Robust adaptive FTC allocation for over‐actuated systems with uncertainties and unknown actuator non‐linearity

Zhi, Jianhui; Chen, Yong; Dong, Xinmin; Liu, Zongcheng; Shi, Chao

doi:10.1049/iet-cta.2017.0385

Cited by 7 publications

(7 citation statements)

References 31 publications

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“…(Andrade et al, 2017) employed the LMI technique to guarantee the stability and the robust performance of an F-16 aircraft and to improve its damping factor. Similarly, (Zhi & al., 2018), presented a robust FTC technique in order to deal with disturbances and unmodelled dynamics of an over-actuated system. However, despite the effectiveness and the robustness of these methods, they remain "passive".…”

Section: Introductionmentioning

confidence: 81%

“…Various FTC strategies were reported in specialised literature: linear matrix inequality (Andrade et al, 2017), the pseudo-inverse (Tchon & Janiak, 2009), multiple model (Pandey, Kar & Mahanta, 2017) and adaptive control methods (Yu et al, 2019), robust controls (Zhi & al., 2018), the Algebraic Riccati Equation (ARE), the Hamilton-Jacobi Equation (HJE), the sliding mode control (SMC) (Zhang et al, 2018) and intelligent controls based on artificial neural network (Yen & Ho, 2004). The "suitable" technique for FTC depends on the type of system considered and the nature/gravity of the fault.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Nonlinear Fault Recovery of a Quadrotor Using Progressive Accommodation

Mlayeh¹,

Ghachem²,

Nasri³

et al. 2020

STUD INFORM CONTROL

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This paper proposes a nonlinear (NL) fault-tolerant control (FTC) strategy for stabilizing the attitude of a quadrotor affected by multiple rotor failures. The recovery process relies mainly on progressive accommodation (PA) strategy. The latter uses the Newton-Raphson (NR) algorithm in order to solve the state-dependent Riccati Equation (SDRE) in a recursive way. Simulations are performed using the NL model of the quadrotor in fault-free and post-fault cases. Both PA and the direct SDRE methods are employed so that the attitude control of the quadrotor can be stabilized. The results show that the PA strategy performs better than the classical method when it comes to convergence speed and to preserving the control objectives of the nominal system.

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Section: Introductionmentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

Nonlinear Fault Recovery of a Quadrotor Using Progressive Accommodation

Mlayeh¹,

Ghachem²,

Nasri³

et al. 2020

STUD INFORM CONTROL

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show abstract

“…where c i1 = u i and c i2 = ūi . The feasible region of the control allocation problem is a convex polygon whose vertices are among the intersection points of the lines which satisfy all the inequalities in (9). Intersection points could be determined as follows:…”

Section: Determining the Feasible Regionmentioning

confidence: 99%

“…The corrective term is calculated such that elements of the control signal which violate the constraints are forced back to the admissible region. [9] presents a robust adaptive fault tolerant control using a wieghted pseudo inverse control allocation in the presence of disturbance, unmodeled dynamics and actuator nonlinearity. The main controller is a combination of an adaptive control, radial basis function neural network and a robust controller.…”

Section: Introductionmentioning

confidence: 99%

Guaranteed feasible control allocation using model predictive control

Naderi

Sedigh

Johansen

2019

Control Theory Technol.

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This paper proposes a guaranteed feasible control allocation method based on the model predictive control. Feasible region is considered to guarantee the determination of the desired virtual control signal using the pseudo inverse methodology and is described as a set of constraints of an MPC problem. With linear models and the given constraints, feasible region defines a convex polyhedral in the virtual control space. In order to reduce the computational time, the polyhedral can be approximated by a few axis aligned hypercubes. Employing the MPC with rectangular constraints substantially reduces the computational complexity. In two dimensions, the feasible region can be approximated by a few rectangles of the maximum area using numerical geometry techniques which are considered as the constraints of the MPC problem. Also, an active MPC is defined red selected as the controller to minimize the cost function in the control horizon. Finally, several simulation examples are employed to illustrate the effectiveness of the proposed techniques.

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“…Several works based on NN controllers with an adaptive control technique have been proposed; [9] presents an adaptive neural network saturated control for MDF continuous hot pressing hydraulic system with uncertainties, the RBFNN-based reconstruction law is introduced to approximate the composite term consisting of an unknown function, disturbances, and a saturation error. In [10], a robust adaptive fault-tolerant control has been proposed for over-actuated systems in the simultaneous presence of matched disturbances, unmodeled dynamics and unknown non-linearity of the actuator, authors used the radial basis function neural network in order to have an approximation of the unmodeled dynamics. [11] proposes an actuator fault tolerant control using an adaptive RBFNN fuzzy sliding mode controller for coaxial octorotor UAV, simulation results show that, despite the rotor failure, the octorotor can remain in flight and can perfectly perform trajectory control in x, y and z and can also control yaw, roll and pitch angles.…”

Section: Related Workmentioning

confidence: 99%

Adaptive RBFNN Strategy for Fault Tolerant Control: Application to DSIM under Broken Rotor Bars Fault

Layadi¹,

Zeghlache²,

Djerioui³

et al. 2019

IJISA

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This paper presents a fault tolerant control (FTC) based on Radial Base Function Neural Network (RBFNN) using an adaptive control law for double star induction machine (DSIM) under broken rotor bars (BRB) fault in a squirrel-cage in order to improve its reliability and availability. The proposed FTC is designed to compensate for the default effect by maintaining acceptable performance in case of BRB. The sufficient condition for the stability of the closed-loop system in faulty operation is analyzed and verified using Lyapunov theory. To proof the performance and effectiveness of the proposed FTC, a comparative study within sliding mode control (SMC) is carried out. Obtained results show that the proposed FTC has a better robustness against the BRB fault.

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Robust adaptive FTC allocation for over‐actuated systems with uncertainties and unknown actuator non‐linearity

Cited by 7 publications

References 31 publications

Nonlinear Fault Recovery of a Quadrotor Using Progressive Accommodation

Nonlinear Fault Recovery of a Quadrotor Using Progressive Accommodation

Guaranteed feasible control allocation using model predictive control

Adaptive RBFNN Strategy for Fault Tolerant Control: Application to DSIM under Broken Rotor Bars Fault

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