a combined navigation guidance law for two-dimensional ground-to-air missile autopilot is presented. In the proposed guidance law, in the missile guidance phase, the proportional navigation guidance method is used for the active guidance system of the ground-to-air defense missile. Since autopilot must be designed to control the path of the missile, we use the linear two-regulator (LQR) method to design the missile autopilot. The design of the closed-loop system is completed using the guidance and autopilot system, as well as the use of a search engine to estimate the range and speed of the missile approaching the target. The simulation results of the designed system show that using the proposed design for the overall control system, the missile was able to track and hit the target at a constant speed.

The problem of attitude stabilization of a fighter aircraft is investigated in this paper. The practical aspects of a real physical system like existence of external disturbance with unknown upper bound and actuator saturation are considered in the process of controller design of this aircraft. In order to design a robust autopilot in the presence of the actuator saturation, the Composite Nonlinear Feedback (CNF) controller along with the Adaptive Integral Sliding Mode (AISM) controllerand the new robust controller that is called AISM-CNF control law is proposed. The CNF part of controller is used for stabilization of the nominal system and also improvement of the transient performance by considering the actuator saturation. The AISM part guarantees robustness against the model uncertainties and/or external disturbances. Since in the proposed approach, the upper bound of the uncertain terms is estimated and therefore there is no need to the prior knowledge of the upper bound of the model uncertainties. Finally, simulation results show the performance of the proposed AISM-CNF controller in term of attitude stabilization of fighter aircraft, robustness, and the good characteristics of the transient responses of the autopilot system in spite of actuator saturation and external disturbance.

Designing of ship autopilot is a challenging problem because of high nonlinearity of ship dynamics and various acting disturbances. The goal of this research work is to design a robust and efficient sliding mode control for ship autopilot system. The main contribution is to employ a nonlinear dynamic model bound of uncertainties for controller design. In order to reduce the side effects of chattering phenomenon, integral switching variable is used to modify the proposed control technique. Simulation results in the rough wave condition show that the proposed sliding mode control method is able to cope with nonlinearity, uncertainty, and disturbances in the system. Moreover, the controller significantly reduces the chattering in comparison with a previous similar research.

In this paper, a functional model, which is identified based on flight test data sets, is used to improve the performance of guidance loop of a commercial quadrotor equipped with a low-cost autopilot and navigation system. For this purpose, relationships between horizontal velocities and the quadrotor attitudes around the horizontal axes and relationships between three quadrotor attitudes with the correspondent commands sent to the autopilot are identified independently. The first model describes the relation between rotation around the axes of the quadrotor with declared commands in the autopilot system and the other describes the relation between horizontal velocities with the angle around the longitudinal and latitudinal axes of the quadrotor. According to the experimental test results, a first order transfer function has enough accuracy to describe the rotation around the vertical axis with related commands. Also, the description of the relation between the horizontal velocities and the autopilot commands has suitable accuracy using two consequent first order transfer functions; one model between the applied commands and changes of roll and pitch angles, and the other between roll and pitch angles and the horizontal velocities. Due to the linear time invariant characteristics of the system, the least square estimator is used to estimate unknown parameters of the models. To evaluate the identified models, the performance of the path tracking using a PID controller as the outer controller is investigated. The results of the indoor tests show significant improvements in path tracking that confirms enough accuracy of the identified models.

Due to the importance of autopilot systems in Micro Aerial Vehicles (MAVs), in this paper first, parametric guidance and control systems are designed, and then they are implemented on a simulated nonlinear six-DOF MAV. The control system is fuzzy-supervisory which its gains are optimized using genetic algorithm. For designing the guidance system, first, two-dimensional (constant height) path following algorithms of vector field and carrot-chasing are developed to 3D algorithms. Then, an optimized 3D fuzzy carrot-chasing guidance system is presented using a combination of the carrot-chasing geometric algorithm, fuzzy logic, and genetic algorithm. Augmentation of the fuzzy logic to the carrot-chasing algorithm, improves its performance significantly. In any autonomous flight maneuver, guidance and control systems affect the performance of the aircraft, simultaneously. So, using a similar control system, the performance of the 3D carrot-chasing algorithm, 3D vector field method, and the proposed 3D fuzzy carrot chasing algorithms are compared with and without applying the wind external disturbance. Results have shown significant superiority of the proposed 3D fuzzy carrot-chasing approach in the horizontal plane of motion and the 3D vector field method in the vertical plane of motion.

