Navigation, for many years, has been one of the issues that preoccupied the human mind. The compass was the first instrument built for navigation by the Chinese. The compass determined geographical north instead of a magnetic one [1]. Gradually over time, humans found that it was possible to make orientations by celestial bodies. However, positioning was still impossible. In 1757, the first sextant able to measure angles of celestial bodies and the Earth's horizon was invented by John Bird [1], [2], [3]. Also, the first star catalog containing 992 stars was obtained by Hip-parchus in 1437. The star catalog and the sextant were used for simple navigation [4]. More elaborate star catalogs were developed by advanced sky observation devices, such as SKY2000 with 299,460 stars, introduced in 2006 [5].

In this paper, a new algorithm called blind star identification is presented to identify the stars in night sky images without using the intrinsic parameters of the star sensor camera (focal length, principal point, and pixel size), and in lost in space mode. The accuracy and reliability of the proposed algorithm were successfully validated by using the real night sky images and Monte Carlo simulations. Accordingly, the proposed algorithm was successfully able to identify in more than 90% of the images containing more than five stars and no wrong identification was observed in the Monte Carlo simulations. On the other hand, the rotation around the optical axis, which cannot be estimated using vector observations, should be minimized in the process of designing and manufacturing the star sensor and carefully measured in ground calibration, like as the aberration and lens distortion. Ultimately, another advantage of this algorithm is the simultaneous use of planar and interstellar angles. This advantage leads to data redundancy and greater reliability of the algorithm so that the performance of the algorithm is guaranteed under severe error conditions.

In this research, time-optimal open-loop and closed-loop guidance laws for MAVs, such as Samarai, were investigated. Open-loop optimal solutions were achieved with a new technique,based on using mathematical methods, such as calculus of variation and the GA-PSO optimization algorithm. This novel method could overcome difficulties of the usual optimal control methods. The TS approach developed closed-loop guidance that dealt with the novel neural-fuzzy training algorithm based on open-loop optimal trajectories to achieve closed-loop guidance. By feed forwarding the optimal control as a nominal optimum trajectory plus deviation, closed-loop optimal control can be generated. On the other hand, (TS) fuzzy logic needs storing nominal optimal trajectories to train the system. As discussed, the responses of the optimal closed-loop guidance were achieved, and the robustness of closed-loop policies against noises and/or disturbances was studied. However, it was difficult to obtain closed-loop solutions when the other methods, such as dynamic programming or analytical methods, were considered. The case mentioned is an important innovative idea for autonomous MAVs, whose results are important for further developments of Samarai monocopter. The results of this work demonstrated that the closed-loop optimal responses based on TS fuzzy logic methods can damp disturbances for minimum time criterion. However, the result of fuzzy logic is perfectly suitable, where higher noises were applied. Therefore, it can be concluded that fuzzy logic was robust and efficient in eliminating perturbations. It is proposed that aerospace engineers, such as primary trajectory designers, use TS fuzzy training guidance because its robustness was guaranteed by this work, especially for MAVs, such as Samarai.

Purpose: The purpose of the paper is to design a new attitude stabilization system for a microsatellite based on single gimbal control moment gyro (SGCMG) in which the gimbal rates are selected as controller parameters.Design/methodology/approach: In the stability mode, linear quadratic regulator (LQR) and linear quadratic Gaussian (LQG) control strategies are presented with the gimbal rates as a controller parameters. Instead of developing a control torque to solve the attitude problem, the attitude controller is developed in terms of the control moment gyroscope gimbal angular velocities. Attitude control torques are generated by means of a four SGCMG pyramid cluster.Findings: Numerical simulation results are provided to show the efficiency of the proposed controllers. Simulation results show that this method could stabilize satellite from initial condition with large angles and with more accuracy in comparison with feedback quaternion and proportional-integral-derivative controllers. These results show the effect of filtering the noisy signal in the LQG controller. LQG in comparison to LQR is more realistic.Practical implications: The LQR method is more appropriate for the systems that have project models reasonably exact and ideal sensors/actuators. LQG is more realistic, and it can be used when not all of the states are available or when the system presents noises. LQR/LQG controller can be used in the stabilization mode of satellite attitude control.Originality/value: The originality of this paper is designing a new attitude stabilization system for an agile microsatellite using LQR and LQG controllers.