Nonaffine Helicopter Control Design and Implementation Based on a Robust Explicit Nonlinear Model Predictive Control

Seo, Joohwan; Lee, Seongwon; Choi, Jongeun

doi:10.1109/tcst.2021.3069106

Cited by 15 publications

(15 citation statements)

References 29 publications

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“…A robust MPC was proposed for a helicopter in [20] where an extended high-gain observer estimates model uncertainties and disturbances. However, the nominal mathematical model is required.…”

Section: A Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Incremental Model Predictive Control Exploiting Time-Delay Estimation for a Robot Manipulator

Wang

Leibold

Lee

et al. 2022

IEEE Trans. Contr. Syst. Technol.

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Section: A Related Workmentioning

confidence: 99%

“…As assumed, ḡ is selected such that ∥I − g(x)ḡ −1 ∥ ≤ δ < 1. Besides, according to (20), the following inequalities are obtained:…”

Section: Appendix a Proof Of Lemmamentioning

confidence: 99%

Incremental Model Predictive Control Exploiting Time-Delay Estimation for a Robot Manipulator

Wang

Leibold

Lee

et al. 2022

IEEE Trans. Contr. Syst. Technol.

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show abstract

“…Due to the characteristics of nonlinear feedback and extended state, ESO could achieve accurate and agile observation of states and disturbances with little reliance on the system model [40]. Thus, it could be seen that the ESO presented above could track the total disturbances accurately as shown in (14).…”

Section: Design Of Esomentioning

confidence: 99%

“…By considering the constraints on both state variables and control inputs, MPC could achieve good tracking performance under given conditions [13]. In [14], the explicit nonlinear MPC was used to build the multi-time-scale control structure, while a state observer was designed to handle disturbances and the dynamic inversion was employed to simplify the input-output relations, these algorithms were tested on a mode-scale helicopter. To deal with model uncertainties, an MPC strategy based on an adaptive law was proposed for an unmanned helicopter with two degrees of freedom in [15].…”

Section: Introductionmentioning

confidence: 99%

Backstepping Control of an Unmanned Helicopter Subjected to External Disturbance and Model Uncertainty

et al. 2021

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A helicopter is a highly nonlinear system. Its mathematical model is difficult to establish accurately, especially the complicated flapping dynamics. In addition, the forces and moments exerted on the fuselage are very vulnerable to external disturbances like wind gust when flying in the outdoor environment. This paper proposes a composite control scheme which consists of a nonlinear backstepping controller and an extended state observer (ESO) to handle the above problems. The stability of the closed-loop system can be guaranteed based on Lyapunov theory. The external disturbances and model nonlinearities are treated as a lumped disturbance. Meanwhile, the ESO is employed to compensate the influence by estimating the lumped disturbance in real-time. Numerical simulation results are presented to demonstrate that the algorithm can achieve accurate and agile attitude tracking under the external wind gust disturbances even with model uncertainties. When coming to the flight test, a block dropping device was designed to generate a quantifiable and replicable disturbance, and the experimental results indicate that the algorithm introduced above can reject the external disturbance rapidly and track the given attitude command precisely.

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“…However, there are still few related research results to deal with the challenges in advanced controller design. These related literatures mainly consider one certain flight mode such as high-speed mode [12] or low-speed mode [13] with traditional model-based approaches [11] including nonlinear dynamic inversion (NDI), sliding mode control (SMC) and backstepping. However, these methods ignored nonaffine property of compound helicopter and can hardly overcome uncertain cross-coupling perturbation throughout flight envelope.…”

Section: Introductionmentioning

confidence: 99%

Improved Neural Network Adaptive Control for Compound Helicopter with Uncertain Cross-Coupling in Multimodal Maneuver

Fang¹,

Xiong²,

Zhang³

et al. 2021

Preprint

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The main goal of this study was to create a robust control system that could guide or replace the pilots in tracking of commanded velocity and attitude in multimodal maneuver, while complex dynamics and uncertain aerodynamic cross-coupling among control surfaces of compound helicopter are considered. To this end, a Pi-Sigma neural network (PSNN) adaptive controller is proposed based upon the certainty-equivalence (CE) principle, where a novel Lyapunov-based weight self-tuning algorithm augmented with e-modification is designed to realize efficient uncertainty approximation and guarantee robustness of convergence process. Compared with traditional neural networks in control field, stronger generalization ability of PSNN must be balanced against weaker stability, which leads to inevitable parameters perturbation. Therefore, an incremental nonlinear dynamic inversion (INDI) framework is established to decouple original overactuated system and reject parameters perturbation in PSNN. Meanwhile, by incorporating Lagrang- multiplier method into allocation, an original incremental allocation method is designed to get globally ideal control input according to time-varying working capability of each surface. In terms of Lyapunov theorem, it is demonstrated that the closed-loop augmented system driven by the proposed control scheme is semi-global uniformly ultimately bounded (SGUUB). Finally, the simulation result validates the effectiveness of proposed control scheme.

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Nonaffine Helicopter Control Design and Implementation Based on a Robust Explicit Nonlinear Model Predictive Control

Cited by 15 publications

References 29 publications

Incremental Model Predictive Control Exploiting Time-Delay Estimation for a Robot Manipulator

Incremental Model Predictive Control Exploiting Time-Delay Estimation for a Robot Manipulator

Backstepping Control of an Unmanned Helicopter Subjected to External Disturbance and Model Uncertainty

Improved Neural Network Adaptive Control for Compound Helicopter with Uncertain Cross-Coupling in Multimodal Maneuver

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