Flight Control Design using Incremental Nonlinear Dynamic Inversion with Fixed-lag Smoothing Estimation

Cervantes, Tito J. Ludeña; Choi, Seong H.; Kim, Byoung S.

doi:10.1007/s42405-020-00273-8

Cited by 7 publications

(4 citation statements)

References 11 publications

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“…e so-called model identification refers to the estimation and judgment of parameters such as coefficients and lag period in the model [22][23][24].…”

Section: Pvar Model Identificationmentioning

confidence: 99%

An Empirical Study on the Relationship between Education and Economic Development Based on PVAR Model

Sun

2021

Scientific Programming

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In order to effectively analyze the dynamic relationship between education and economic development, an empirical study on the relationship between education and economic development based on the PVAR model is proposed. This article expounds on the principles, assumptions, identification, and estimation methods of the PVAR model, takes the education level and economic development level as the research object, explains the corresponding variables, and selects the indicators. Using Cobb–Douglas production function as a theoretical model, this article analyzes the theoretical relationship between the education level and economic development level. Based on this theoretical relationship, this article makes an empirical analysis on the relationship between education level and economic development level using the PVAR model. The results show that, on the whole, economic development can drive the development of education, but there are obvious regional differences in the impact of economic growth on the development of education. The impact of economic growth in the eastern region on education is significantly higher than that in the central and western regions. There is an interactive relationship between education level and economic development level, and there is a certain incubation period for education level and economic development level to play their role.

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“…e so-called model identification refers to the estimation and judgment of parameters such as coefficients and lag period in the model [22][23][24].…”

Section: Pvar Model Identificationmentioning

confidence: 99%

An Empirical Study on the Relationship between Education and Economic Development Based on PVAR Model

Sun

2021

Scientific Programming

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“…By producing the incremental form of the control command by calculating the error between the virtual control law and the acceleration of the system state, INDI is robust to model uncertainties [12][13][14][15][16][17][18][19]. In [17], the stability and robustness of the INDI technique has been proven.…”

Section: Introductionmentioning

confidence: 99%

Incremental Nonlinear Dynamic Inversion Attitude Control for Helicopter with Actuator Delay and Saturation

Zhang

2023

Aerospace

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In this paper, an incremental nonlinear dynamic inversion (INDI) control scheme is proposed for the attitude tracking of a helicopter with model uncertainties, and actuator delay and saturation constraints. A finite integral compensation based on model reduction is used to compensate the actuator delay, and the proposed scheme can guarantee the semi-globally uniformly ultimately bounded tracking. The overall attitude controller is separated into a rate, an attitude, and a collective pitch controller. The rate and collective pitch controllers combine the proposed method and INDI to enhance the robustness to actuator delay and model uncertainties. Considering the dynamic of physical actuators, pseudo-control hedging (PCH) is introduced both in the rate and attitude controller to improve tracking performance. By using the proposed controller, the helicopter shows good dynamics under the multiple restrictions of the actuators.

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“…The policy evaluation step is outlined in Eq. (1), where ( , | , ) defines the probability of a subsequent state, , occurring with the respective reward, . Furthermore, ( | ) denotes the probability of an action to be taken at a given state, .…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, controllers that can be derived using data-driven methods are preferred in such cases. Advanced sensor-based controllers exist, such as incremental dynamic inversion controllers [1], to alleviate the burden of modelling complex systems. However, the aforementioned methods suffer from state reconstruction dependencies and synchronisation issues with the sensor data.…”

mentioning

confidence: 99%

Safe Curriculum Learning for Linear Systems with Parametric Unknowns in Primary Flight Control

Buysscher

Pollack

Kampen

2022

AIAA SCITECH 2022 Forum

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AIAA SciTech Forum controllers that are more flexible in their design process. Additionally, complex systems are often difficult to model and validate. Consequently, controllers that can be derived using data-driven methods are preferred in such cases. Advanced sensor-based controllers exist, such as incremental dynamic inversion controllers [1], to alleviate the burden of modelling complex systems. However, the aforementioned methods suffer from state reconstruction dependencies and synchronisation issues with the sensor data. Another approach to data driven controller design resides in machine learning paradigms such as Reinforcement Learning (RL). The fundamental principle used in RL is the representation of the world as an agent being confronted with a choice of action. The agent learns a control policy by interacting with the environment and gaining experience of the dynamics over time. Its basic principle is simple yet effective. However, with increasing task complexity (i.e., growing state and action spaces) increases, RL agents have a tendency to struggle learning a policy reliably [2]. Curriculum Learning (CurL) indroduced in [2], provides a structured approach to allow learning on more complex applications by dividing the initial task into sub-tasks [3,4]. This facilitates the agent's learning process and increases the likelihood of successfully finding a control policy [5]. Given the examples cited previously, certainly in transport applications where stringent (safety) requirements apply, the safety aspect of the learning process and the correct operation of the controller is of crucial importance. Unlike RL methods which in their simplest forms generally lack consideration of the safety aspect [6], Safe Learning (SL) does provide a framework to this end [7].The research outlined in this paper proposes a safe curriculum learning architecture that builds on the research presented in [8]. Here, the dependency on knowledge about an uncertain model for the safety algorithm is removed by complementing the paradigm in [8] with a system identification capability.First a brief introduction to the fields of RL, Curriculum Learning, Safe Learning, and system identification is provided in sections II.A, II.B, II.C and II.D, respectively. This is followed by a detailed presentation of the approach chosen in this research outlined in Section III. Finally, the proposed paradigm is tested through two experiments. Initially, a Mass-Spring-Damper (MSD) system is used to verify the architecture for which the results are presented in Section IV.A. In Section IV.B, the results of a the safe curriculum architecture applied on a quadrotor are outlined. The paper is concluded with a discussion of the results of the experiments, as well as a conclusion and recommendations for further research. II. Safe Curriculum Learning FrameworkThe core principles in safe curriculum learning are derived from three research fields: reinforcement learning, curriculum learning and safe learning. Inherently, the fundamentals originate from th...

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Flight Control Design using Incremental Nonlinear Dynamic Inversion with Fixed-lag Smoothing Estimation

Cited by 7 publications

References 11 publications

An Empirical Study on the Relationship between Education and Economic Development Based on PVAR Model

An Empirical Study on the Relationship between Education and Economic Development Based on PVAR Model

Incremental Nonlinear Dynamic Inversion Attitude Control for Helicopter with Actuator Delay and Saturation

Safe Curriculum Learning for Linear Systems with Parametric Unknowns in Primary Flight Control

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