Konstantin Avenirovich Neusypin

А fault-tolerant control algorithm of the spacecraft attitude, which consists of a learning observer and a regulator with a predetermined time, is developed. The learning observer is capable of quickly and accurately estimate the total disturbance, including external influences, model parametric uncertainty and partial actuator failures. Based on the assessment of the total disturbance, the regulator with a predetermined time ensures task completion within the given time. The stability of the closed-loop system using this algorithm is analyzed by using the Lyapunov function. The algorithm effectiveness is confirmed by numerical indicators.

Correction algorithms for an unmanned aerial vehicle's navigation system, based on an adaptive Kalman filter and a self-organizing algorithm are considered. The navigation system consists of an inertial navigation system (INS) and a satellite navigation system (GNSS). The accuracy of the navigation system is improved algorithmically in two modes. In the stable operation mode of the satellite system, an adaptive Kalman filter in scalar form is used. In the absence of signals from the satellite system, a linear predictive trend modified by nonlinear self-organizing terms is applied.

This study investigates the concept of the adaptability degree of state variables in mathematical models used in estimation algorithms. Two numerical criteria are proposed for calculating the adaptability degree of state variables in linear models. Qualitative characteristics of the adaptability of inertial navigation system errors are studied under conditions of varying sampling periods and flight altitudes of an atmospheric aircraft. The developed methods allow for the creation of models for estimation algorithms with desired adaptability properties.

Learning from demonstration with multiple executions must contend with time warping, sensor noise, and alternating quasi stationary and transition phases. We propose a la-bel free pipeline that couples unsupervised segmentation, duration explicit alignment, and probabilistic encoding. A dimensionless multi feature saliency (velocity, acceleration, curvature, direction change rate) yields scale robust keyframes via persistent peak–valley pairs and non maximum suppression. A hidden semi Markov model (HSMM) with ex-plicit duration distributions is jointly trained across demonstrations to align trajectories on a shared semantic time base. Segment level probabilistic motion models (GMM/GMR or ProMP, optionally combined with DMP) produce mean trajectories with calibrated co-variances, directly interfacing with constrained planners. Feature weights are tuned without labels by minimizing cross demonstration structural dispersion on the simplex via CMA ES. Across UAV flight, autonomous driving, and robotic manipulation, the method reduces phase boundary dispersion by 31% on UAV Sim and by 30–36% under monotone time warps, noise, and missing data (vs. HMM); improves the sparsity–fidelity trade off (higher time compression at comparable reconstruction error) with lower jerk; and attains nominal 2σ coverage (94–96%), indicating well calibrated uncertainty. Abla-tions attribute the gains to persistence plus NMS, weight self calibration, and dura-tion explicit alignment. The framework is scale aware and computationally practical, and its uncertainty outputs feed directly into MPC/OMPL for risk aware execution.

Statistical Learning of Robotic Demo Trajectories Based on Multicriteria Segmentation and Multi-Demonstration Alignment (HSMM) addresses complex tasks in human-robot interaction and intelligent manufacturing. The research goal of this study is to automatically extract generalized key segments from multiple robotic demonstration trajectories in the absence of prior annotations and establish statistical and parametric models for universal trajectory reproduction across diverse tasks and conditions. To achieve this, the research tasks include multicriteria segmentation (speed, curvature, acceleration, direction change), trajectory alignment using Hidden Semi-Markov Models (HSMM), and subsequent implementation of statistical representations (ProMP, GMM/GMR, DMP). The proposed methodology begins with the smoothing of raw data and the identification of key points via topological simplification and non-maximum suppression, then, using HSMM, it ensures consistent segmentation of multiple demonstrations into characteristic segments. The conducted experiments confirm the results of the approach, demonstrating low reconstruction error while simultaneously improving data compression and preserving key actions, indicating the high efficiency of the method. Finally, the novelty and practical significance of this study can be highlighted by the potential industrial applications (such as welding, painting, etc.), as well as the future prospective expansions of the method to more dynamic and non-stationary scenarios, requiring adaptive and statistically grounded trajectory planning.

In modern transportation systems, the accuracy of micro-electro-mechanical system (MEMS) gyros is critical for various automotive and robotic applications. However, MEMS gyro bias exhibits time-varying multiphysics field coupling characteristics, presenting challenges for online modeling and correction. In response, we propose a candidate subfield aggregation shallow network (CSASN)-based method for gyro bias online modeling, departing from existing complex neural network methods. Our CSASN-based method is distinguished by reconstruction of gyro bias estimation and careful selection of effect aggregation factors (AFs). The gyro bias estimation is reconstructed through attitude estimation from an integrated navigation system when global navigation satellite system (GNSS) signal is available. The AFs of CSASN include time, temperature, angular rate, and acceleration, ensuring the model reflects the time-varying multiphysics field coupling characteristics of MEMS gyro bias. The MEMS gyro output is corrected by the model predicted bias for real-time attitude calculation in scenarios where GNSS is inaccessible. Field experimental results with a ground vehicle demonstrate the feasibility and effectiveness of the proposed online gyro bias modeling method.

The nonlinear model of a spacecraft requires high robustness, and the classical control method, as one of the earliest developed control methods, has simple algorithms and high robustness. A control system structure that ensures retention attack angle and overload by combining fuzzy control with classical control for adaptive optimization and adjustment of control parameters and tracking of the attack angle step signals is proposed. The simulation results show that the performance of the improved system is better than that of the original system. Improved algorithms are used to simulate the attack angle and overload retention sections, also simulated aerodynamic uncertainties is carried out. The results show that the developed control law meets the requirements of a certain level of robustness. Keywords aerospace vehicles, position control, adaptive fuzzy control, PID adjusting, uncertainty

In robotic applications, the dynamics sensitivities of commercial-grade micro-electro-mechanical system (MEMS) gyros often exhibit uncertainties that cannot be accurately modeled by linear drift. To address the estimation of these uncertain biases, we propose a novel nonlinear robust bias observer (NRBO) in this article. Unlike existing nonlinear observers for attitude and gyro bias, our proposed method incorporates a dynamics-sensitive gyro bias estimation approach, achieved through the synthesis of the attitude-angular rate nonlinear dynamic coupling (AARNDC) term and the attitude-linear coupling (ALC) term. We highlight the potential advantages of our proposed method, including the asymptotic stability of the NRBO and its robustness against MEMS gyro bias instability, enabled by a rational design of the AARNDC and ALC terms. In addition to gyro bias estimation, we present the attitude estimation within the NRBO framework. Field experimental results, conducted with a cable-driven parallel robot, demonstrate the robustness of the proposed NRBO against bias instability measurement noise. Moreover, the results highlight its superior accuracy when compared with the invariant extended Kalman filter and nonlinear navigation observer methods.

In this paper the problem of atmospheric disturbances during the UAV carrier landing operation is considered. A UAV dynamics model, and a wind gust and airwake disturbance model are introduced. A LADRC-based cascade control scheme is developed for fixed-wing UAVs. In the control scheme, three ADRC controllers are designed for attitude control, and another two ADRC controllers are designed for course and altitude tracking. Finally, a series of simulations are implemented in Simulink and the results are presented to demonstrate the performance of the proposed control scheme.