At present, the use of unmanned aerial vehicles (UAVs) has been increased dramatically. The reasons for this development are cheapness, smallness, simplicity, and diversity of missions. The simplicity of guidance and control of multi-rotor drones is that they are equipped with an autopilot system. This system is responsible for flying control. UAVs do not have a high weight and often have three-phase high-speed motors which makes a fast and complex flight dynamics. In this paper, a fuzzy adaptive PID controller is applied to control an UAV for carrying a time-varying cargo. The performance of the flight control system implemented on a quadrotor is evaluated, experimentally. A sandbox is used to model the time-varying mass. The sand passes from the beginning of the fly through some holes of the box, and after about a minute all the sand is poured. At the end of the paper, the practical results are compared with results obtained by fixed-parameter PID controller.

In this paper, a three-dimensional robust guidance law for a surface to air missile considering actuator saturation and first order dynamic for autopilot system has been designed in order to enhance the performance of defense systems. To attain this goal, first, modeling of the system in 3D spherical coordination using engagement basics has been derived and after that, appropriate cost function for collision of missile and target considering actuator constraints and in absence of target maneuver information has been formulated. Hamilton-Jacobi-Isaacs (HJI) differential equation inequality should be solved according to robust control literature for achieving guidance law which unfortunately does not have closed form solution in our case study problem. Therefore, to overcome this challenge, using adaptive dynamic programming theory for solving acquired HJI, algorithm for designing robust guidance law has been presented. Simplification solving of differential inequality and also guaranteeing robustness of the controller are the most important feature of the proposed algorithm. Numerous simulations for targets with different maneuvering capabilities and comparison of the proposed method with conventional augmented proportional navigation, show effectiveness of designed 3D robust guidance law.

The purpose of this article is to maximize the aerodynamic efficiency of an airplane during flight in the vicinity of the sea waves. A whole 3D aircraft flight at the closed proximity to the wavy sea water has been numerically simulated by the Fluent commercial software and the results were used to train a neural network. This network was then employed to determine the maximum aerodynamic efficiency in the vicinity of sea waves. The combination of the computational fluid dynamics and the neural network has approved that the flight must be performed at a certain speed to attain the maximum aerodynamic efficiency with a respect to a varied range of sea waves amplitude. The results can be considered as the inputs for autopilot system to provide the most efficient flight over the sea waves.

Nowadays, the utilization of software engineering in various areas of technology is remarkably increased. As a matter of fact, it is used in many critical applications such as eye surgery, autopilot systems of airplanes, centralized traffic control (CTC), and so on. Therefore, the reliability of software is very important, and it plays an essential role in the lifetime of the software. Software reliability is one of the main characteristics of software quality. Moreover, the rapid assessment of the reliability of the application is essential during the software life cycle. In this paper, Iuse the neuro-fuzzy methods to assess the software's reliability in order to cope with uncertainties in measuring the actual parameters of the software. By designing neuro-fuzzy inference systems and applying four parameters of the ISO/IEC 9126quality model(i.e., the maturity of software, fault-tolerant, recoverability, and reliability compliance) and finding the parameters of a fuzzy system by exploiting approximation techniques from neural networks, Ipresent an integrated assessment model for evaluation of software reliability. The case study used in this paper to evaluate the proposed method is the software income tax calculator. By applying the input parameters, Iobserve that the software reliability is 0.65. software reliability in our proposed method is more exact than software reliability in the fuzzy multi-criteria and fuzzy method because The weights of the input parameters have been set by experts and software developers, and simulations are carried out using MATLAB tool (ANFIS). Simulations confirm that the proposed method provides acceptable results.