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This paper introduces Disturbance-Aware Redundant Control (DARC), a control framework addressing the challenge of human–robot co-transportation under disturbances. Our method integrates a disturbance-aware Model Predictive Control (MPC) framework with a proactive pose optimization mechanism. The robotic system, comprising a mobile base and a manipulator arm, compensates for uncertain human behaviors and internal actuation noise through a two-step iterative process. At each planning horizon, a candidate set of feasible joint configurations is generated using a Conditional Variational Autoencoder (CVAE). From this set, one configuration is selected by minimizing an estimated control cost computed via a disturbance-aware Discrete Algebraic Riccati Equation (DARE), which also provides the optimal control inputs for both the mobile base and the manipulator arm. We derive the disturbance-aware DARE and validate DARC with simulated experiments with a Fetch robot. Evaluations across various trajectories and disturbance levels demonstrate that our proposed DARC framework outperforms baseline algorithms that lack disturbance modeling, pose optimization, or both. 

This paper studies the data-driven reconstruction of firing rate dynamics of brain activity described by linear-threshold network models. Identifying the system parameters directly leads to a large number of variables and a highly non-convex objective function. Instead, our approach introduces a novel reformulation that incorporates biological organizational features and turns the identification problem into a scalar variable optimization of a discontinuous, non-convex objective function. We prove that the minimizer of the objective function is unique and establish that the solution of the optimization problem leads to the identification of all the desired system parameters. These results are the basis to introduce an algorithm to find the optimizer by searching the different regions corresponding to the domain of definition of the objective function. To deal with measurement noise in sampled data, we propose a modification of the original algorithm whose identification error is linearly bounded by the magnitude of the measurement noise. We demonstrate the effectiveness of the proposed algorithms through simulations on synthetic and experimental data.