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Presents a study which discussed a multiple-model (MM) estimator with a variable structure called likely-model set algorithm applied to hybrid estimation problems. Impact of (M) estimation to adaptive estimation; Conclusions.

Investigates the importance in the application of a variable-structure multiple-model (VSMM) estimator, called model-group switching (MGS) algorithm. Design of model-group switching algorithm; Design of test scenarios between MGS algorithm with the Interacting Multiple-Model estimator; Performance measures of MM estimators.

Presents information on the detection and diagnosis of sensor and actuator failures, using an approach which is based on the interacting multiple-model (IMM) estimation algorithm. Examination of modern engineering systems; Details on the integrated framework of this approach; Conclusions of this research.

In recent decades, deep learning (DL) has become a rapidly growing research direction, redefining the state-of-the-art performances in a wide range of techniques, such as object detection and speech recognition. In the aircraft design, dynamics, and control field, many works hinge on the information-rich data-driven approach, which includes the fusion-based prognostic and health management, the airliner's flight safety monitoring, intelligent sensing, and flight control systems development. While DL provides great potentials to solve these data-driven problems, a systematic review and discussion as to how the DL has been/can be used for these problems are still missing in relation to the rapidly developing and widely used DL techniques. In this article, we aim to address this urgent issue to provide a timely overview of the state-of-the-art for applying DL to the aircraft design, dynamics, and control field. In particular, we briefly introduce five representative DL methods, i.e., deep neural network, deep autoencoder, deep belief network, convolutional neural network, and recurrent neural network. Mathematical definitions for each method are presented, and illustrative applications are also discussed. We then review the existing DL-based works that have appeared in the aircraft design, dynamics, and control field. The review efforts are divided into two major groups, i.e., the own-ship aircraft modeling, wherein the works have been/can be implemented online for the aircraft design/dynamics/control, and other airplanes research works, wherein DL-based schemes provide offline monitoring of the aircraft operation. We then summarize the data sources and DL architectures. Referring to the experiences of DL research works/techniques development in other related fields, future opportunities, challenges, and potential solutions for implementing DL in the aircraft design, dynamics, and control field are also discussed. [ABSTRACT FROM AUTHOR]

This article focuses on the 3-D guidance strategy for a reusable launch vehicle (RLV) during terminal area energy management (TAEM) phase. Based on sliding-mode and shrinking-horizon techniques, the proposed scheme consists of trajectory generation and correction mechanisms, which can enhance the guidance precision and robustness against disturbances. The RLV guidance model, in the form of a set of highly nonlinear differential equations in the time domain, is recast as an altitude-domain model. By this means, the main characteristics of TAEM gliding motion are extracted. The altitude-domain model is thereby used for trajectory generation. A sliding surface and a guidance law are proposed. Hybrid TAEM constraints can be fully satisfied when the proposed guidance law drives the altitude-domain vehicle model to the designated altitude. Using the proposed guidance law as the input of the altitude-domain model, a constrained TAEM trajectory is generated, leading to TAEM guidance commands simultaneously. The commands are utilized to drive the time-domain model to the terminal target. In an attempt to compensate for model uncertainties and initial deviations, the guidance commands are modified periodically by the shrinking-horizon correction mechanism according to current states. Simulations on different scenarios are provided to demonstrate the performance of the proposed guidance strategy. [ABSTRACT FROM AUTHOR]

This work presents a new approach for designing decentralized receding horizon controllers (DRHC) for cooperative multiple vehicle systems with inter-vehicle communication delays arising from communication failures. Using DRHC each vehicle plans its own state trajectory over a finite prediction time horizon. The neighboring vehicles then exchange their predicted trajectories at each sample time to maintain cooperation objectives. Such communication failures lead to large, inter-vehicle communication delays of exchanged information. Large inter-vehicle communication delays can potentially lead to degraded cooperation performance and unsafe vehicle motion. To maintain desired cooperation performance during faulty conditions, the proposed fault-tolerant DRHC architecture estimates the tail part of the neighboring vehicle trajectory that is unavailable due to communication delays. Furthermore, to address the safety of the team against possible collisions during faulty situations, a fault-tolerant DRHC is developed, which provides safety using a safe protection zone called a tube around the trajectory of faulty neighboring vehicles. The radius of the tube increases with communication delay and maneuverability. A communication failure diagnosis algorithm is also developed. The required communication capability for the fault-diagnosis algorithm and fault-tolerant DRHC suggests a hierarchical fault-tolerant DRHC architecture. Simulations of formation flight of miniature hovercrafts are used to illustrate the effectiveness of the proposed fault-tolerant DRHC architecture. [ABSTRACT FROM AUTHOR]