Model-free Integrated Navigation of Small Fixed-wing UAVs Full State Estimation in Wind Disturbance

2022 ◽  
pp. 1-1
Author(s):  
Yue Yang ◽  
Xiaoxiong Liu ◽  
Xuhang Liu ◽  
Yicong Guo ◽  
Weiguo Zhang
Keyword(s):  
State Estimation ◽  
Integrated Navigation ◽  
Wind Disturbance ◽  
Model Free ◽  
Full State

Flexible-Joint Humanoid Balancing Augmentation via Full-State Feedback Variable Impedance Control

2021 ◽  
Vol 13 (2) ◽  
Author(s):  
Emmanouil Spyrakos-Papastavridis ◽  
Jian S. Dai
Keyword(s):  
State Feedback ◽  
Position Control ◽  
Impedance Control ◽  
Humanoid Robots ◽  
Flexible Joint ◽  
Model Free ◽  
Floating Base ◽  
Full State ◽  
Full State Feedback ◽  
Series Elastic Actuators

Abstract This paper attempts to address the quandary of flexible-joint humanoid balancing performance augmentation, via the introduction of the Full-State Feedback Variable Impedance Control (FSFVIC), and Model-Free Compliant Floating-base VIC (MCFVIC) schemes. In comparison to rigid-joint humanoid robots, efficient balancing control of compliant bipeds, powered by Series Elastic Actuators (or harmonic drives), requires the design of more sophisticated controllers encapsulating both the motor and underactuated link dynamics. It has been demonstrated that Variable Impedance Control (VIC) can improve robotic interaction performance, albeit by introducing energy-injecting elements that may jeopardize closed-loop stability. To this end, the novel FSFVIC and MCFVIC schemes are proposed, which amalgamate both collocated and non-collocated feedback gains, with power-shaping signals that are capable of preserving the system's stability/passivity during VIC. The FSFVIC and MCFVIC stably modulate the system's collocated state gains to augment balancing performance, in addition to the non-collocated state gains that dictate the position control accuracy. Utilization of arbitrarily low-impedance gains is permitted by both the FSFVIC and MCFVIC schemes propounded herein. An array of experiments involving the COmpliant huMANoid reveals that significant balancing performance amelioration is achievable through online modulation of the full-state feedback gains (VIC), as compared to utilization of invariant impedance control.

scholarly journals Constrained Unscented Particle Filter for SINS/GNSS/ADS Integrated Airship Navigation in the Presence of Wind Field Disturbance

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 471 ◽  
Author(s):  
Zhaohui Gao ◽  
Dejun Mu ◽  
Yongmin Zhong ◽  
Chengfan Gu
Keyword(s):  
Wind Speed ◽  
Particle Filter ◽  
State Estimation ◽  
Wind Field ◽  
Satellite System ◽  
Integrated Navigation ◽  
Unscented Particle Filter ◽  
Field Disturbance ◽  
Global Navigation Satellite ◽  
Optimal State Estimation

Due to the disturbance of wind field, it is difficult to achieve precise airship positioning and navigation in the stratosphere. This paper presents a new constrained unscented particle filter (UPF) for SINS/GNSS/ADS (inertial navigation system/global navigation satellite system/atmosphere data system) integrated airship navigation. This approach constructs a wind speed model to describe the relationship between airship velocity and wind speed using the information output from ADS, and further establishes a mathematical model for SINS/GNSS/ADS integrated navigation. Based on these models, it also develops a constrained UPF to obtain system state estimation for SINS/GNSS/ADS integration. The proposed constrained UPF uses the wind speed model to constrain the UPF filtering process to effectively resist the influence of wind field on the navigation solution. Simulations and comparison analysis demonstrate that the proposed approach can achieve optimal state estimation for SINS/GNSS/ADS integrated airship navigation in the presence of wind field disturbance.

scholarly journals Model-Free State Estimation Using Low-Rank Canonical Polyadic Decomposition

2021 ◽  
Vol 5 (2) ◽  
pp. 605-610
Author(s):  
Ahmed S. Zamzam ◽  
Yajing Liu ◽  
Andrey Bernstein
Keyword(s):  
State Estimation ◽  
Free State ◽  
Low Rank ◽  
Model Free ◽  
Canonical Polyadic Decomposition

Design of a Decentralized Detection Filter for a Class of Interconnected Systems

2001 ◽  
Author(s):  
Shamanth Shankar ◽  
Darbha Swaroop ◽  
Aniruddha Datta
Keyword(s):  
Transfer Function ◽  
State Estimation ◽  
Interconnected Systems ◽  
Decentralized Detection ◽  
Estimation Errors ◽  
State Information ◽  
Identical Structure ◽  
Full State ◽  
Filtering Problem ◽  
Detection Filter

Abstract In this paper, we consider the problem of designing a decentralized detection filter for a class of homogeneous interconnected systems; in this class of systems, all subsystems have identical structure. A fault in a subsystem propagates via interactions with other subsystems in the collection. The decentralized detection filter is composed of interacting detection filters, one for each subsystem. We assume communication of state estimates amongst subsystems to be feasible. A concern, dealt with here, is that of propagation of state estimation errors. It is treated as a Ĥ∞ filtering problem with full state information, by requiring the transfer function from the propagated input of the ith subsystem to that of the (i + 1)st subsystem to have a magnitude less than unity at all frequencies.

New Method Based on Model-Free Adaptive Control Theory and Kalman Filter for Multi-Product Unsteady Flow State Estimation

2021 ◽  
Vol 143 (7) ◽  
Author(s):  
Lei He ◽  
Jing Gong ◽  
Kai Wen ◽  
Changchun Wu ◽  
Yuan Min
Keyword(s):  
Adaptive Control ◽  
Unsteady Flow ◽  
State Estimation ◽  
Flow Model ◽  
Transient Flow ◽  
Pipeline System ◽  
Flow State ◽  
Flow Estimation ◽  
Model Based ◽  
Model Free

Abstract In this paper, a new methodology is proposed to realize real-time unsteady flow estimation for a multi-product pipeline system. Integrating transient flow model, adaptive control theory, and adaptive filter, this method is developed to solve the contradiction between the efficiency and accuracy in traditional model-based methods. In terms of improving computational efficiency, the linear flow model based on frequency response and difference transforming is established to replace the traditional nonlinear flow model for transient flow state estimation. To reduce the deviation between actual observations and linear model estimates, we first introduce a model-free adaptive control method as linear compensation of the reduced order unsteady flow state model. To overcome the interference of observation noise, the Kalman filter method is applied to the modified state space model to obtain the one-step-ahead transient flow estimation. The proposed method is applied to the transient flow state estimation of a multi-product pipeline system and compared with the model-based method and two data-driven methods. The proposed method can reduce the deviation of transient flow estimation between the reduced order linear model and the traditional nonlinear model to less than 0.5% under unforeseen conditions and shows strong robustness to noise interference and parameter drift.

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