A Robust Adaptive RBFNN Augmenting Backstepping Control Approach for a Model-Scaled Helicopter

Zou, Yao; Zheng, Zewei

doi:10.1109/tcst.2015.2396851

Cited by 102 publications

(40 citation statements)

References 27 publications

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“…Remark In practice, typical VTOL UAVs include helicopter, quadrotor, and ducted‐fan aircraft. Their kinematics and dynamics all comply with except that the mechanisms generating the applied thrust and torque are different and they suffer from distinct forms of aerodynamic disturbances. In fact, by assuming negligible aerodynamic and external disturbances, can be regarded as a nominal model for this class of VTOL UAVs.…”

Section: Problem Statementmentioning

confidence: 98%

Distributed hierarchical control for multiple vertical takeoff and landing UAVs with a distance‐based network topology

Zou

Meng

2019

Intl J Robust & Nonlinear

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Summary A distributed formation control algorithm is proposed for multiple vertical takeoff and landing (VTOL) unmanned aerial vehicles (UAVs) in this paper. The neighboring information is obtained by active measurements rather than by nonactive communications as done in the current literatures. By properly expanding the distance‐based network topology structure between each pair of UAVs, a distributed algorithm is proposed such that the switching topology remains connected along the time. Since a VTOL UAV system is typically underactuated, a hierarchical idea is introduced to derive the control design procedure. More specifically, the studied formation control problem of multiple VTOL UAVs is first transformed into the consensus problem of their corresponding error systems. Then, a command force and an applied torque are synthesized for each VTOL UAV such that the error systems reach a consensus. It is demonstrated in terms of Lyapunov theory that the proposed distributed hierarchical control algorithm guarantees the formation realization of multiple VTOL UAVs while maintaining the network connectivity. Finally, simulations are performed to validate the theoretical results.

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Section: Problem Statementmentioning

confidence: 98%

Distributed hierarchical control for multiple vertical takeoff and landing UAVs with a distance‐based network topology

Zou

Meng

2019

Intl J Robust & Nonlinear

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“…/. Further, in terms of [4,7,11,21], the x b -axis and y baxis force components and the tail rotor anti-torque t make little contributions to the applied force f and torque , respectively. Thus, f and can be decomposed as…”

Section: Helicopter Model For Controller Designmentioning

confidence: 99%

“…Furthermore, in order to derive the longitudinal and lateral cyclic pitches ı lon and ı lat , the flapping dynamics (10) is also required to be simplified. According to [21], the flapping time constant t f is so small that the response of the flapping dynamics is faster than the one of the fuselage dynamics. In this case, we have P a s D P b s D 0, which implies that…”

Section: Remarkmentioning

confidence: 99%

“…Given a smooth reference trajectory p c D OEp cx ; p cy ; p c´ T with its derivatives P p c and R p c , the control objective is to design control inputs Â m , Â t , ı lon , and ı lat for the miniature helicopter with the proposed adaptive backstepping controller, so that it can asymptotically track p c . The controller design is based on the model composed by (3), (5), (13), and (14); the backstepping technique serves as the main control framework along with the adaptive algorithms designed by the projection algorithm to estimate and compensate the upper bounds of the unmodeled dynamics. In the position control loop, a controller T m m R T c e 3 is designed to achieve the position tracking, where an auxiliary dynamic system is introduced to ensure the constrained requirement in extracting the main rotor thrust T m and the command attitude Q c .…”

Section: Controller Development Projectmentioning

confidence: 99%

“…There have been some references dealing with the unmodeled dynamics during the controller developments. With the unmodeled dynamics considered, the backstepping technique was combined with the nonlinear damping [11], the neural networks [12,13], and the disturbance observer [14] for the miniature helicopter. However, these approaches achieved the bounded, not the asymptotically stable tracking.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive backstepping trajectory tracking controller for a miniature helicopter

Zou

2016

Adaptive Control & Signal

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An adaptive backstepping controller is proposed to achieve the asymptotically stable trajectory tracking for a miniature helicopter. The helicopter model is firstly decomposed into a cascaded structure between the position and attitude loops with unmodeled dynamics. The backstepping technique is applied to exploit this cascaded structure and to streamline the controller design procedure. Hyperbolic tangent functions, instead of traditional sign functions, are adopted to complete adaptive algorithms for adjusting the upper bounds of the unmodeled dynamics. Further, the controller introduces an auxiliary dynamic system to ensure the thrust constraint and to obviate singularity during the command attitude extraction. The stability analysis demonstrates that, the asymptotical stability of the auxiliary dynamic system warrants the asymptotically stable tracking of the miniature helicopter system. Simulations verify the theoretical results and analyze the tracking performance.

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Adaptive neural network control for permanent magnet synchronous motor with input nonlinearity

Lin,

Wu,

Liu

et al. 2024

Asian Journal of Control

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This study aims to design a new adaptive control method for permanent magnet synchronous motors (PMSMs) using neural networks (NNs). In comparison to traditional motor backstepping control designs, this research introduces a command filtering strategy to effectively address the common issue of “complexity explosion” in traditional methods. Additionally, considering the potential input hysteresis nonlinearity in practical applications, we introduce a hysteresis inverse operator to mitigate its adverse effects on control. Furthermore, by employing a finite‐time control strategy, we ensure rapid convergence of tracking errors within a finite time frame. Moreover, an adaptive NN controller is designed to approximate unknown continuous nonlinear functions of the system. Finally, the stability and convergence of the closed‐loop system are analyzed using the direct Lyapunov method.

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A Robust Adaptive RBFNN Augmenting Backstepping Control Approach for a Model-Scaled Helicopter

Cited by 102 publications

References 27 publications

Distributed hierarchical control for multiple vertical takeoff and landing UAVs with a distance‐based network topology

Distributed hierarchical control for multiple vertical takeoff and landing UAVs with a distance‐based network topology

Adaptive backstepping trajectory tracking controller for a miniature helicopter

Adaptive neural network control for permanent magnet synchronous motor with input nonlinearity

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