Synchronised trajectory tracking for a network of MIMO non‐minimum phase systems with application to aircraft control

Zhu, Yang; Chen, Jingyang; Zhu, Bin; Qin, Kaiyu

doi:10.1049/iet-cta.2017.0942

Cited by 10 publications

(6 citation statements)

References 31 publications

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“…inr are assumed to be unknown to reduce the cost and not directly used in the controller design. Therefore, according to (4), (5), (8), (9) and (13), the non-linear functions f i1 and f i2 are unknown. With respect to the remaining aerodynamic coefficients C ilδ a , C ilδ r , C imδ e , C inδ a , and C inδ r , very rough aerodynamic coefficient values can be obtained via low-cost wind tunnel test or software calculation.…”

Section: Preliminaries and Problem Formulation 21 Uav Modelmentioning

confidence: 99%

“…Despite the difficulties in designing distributed controllers for networked UAVs, some results have been obtained to achieve the formation/cooperative control of multiple UAVs by using the firstorder kinematic model [12], linearised state-space equation [13], double integrator model [14,15], or second-order model [16], without consideration of strongly non-linear dynamics and model uncertainties. In practical applications, strong non-linearities inevitably exist in the networked UAVs.…”

Section: Introductionmentioning

confidence: 99%

“…(i) Different from the results of [12][13][14][15][16], in which the distributed cooperative control schemes were developed for networked UAVs based on the first-order kinematic model, linearised state-space model, double integrator model or second-order model without strongly non-linear dynamics and model uncertainties, this study considers the distributed cooperative control of networked UAVs in the presence of non-linear dynamics and model uncertainties. To handle the lumped terms due to inherently unknown non-linearities and actuator faults, FNNs with updating weight matrices, centres, and widths are incorporated into the designed scheme to make use of the advantages of both FLS and NN.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Distributed adaptive fractional‐order fault‐tolerant cooperative control of networked unmanned aerial vehicles via fuzzy neural networks

Zhang

Liu

et al. 2019

IET Control Theory & Applications

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Section: Preliminaries and Problem Formulation 21 Uav Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Distributed adaptive fractional‐order fault‐tolerant cooperative control of networked unmanned aerial vehicles via fuzzy neural networks

Zhang

Liu

et al. 2019

IET Control Theory & Applications

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“…In [22], this goal is attained by employing a distributed iterative learning control based on the successive projection framework that ensures the satisfaction of the system constraints. In contrast, an inversion-based control approach is proposed in [23], where a causal stable inversion module uses local and neighbors' information from the distributed observer network to calculate the estimates of the bounded input and state reference for the controller, such that the output tracking and internal dynamics stabilization are simultaneously delivered.…”

Section: Introductionmentioning

confidence: 99%

Robust Flight-Path Angle Consensus Tracking Control for Non-Minimum Phase Unmanned Fixed-Wing Aircraft Formation in the Presence of Measurement Errors

Zhu

Qin

2023

Drones

Self Cite

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The robust flight-path angle consensus tracking control problem for multiple unmanned fixed-wing aircrafts is investigated in this paper, where the non-minimum phase properties and the presence of measurement errors are systematically addressed. A three-module control scheme is proposed for each aircraft: a Distributed Observer that obtains the available information from the reference system and the neighbor aircraft to provide the estimates of the reference states; a Casual Stable Inversion that calculates the bounded estimates of the desired input, desired external states, and most importantly, desired internal states to resolve the divergence issues caused by the non-minimum phase properties; and a Local Measurement Error Rejection Controller that includes a measurement error estimator (MEE) to actively compensate for the adverse effect of measurement errors to achieve robust consensus tracking control. Stability, convergence, and robustness of the proposed control are analyzed, showing that (1) the non-minimum phase issue can be systematically resolved by the designed Casual Stable Inversion to ensure aircraft internal stability and flight safety, and (2) the consensus tracking accuracy can be improved by tuning a single MEE parameter, which is favorable in practical applications to large-scale unmanned aircraft formations. Comparative simulation results with classic PID-based consensus control demonstrate the advantage of the proposed control in transient oscillations, steady-state tracking accuracy, and robustness against measurement errors.

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“…Then the method is used to realize the trajectory tracking control of the Unmanned Aerial Vehicle (UAV) in the two-dimensional plane [10]. To improve the robustness of control, the uncertainty and external disturbances of the observer estimation model are introduced [11,12]. In addition, the literature [13][14][15] employs the stable inversion and iterative learning control methods to tackle the aircraft trajectory tracking problem during the phase of landing, continuous climbing and continuous descending.…”

Section: Introductionmentioning

confidence: 99%

A Model Free Adaptive Scheme for Integrated Control of Civil Aircraft Trajectory and Attitude

Jiang

Liu

2021

Symmetry

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The adaptive trajectory and attitude control is essential for the four-dimensional (4D) trajectory operation of civil aircraft in symmetric thrust flight. In this work, an integrated trajectory and attitude control scheme is proposed based on the =multi-input multi-output (MIMO) model free adaptive control (MFAC) method. First, the full-form dynamic linearization technique is adopted to build the equivalent data model of aircraft. Also, the MIMO MFAC scheme with saturation constraint is designed to achieve an accurate tracking control for a given 4D trajectory and attitude. Besides, the performance limitations of aircraft are taken into consideration, and the MIMO MFAC scheme with hard constraints is designed. In addition, to improve the simulation efficiency, a control scheme with mixed constraints, i.e., saturation and hard constraints, is further proposed. It can be seen from the simulation results that the proposed method can perform an integrated control of the aircraft 4D trajectory and attitude without precise modeling, and the control performance is better than that of the model-based control method in terms of flight altitude and yaw angle control. The integrated data-driven control scheme proposed in this paper provides a theoretical solution for the precise operation of aircraft under 4D trajectory.

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Synchronised trajectory tracking for a network of MIMO non‐minimum phase systems with application to aircraft control

Cited by 10 publications

References 31 publications

Distributed adaptive fractional‐order fault‐tolerant cooperative control of networked unmanned aerial vehicles via fuzzy neural networks

Distributed adaptive fractional‐order fault‐tolerant cooperative control of networked unmanned aerial vehicles via fuzzy neural networks

Robust Flight-Path Angle Consensus Tracking Control for Non-Minimum Phase Unmanned Fixed-Wing Aircraft Formation in the Presence of Measurement Errors

A Model Free Adaptive Scheme for Integrated Control of Civil Aircraft Trajectory and Attitude

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