In this paper, we present the development of a non-linear model predictive controller for the trajectory tracking of a quadrotor using the CasADi optimization framework. The non-linear dynamic model of the quadrotor was derived using Newton–Euler equations, and the control algorithm and drone dynamics were wrapped in Matlab. The proposed controller was tested by simulating the tracking of a 3D helical reference trajectory, and its efficiency was evaluated in terms of numerical performance and tracking accuracy. The results showed that the proposed controller leads to faster computational times, approximately 20 times faster than the Matlab toolbox (nlmpc), and provides better tracking accuracy than both the Matlab toolbox and classical PID controller. The robustness of the proposed control algorithm was also tested and verified under model uncertainties and external disturbances, demonstrating its ability to effectively eliminate tracking errors.

This paper presents new methods for spacecraft relative pose estimation using the Unscented Kalman Filter (UKF), taking into account non-additive process and measurement noises. A twistor model is employed to represent the spacecraft’s relative 6-DOF motion of the chaser with respect to the target, expressed in the chaser body frame. The twistor model utilizes Modified Rodrigues Parameters (MRPs) to represent attitude with a minimal number of parameters, eliminating the need for the normalization constraint that exists in the quaternion-based model. Additionally, it incorporates both relative position and attitude in a single model, addressing kinematic coupling of states and simplifying the estimator design. Despite numerous existing pose estimation algorithms, many rely on the simplification of additive noise assumptions. This work enhances the robustness and improves the convergence of non-additive noise algorithms by deriving two methods to accurately approximate process and measurement noise covariance matrices for systems with non-additive noises. The first method utilizes the Stirling Interpolation Formula (SIF) to obtain equivalent process and measurement noise covariance matrices. The second method employs State Noise Compensation (SNC) to derive the equivalent process noise covariance matrix and uses SIF to compute the equivalent measurement noise covariance matrix. These methods are integrated into the UKF framework for estimating the relative pose of spacecraft in proximity operations, demonstrated through two scenarios: one with a cooperative target using Position Sensing Diodes (PSDs) and another with an uncooperative target using LiDAR for 3-D imaging. The effectiveness of these methods is validated against others in the literature through Monte Carlo simulations, showcasing their faster convergence and robust performance.

This paper presents design, implementation and testing of attitude determination and control subsystem (ADCS) in CubeSat. The procedures of designing the subsystem are discussed. They include mission requirements and specifications, sizing, components selection, software and hardware design. The proposed subsystem is experimentally tested by using low-cost experimental setup. This setup includes an air bearing table that allows 1-DOF friction-less rotation of the CubeSat. The Detumbling and the sun-pointing modes are experimentally demonstrated by using this setup.