Onboard control, tracking and navigation for autonomous systems

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To operate effectively and safely, autonomous systems must be able to navigate complex environments, precisely control their attitude, accurately estimate their own states and make real-time decisions. A new adaptive controller is designed for attitude tracking control of rigid spacecraft with inertia uncertainties and full state feedback of attitude and angular rate. The controller preserves the proportional-derivative plus feedforward (PD+) structure but introduces time varying feedback gains, wherein the desired attitude state is represented by quaternions. Stable asymptotic tracking of the desired reference trajectories is guaranteed without any further restrictions upon the initial conditions, reference trajectories or any requirement for a priori availability of bounds upon the inertia matrix. The onboard estimation of the angular velocity of a spacecraft using Rate-integrating Gyroscopes (RIGs) is considered next. RIGs provide measurements of angular displacement which need to a low pass filter or observer to obtain angular velocity of the system. A continuous time observer is proposed which estimates angular velocity using the RIG measurements and achieves exponential convergence, while asymptotic convergence is guaranteed for the adaptive inertia observer. Unlike conventional certainty equivalence methods, a novel adaptation update law is proposed with additional control knobs changing with the attitude states. The final part of the dissertation deals with the Simultaneous Localization and Mapping (SLAM) problem. Estimating the location of a robot along with the position of the surrounding features on a map can be done onboard recursively with a simple Extended Kalman Filter (EKF-SLAM), but has higher chances of divergence and inconsistency. The robocentric SLAM method transforms the map of features to a local reference frame centered at the robot's position, leading to reduced inconsistency due to lower linearization errors in the update function. Improvements to the robocentric SLAM methods are suggested in the form of addition of second order terms to the linear propagation step and elimination of the composition step by transforming the feature maps before every update step. These modifications provide better filter consistency and prevent divergence in cases which were previously not possible.


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