Non-linear model-free control of flapping wing flying robot using iPID

Chand, Aneesh N.; Kawanishi, Michihiro; Narikiyo, Tatsuo

doi:10.1109/icra.2016.7487458

Cited by 20 publications

(11 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, the model-free control (MFC) approach proposed by Fliess and Join 29 has been successfully illustrated in different concrete case-studies varying from wastewater denitrification, 30 nanopositioning of piezoelectric systems 31 up to inflammation resolution in biomedical applications, 32 see also its references for additional case-study examples and supplementary information. Some research works based on MFC techniques have led to patents, such as Join et al 33 and Abouaı¨ssa et al 34 This control approach has been applied in the aerospace field 35,36 and, except for our previous work, it has never been applied on hybrid MAVs which is an additional motivation for the development of our research project. The advantage of the control methodology proposed in this paper is the capability to estimate the hybrid MAV dynamics, without a prior knowledge of its parameters, only from its output and input-control signal measurements.…”

Section: Literature Reviewmentioning

confidence: 99%

“…33 and Abouaïssa et al. 34 This control approach has been applied in the aerospace field 35,36 and, except for our previous work, it has never been applied on hybrid MAVs which is an additional motivation for the development of our research project. The advantage of the control methodology proposed in this paper is the capability to estimate the hybrid MAV dynamics, without a prior knowledge of its parameters, only from its output and input-control signal measurements.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Model-free control algorithms for micro air vehicles with transitioning flight capabilities

Barth

Condomines

Bronz

et al. 2020

International Journal of Micro Air Vehicles

View full text Add to dashboard Cite

Micro air vehicles with transitioning flight capabilities, or simply hybrid micro air vehicles, combine the beneficial features of fixed-wing configurations, in terms of endurance, with vertical takeoff and landing capabilities of rotorcrafts to perform five different flight phases during typical missions, such as vertical takeoff, transitioning flight, forward flight, hovering and vertical landing. This promising micro air vehicle class has a wider flight envelope than conventional micro air vehicles, which implies new challenges for both control community and aerodynamic designers. One of the major challenges of hybrid micro air vehicles is the fast variation of aerodynamic forces and moments during the transition flight phase which is difficult to model accurately. To overcome this problem, we propose a flight control architecture that estimates and counteracts in real-time these fast dynamics with an intelligent feedback controller. The proposed flight controller is designed to stabilize the hybrid micro air vehicle attitude as well as its velocity and position during all flight phases. By using model-free control algorithms, the proposed flight control architecture bypasses the need for a precise hybrid micro air vehicle model that is costly and time consuming to obtain. A comprehensive set of flight simulations covering the entire flight envelope of tailsitter micro air vehicles is presented. Finally, real-world flight tests were conducted to compare the model-free control performance to that of the Incremental Nonlinear Dynamic Inversion controller, which has been applied to a variety of aircraft providing effective flight performances.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Model-free control algorithms for micro air vehicles with transitioning flight capabilities

Barth

Condomines

Bronz

et al. 2020

International Journal of Micro Air Vehicles

View full text Add to dashboard Cite

show abstract

“…More recently, an intelligent PID (iPID) control has been proposed with many practical applications due to its simple structure and light calculation, such as two-wheeled inverted pendulum (Yu and Wu, 2016), flapping wing flying robot (Chand et al, 2016), glycemia regulation of type-1 diabetes (Ridha and Moog, 2015), etc. The gains of iPID can be tuned through estimations of the uncertainties and thus the iPID control brings out better control performance than the traditional PID control, as illustrated in Novel et al (2010).…”

Section: Introductionmentioning

confidence: 99%

“…The main distinct features of the proposed improved dynamic linearization based MFA-PID (iDL-based MFA-PID) controllers can be summarized as: Compared with the traditional model-free adaptive control (Hou and Huang, 1997; Hou and Jin, 2013; Hou et al, 2017), (a) not only additional error information is used in the proposed iDL-based MFA-PID control laws, but also the estimation of the residual nonlinear uncertainties is incorporated as a compensation; while, compared with the intelligent PID controller (Chand et al, 2016; Li and Wang, 2016; Novel et al, 2010; Ridha and Moog, 2015; Yu and Wu, 2016; Zhang et al, 2018), (b) the proposed iDL-based MFA-PID controllers are directly for nonlinear nonaffine discrete-time systems rather than affined nonlinear continuous-time systems, and (c) an adaptive mechanism is introduced to update the improved linear data model using real-time I/O data. Further, the proposed methods are data-driven where the design and analysis do not depend on model information explicitly.…”

Section: Introductionmentioning

confidence: 99%

Model-free adaptive PID control for nonlinear discrete-time systems

Zhang

Chi

2020

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

This work explores a model-free adaptive PID (MFA-PID) control for nonlinear discrete-time systems with rigorous mathematical analysis under a data-driven framework. An improved compact form dynamic linearization (iCFDL) is proposed to transfer the original nonlinear system into an affined linear data model including a nonlinear residual term. Both a time-difference estimator and a gradient parameter estimator are designed to estimate the nonlinear residual uncertainties and the unknown parameters in the iCFDL model. Subsequently, a novel improved CFDL based MFA-PID (iCFDL-MFA-PID) control is proposed by incorporating these two estimators. The results are extended by the use of improved partial format dynamic linearization (iPFDL) and full format dynamic linearization (iFFDL). The theoretical results are shown using contraction mapping principle-based mathematical analysis, as well as simulations.

show abstract

“…This new viewpoint in control engineering has been successfully illustrated in many concrete casestudies (see, e.g., the references in[34], and[1,2,3,4,17,21,43,47,49,50,52,53,54,57,55,66,72,81,82,87,88,89,90,91,94,95]). Some of the methods have been patented and some have been applied to life sciences[35,43,47,57,81].…”

mentioning

confidence: 95%

Toward a model-free feedback control synthesis for treating acute inflammation

Bara

Fliesś

Join

et al. 2018

Journal of Theoretical Biology

View full text Add to dashboard Cite

An effective and patient-specific feedback control synthesis for inflammation resolution is still an ongoing research area. A strategy consisting of manipulating a pro and anti-inflammatory mediator is considered here as used in some promising model-based control studies. These earlier studies, unfortunately, suffer from the difficultly of calibration due to the heterogeneity of individual patient responses even under similar initial conditions. We exploit a new model-free control approach and its corresponding "intelligent" controllers for this biomedical problem. A crucial feature of the proposed control problem is as follows: the two most important outputs which must be driven to their respective desired states are sensorless. This difficulty is overcome by assigning suitable reference trajectories to the other two outputs that do have sensors. A mathematical model, via a system of ordinary differential equations, is nevertheless employed as a "virtual" patient for in silico testing. We display several simulation results with respect to the most varied situations, which highlight the effectiveness of our viewpoint.

show abstract

Non-linear model-free control of flapping wing flying robot using iPID

Cited by 20 publications

References 23 publications

Model-free control algorithms for micro air vehicles with transitioning flight capabilities

Model-free control algorithms for micro air vehicles with transitioning flight capabilities

Model-free adaptive PID control for nonlinear discrete-time systems

Toward a model-free feedback control synthesis for treating acute inflammation

Contact Info

Product

Resources

About