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The paper revisits a Lyapunov-based feedback control to implement spacecraft orbital transfer maneuvers. The spacecraft equations of motion in the form of Gauss Variational Equations (GVEs) are used. By shaping the Lyapunov function using barrier functions, we demonstrate that state and control constraints during orbital maneuvers can be enforced. Simulation results from orbital maneuvering scenarios are reported. The synergistic use of the reference governor in conjunction with the barrier functions is proposed to ensure convergence to the target orbit (liveness) while satisfying the imposed constraints., Comment: This is the author's original manuscript (pre-print) of the paper AAS 23-292 presented at 33rd AAS/AIAA Space Flight Mechanics Meeting, Austin, Texas, January 15-19 2023, https://www.space-flight.org/docs/2023_winter/33rdSFFM-Full_Program_Abstracts.pdf

When robots are able to see and respond to their surroundings, a whole new world of possibilities opens up. To bring these possibilities to life, the robotics industry is increasingly adopting camera-based vision systems, especially when a robotic system needs to interact with a dynamic environment or moving target. However, this kind of vision system is known to have low data transmission rates, packet loss during communication and noisy measurements as major disadvantages. These problems can perturb the control performance and the quality of the robot-environment interaction. To improve the quality of visual information, in this paper, we propose to model the dynamics of the motion of a target object and use this model to implement an Extended Kalman Filter based on Intermittent Observations of the vision system. The effectiveness of the proposed approach was tested through experiments with a robotic arm, a camera device in an eye-to-hand configuration, and an oscillating suspended block as a target to follow., Comment: 6pages, 11 figures

The paper considers the application of feedback control to orbital transfer maneuvers subject to constraints on the spacecraft thrust and on avoiding the collision with the primary body. Incremental reference governor (IRG) strategies are developed to complement the nominal Lyapunov controller, derived based on Gauss Variational Equations, and enforce the constraints. Simulation results are reported that demonstrate the successful constrained orbital transfer maneuvers with the proposed approach. A Lyapunov function based IRG and a prediction-based IRG are compared. While both implementations successfully enforce the constraints, a prediction-based IRG is shown to result in faster maneuvers., Comment: 17 pages, 14 figures, submitted to Advanced Control for Applications

Reference and command governors are add-on schemes that augment nominal closed-loop systems with the capability to enforce state and control constraints. They do this by monitoring and modifying, when necessary, the reference command. Existing command governors do this by solving at each sampling time a quadratic programming problem to find a modified reference closest to the original command such that the current state and the modified reference pair are constraint admissible. In this paper, we show that a simple modification of the basic command governor enables it to operate with inexact optimization and even without requiring invariance of the constraint admissible set. Thus this modification significantly extends the applicability of the reference and command governors to practical problems where finding invariant sets may be problematic and where exact optimization may not be feasible due to reliability of the optimizers or limited computing power. Numerical examples are reported which illustrate the approach.

In this paper, we address a general eigenstructure assignment problem where the objective is to distribute the closed-loop modes over the components of the system outputs in such a way that, if a certain mode appears in a given output, it is unobservable from any of the other output components. By linking classical geometric control results with the theory of combinatorics, we provide necessary and sufficient conditions for the solvability of this problem, herein referred to as state-to-output decoupling, under very mild assumptions. We propose solvability conditions expressed in terms of the dimensions of suitably defined controlled invariant subspaces of the system. In this way, the solvability of the problem can be evaluated a priori, in the sense that it is given in terms of the problem/system data. Finally, it is worth mentioning that the proposed approach is constructive, so that when a controller that solves the problem indeed exists, it can be readily computed by using the machinery developed in this paper., Comment: 47 pages

Wireless Sensor Networks (WSNs) enable a wealth of new applications where remote estimation is essential. Individual sensors simultaneously sense a dynamic process and transmit measured information over a shared channel to a central fusion center. The fusion center computes an estimate of the process state by means of a Kalman filter. In this paper we assume that the WSN admits a tree topology with fusion center at the root. At each time step only a subset of sensors can be selected to transmit observations to the fusion center due to a limited energy budget. We propose a stochastic sensor selection algorithm that randomly selects a subset of sensors according to certain probability distribution, which is opportunely designed to minimize the asymptotic expected estimation error covariance matrix. We show that the optimal stochastic sensor selection problem can be relaxed into a convex optimization problem and thus solved efficiently. We also provide a possible implementation of our algorithm which does not introduce any communication overhead. The paper ends with some numerical examples that show the effectiveness of the proposed approach., Comment: 7 pages, 2 figures, complete version of a technical note for the IEEE transactions on automatic control