## 1 Introduction
From the point of view of these methods, the literature reveals the existence of two categories: (1) static methods, which offer a deterministic approach in solving the attitude determination problem, being independent of time (it is considered that the measurements are made at the same time or at close time moments, between which the movement of the satellite can be neglected or can be easily compensated); and (2) dynamic methods, which are time-dependent, so they take into account the movement of the satellite, and offer a stochastic attitude estimation perspective, by accepting random noise as a disturbance to the measurement process, which implies the use of statistical filtering methods within the algorithms of data processing (for example, various variants of Kalman filtering) [34, 36]. Among the static attitude methods, the following can be mentioned: the Triaxial Attitude Determination (TRIAD) method, [PERSON]'s problem, [PERSON]'s q-method, the Quaternion (QUEST) estimator method, and the Optimal Linear Attitude Estimator (OLAE) method [36, 37]. Also, the studies exposed in the literature provide as dynamic attitude estimation methods the next ones: Kalman filter (KF), linearized Kalman filter (LKF), extended Kalman filter (EKF), minimal representation EKF, multiplicative EKF, additive EKF, backwards-smoothing EKF, deterministic EKF-like estimator, filter QUEST, extended QUEST, two-step attitude estimator, unscented filtering, particle filters, orthogonal attitude filter, predictive filtering, nonlinear observers, adaptive methods [36, 38]. The current attitude solution determined/estimated by the detection system of the ADCS, based on the previously exposed methods, is further delivered to the control system of the ADCS, which, also based on the desired attitude information, controls the actuation system by using some control algorithms until the satellite will be oriented in the right angular position. The literature shows that this segment of the ADCS for small satellites is in continuous progress, and a lot of control algorithms are being developed, including ones based on linear control theory, but most of them are based on nonlinear control theory, such as sliding mode control [39, 40, 41], adaptive control [42, 43, 44], robust control [45, 46], and other nonlinear control methods [47, 48]. Also, fuzzy theory and neural network-based control algorithms are used and presented in the literature [49, 42, 50, 51]. However, if we look at things from a practical side, all these control algorithms are designed based on the requirements imposed by particular attitude maneuvers of the satellites, which generated what was called attitude control modes: (1) detumbling control (after the separation from the launch vehicle); (2) gravity gradient control; (3) spin-stabilization control; and (4) pointing control (3-axis control: inertial, nadir, target, velocity direction, etc.) [34; 35]. Therefore, all control algorithms come bundled with the type of actuation system used to control the attitude of the satellite: propulsive actuators (thrusters, solar sail), environment interacting actuators (gravity gradient actuators, magnetic torquers, moving mass control (MMC)) and angular momentum exchange actuators (reaction wheels, momentum wheels, control moment gyroscopes). Moreover, because of the various disadvantages of the used actuators, the single-actuator attitude control system configurations have their own limitations, and, as in the multi-sensor based attitude determination methods, have conceived and implemented multi-actuator attitude control systems for satellites, which use multi-actuators to ensure complementarity and redundancy [20]. The miniaturization technologies, like MEMS (micro-electro-mechanical systems) or NEMS (nano-electro-mechanical systems), are more and more applied in inertial sensors design and fabrication [52].