A dynamical systems approach to micro spacecraft autonomy

The drive toward reducing the size and mass of spacecraft has put new constraints on the computational resources available for control and decision making algorithms. The aim of this paper is to present alternative methods for decision making algorithms that can be introduced for micro-spacecraft. The motivation behind this work comes from dynamical systems theory. Systems of differential equations can be built to define behaviors which can be manipulated to define an action selection algorithm. These algorithms can be mathematically validated and shown to be computationally efficient, providing robust autonomous control with a modest computational overhead

Adaptive, locally-linear models of complex dynamics

The dynamics of complex systems generally include high-dimensional, non-stationary and non-linear behavior, all of which pose fundamental challenges to quantitative understanding. To address these difficulties we detail a new approach based on local linear models within windows determined adaptively from the data. While the dynamics within each window are simple, consisting of exponential decay, growth and oscillations, the collection of local parameters across all windows provides a principled characterization of the full time series. To explore the resulting model space, we develop a novel likelihood-based hierarchical clustering and we examine the eigenvalues of the linear dynamics. We demonstrate our analysis with the Lorenz system undergoing stable spiral dynamics and in the standard chaotic regime. Applied to the posture dynamics of the nematode $C. elegans$ our approach identifies fine-grained behavioral states and model dynamics which fluctuate close to an instability boundary, and we detail a bifurcation in a transition from forward to backward crawling. Finally, we analyze whole-brain imaging in $C. elegans$ and show that the stability of global brain states changes with oxygen concentration.Comment: 25 pages, 16 figure

Economic MPC of Nonlinear Processes via Recurrent Neural Networks Using Structural Process Knowledge

This work discusses three methods that incorporate a priori process knowledge into recurrent neural network (RNN) modeling of nonlinear processes to get increased prediction accuracy and provide information on how the neural network models are structured. The first method proposes a hybrid model that integrates first-principles models and RNN models together. The second method proposes a partially-connected RNN model which its structure is based on a priori structural process knowledge. The third method proposes a weight-constrained RNN model that integrates weight constraints into the training of the RNN model. The proposed RNN models are used in an economic model predictive control system and then applied to a chemical process example to validate the improved approximation performance compared to a fully-connected RNN model that is treated as a black box model