Abstract Essential tremor (ET) is a neurological disorder that causes involuntary rhythmic limb movements, which negatively impact quality of life. ET’s severity is often quantified by The Essential Tremor Rating Assessment Scale (TETRAS). However, this clinical scale suffers from inter- and intrarater error, and still requires expert clinical evaluation. Toward the development of an assessment available outside of the clinic, a long short-term memory (LSTM) network was used to predict ET severity with inertial measurement unit (IMU) data collected from 12 participants with ET rated on TETRAS. LSTM hyperparameters were tuned using a Bayesian optimization algorithm (Optuna library). The LSTM was trained on the normalized data and labels to minimize root mean square error (RMSE) and evaluated using a leave-one-out cross-validation (LOOCV). Across the 12 iterations of the LOOCV, the model achieved an RMSE of 6.72 ± 3.52. The model performs more consistently than naive median guessing but suffers from greater variance; however, the model’s performance improvement over naive median guessing was not statistically significant. Overall, the model’s performance falls close to the 10% interrater error seen in TETRAS. However, additional participants representing a larger range of the TETRAS scale could improve the prediction performance. In summary, this paper presents the first use of TETRAS to train an LSTM to predict ET severity, and motivates the acquisition of large datasets and development of this approach into a flexible app-based severity assessment, which can be administered outside of the clinic.

Abstract In this paper, we address the stabilization problem for force-controlled nonholonomic mobile robots under safety-critical constraints. We propose a continuous, time-invariant control law based on the γm-quadratic programming (γm-QP) framework, which unifies control Lyapunov functions (CLFs) and control barrier functions (CBFs) to enforce both stability and safety in the closed-loop system. For the first time, we construct a global, time-invariant, strict Lyapunov function for the closed-loop nonholonomic mobile robot full-dynamic system with a nominal stabilization controller in polar coordinates; this strict Lyapunov function then serves as the CLF in the QP design. Next, by exploiting the inherent cascaded structure of the vehicle dynamics, we develop a CBF for the mobile robot via an integrator backstepping procedure. Our main results guarantee both asymptotic stability and safety for the closed-loop system. Both the simulation and experimental results are presented to illustrate the effectiveness and performance of our approach.

Abstract This article bridges optimization and control and presents a novel closed-loop control framework based on natural gradient descent, offering a trajectory-oriented alternative to traditional cost-function tuning. We leverage the Fisher information matrix to formulate a preconditioned gradient descent update that offers flexibility in shaping closed-loop behavior. To this end, the proposed method parameterizes closed-loop dynamics in terms of stationary covariance and an unknown cost function, providing a geometric interpretation of control adjustments. We establish theoretical stability conditions. The simulation results on a rotary inverted pendulum benchmark highlight the advantages of natural gradient descent in trajectory shaping.

Abstract We address the challenge of temporal change detection and estimation in nonlinear dynamical systems. Traditionally, model-based fault diagnosis methods rely on the parallel execution of multiple filters, each corresponding to different system models (nominal and faulty). Although effective, this approach becomes computationally expensive, especially when using numerical techniques such as the particle filter instead of closed form, Gaussian filters. In this work, we propose a novel fault diagnosis framework that integrates a Bernoulli state variable, in the form of a jump hidden Markov model, governed by a task-specific probabilistic mode switching mechanism, to represent the presence or absence of a fault (or change) within a dynamic system. By embedding this binary state within a Bayesian filtering framework, we develop a hybrid state filter that significantly reduces the computational complexity traditionally associated with particle filtering approaches for fault diagnosis. The proposed filter enables real-time evaluation of fault hypotheses based on the statistical moments of a single-posterior probability density function, thereby eliminating the requirement for maintaining multiple parallel filters. The proposed method is validated through a numerical benchmark example demonstrating its efficiency and effectiveness.

Abstract There has been an increasing attention toward the research of space manipulator systems for different applications like on-orbit servicing, debris removal, etc. from various institutions across the globe. This resulted in the trend of investigating simple methods to couple various manipulator and base/spacecraft pose control methodologies that already exist independently in the literature. One such scientific work in this direction proposed a control strategy for purely manipulator end-effector trajectory tracking control, while the base/spacecraft is station-kept. This article investigates the generalizability of the control strategy proposed by the scientific work for various space manipulation applications.

Abstract Autonomous manipulation is a challenging problem in field robotics due to uncertainty in object properties, constraints, and coupling phenomenon with robot control systems. Humans learn motion primitives over time to effectively interact with the environment. We postulate that autonomous manipulation can be enabled by basic sets of motion primitives as well, but do not necessitate mimicking human motion primitives. This work presents an approach to generalized optimal motion primitives using physics-informed neural networks. Our simulated and experimental results demonstrate that optimality is notionally maintained where the mean maximum observed final position percent error was 0.564% and the average mean error for all the trajectories was 1.53%. These results indicate that notional generalization is attained using a physics-informed neural network approach that enables near optimal real-time adaptation of primitive motion profiles.

Abstract We introduce a partial differential equation model for rechargeable lithium-metal battery (LMB) cells whose parameter values are fully identifiable from cell-level experiments. From this model, we formulate a computationally tractable transfer function (TF) model for use within optimization loops. A strategy is proposed for regressing the TF model to cell electrochemical impedance spectroscopy (EIS) measurements to estimate parameter values. We validate the regression using a synthetic dataset before application to a single-layer LMB pouch cell. The voltage root-mean-square error (RMSE) between the fully identified model’s predictions and laboratory measurements is about 4 mV for a galvanostatic intermittent titration technique (GITT) profile. We provide MATLAB code to simulate the model in comsol, compute cell impedance from the TF model, and perform model regression.

Abstract This paper presents a formulation and methodologies for a vision-aided inertial navigation system designed for autonomous landing or proximity operation. Measurements from a monocular camera are integrated with inertial measurements to enable sensor fusion, leveraging the strengths of both visual and inertial sensing modalities. A multiplicative extended Kalman filter (MEKF) is used to estimate the relative pose to a target space as well as bias variances. The estimator maintains a multiplicative approach to parameterizing the error quaternion and the attitude kinematics throughout filtering. We adapt the histogram-of-oriented gradients (HOGs) algorithm and apply a series of template images for feature extraction in our vision algorithm, thereby improving its rotation variance property. A measurement model is formulated through an auxiliary optimizing process of geometric data to estimate feature locations with uncertainty. The utility of the proposed navigation system and its framework is demonstrated by experimental analysis using the sensor module and the designated helipad target.

Abstract Potential field-based collision avoidance algorithms for mobile robots frequently assume vehicles and obstacles to have circular or spherical shapes. This assumption not only simplifies the analysis but also limits the mobility of agents in confined spaces, particularly for vehicles with elongated or irregular shapes. To increase mobility, this letter presents a decentralized collision avoidance framework for nonholonomic systems of unicycle type that considers the non-circular shape and relative orientation of vehicles and obstacles. The framework builds on the concepts of potential field and avoidance functions. However, it proposes using a non-constant minimum safe distance radius that changes based on the shape, relative position, and relative orientation of agents. The control framework is proven to guarantee collision avoidance at all times and is shown, via simulation, to increase the ability of agents to navigate through narrow spaces safely.

Abstract This work brings forward several interesting facts about system properties of linear time-invariant discrete-time systems like output controllability, state controllability and state observability, trackability, and input and state observability. Particularly, the work brings about the inter-relationships between these properties while taking a state-space formulation-based approach to come up with simple facts that are backed up by proofs using preliminary linear algebra.