Descriptor sliding mode approach for fault/noise reconstruction and fault-tolerant control of nonlinear uncertain systems

Song, Yongduan; Lü, Ying; Gan, Zhongxue

doi:10.1016/j.ins.2016.06.008

Cited by 32 publications

(19 citation statements)

References 39 publications

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“…Problem Description. Considering the following wind turbine pitch system with four conditions, pitch actuator fault, pitch sensor fault, system uncertainty, and external disturbance [14][15][16][17], the corresponding dynamic equations are as follows:…”

Section: Descriptor Sliding Mode Observer Designmentioning

confidence: 99%

“…Theorem 7. Based on the sliding mode input scheme as ( ) (26) presented, the system shown in (41) is stable, if there exist ∈ R × > 0, ∈ R × > 0, and ∈ R ( + +ℎ)× such that the constraint (24) holds and the LMI optimization problem admits a feasible solution [14]:…”

Section: The Stability Condition Of Observermentioning

confidence: 99%

“…An active fault-tolerant control for the pitch system (14) comprises a norm controller and a reconfigurable controller, designed as…”

Section: Active Fault-tolerant Control Designmentioning

confidence: 99%

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Active Fault-Tolerant Control for Wind Turbine with Simultaneous Actuator and Sensor Faults

et al. 2017

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The purpose of this paper is to show a novel fault-tolerant tracking control (FTC) strategy with robust fault estimation and compensating for simultaneous actuator sensor faults. Based on the framework of fault-tolerant control, developing an FTC design method for wind turbines is a challenge and, thus, they can tolerate simultaneous pitch actuator and pitch sensor faults having bounded first time derivatives. The paper’s key contribution is proposing a descriptor sliding mode method, in which for establishing a novel augmented descriptor system, with which we can estimate the state of system and reconstruct fault by designing descriptor sliding mode observer, the paper introduces an auxiliary descriptor state vector composed by a system state vector, actuator fault vector, and sensor fault vector. By the optimized method of LMI, the conditions for stability that estimated error dynamics are set up to promote the determination of the parameters designed. With this estimation, and designing a fault-tolerant controller, the system’s stability can be maintained. The effectiveness of the design strategy is verified by implementing the controller in the National Renewable Energy Laboratory’s 5-MW nonlinear, high-fidelity wind turbine model (FAST) and simulating it in MATLAB/Simulink.

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Section: Descriptor Sliding Mode Observer Designmentioning

confidence: 99%

Section: The Stability Condition Of Observermentioning

confidence: 99%

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Active Fault-Tolerant Control for Wind Turbine with Simultaneous Actuator and Sensor Faults

et al. 2017

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“…Sliding-mode variable structure control has been widely used in the field of nonlinear control because of its excellent robustness [27]. In reference [28], an integral sliding-mode variable structure controller is proposed, which uses the load torque observer to suppress the effect of load disturbance.…”

Section: Introductionmentioning

confidence: 99%

Multi-packet transmission aero-engine DCS neural network sliding mode control based on multi-kernel LS-SVM packet dropout online compensation

Wang

Ren

2020

PLoS ONE

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In view of the strong nonlinear characteristics of the multi-packet transmission Aero-engine DCS with induced delay and random packet dropout, a neural network PID approach law sliding-mode controller using sliding window strategy and multi-kernel LS-SVM packet dropout online compensation is proposed. Firstly, the time-delay term in the system model is transformed equivalently, to establish the discrete system model of multi-packet transmission without time-delay; furthermore, the construction of multi-kernel function is transformed into kernel function coefficient optimization, and the optimization problem can be solved by the chaos adaptive artificial fish swarm algorithm, then the online predictive compensation will be made for data packet dropout of multi-packet transmission through the sliding window multi-kernel LS-SVM. After that, a sliding-mode controller design method of proportional integral differential approach law based on neural network is proposed. And online adjustment of PID approach law parameters can be achieved by nonlinear mapping of neural network. Finally, Truetime is used to simulate the method. The results shows that when the packet dropout rate is 30% and 60%, the average error of packet dropout prediction of multikernel LS-SVM reduces 29.21% and 44.66% compared with that of combined kernel LS-SVM, and the chattering amplitude of the proposed neural network PID approach law sliding-mode controller is decreased compared with other five approach law methods respectively. This controller can ensure a fast response speed, which shows that this method can achieve a better tracking control of the aeroengine network control system.

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“…[8][9][10] Among those techniques, sliding mode control (SMC) is one of the certain approaches to design the controller. [11][12][13][14][15][16][41][42][43] It is a nonlinear control technique showing significant characteristics in terms of efficiency, robustness, simple adjustment, and implementation. 12,17 Some realistic systems such as piezo-actuated stage, spacecraft-attitude-tracking maneuvers, slosh-free motion, six-phase induction machine, and piezo-actuated stage have profitably assisted on SMC technique.…”

Section: Introductionmentioning

confidence: 99%

Grey Wolf Optimization-Based Second Order Sliding Mode Control for Inchworm Robot

Roy

Ghoshal²

2019

Robotica

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SUMMARY The flexible motion of the inchworm makes the locomotion mechanism as the prominent one than other limbless animals. Recently, the application of engineering greatly assists the inchworm locomotion to be applicable in the robotic mechanism. Due to the outstanding robustness, sliding mode control (SMC) has been validated as a robust control strategy for diverse types of systems. Even though the SMC techniques have made numerous achievements in several fields, some systems cannot be comfortably accepted as the general SMC approaches. Accordingly, this paper develops the Grey Wolf-Second order sliding mode control (GW-SoSMC) to control the manipulator of the inchworm robot. The GW-SoSMC reduces the chattering phenomenon of SMC and improves the controlling ability of SoSMC by weightage function. Subsequently, it compares the performance of the proposed method with several conventional techniques like Grey Wolf-SMC (GW-SMC), FireFly-SoSMC (FF-SoSMC), Artificial Bee Colony-SoSMC (ABC-SoSMC), Group Searching-SoSMC (GS-SoSMC), and Genetic Algorithm-SoSMC (GA-SoSMC). It portrays the valuable comparative analysis by measuring the accomplished joint angles, error, and response of the controller. Thus the proposed method discovers the supervisory controller for the inchworm robot that is immensely better than conventional controllers mentioned earlier.

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Descriptor sliding mode approach for fault/noise reconstruction and fault-tolerant control of nonlinear uncertain systems

Cited by 32 publications

References 39 publications

Active Fault-Tolerant Control for Wind Turbine with Simultaneous Actuator and Sensor Faults

Active Fault-Tolerant Control for Wind Turbine with Simultaneous Actuator and Sensor Faults

Multi-packet transmission aero-engine DCS neural network sliding mode control based on multi-kernel LS-SVM packet dropout online compensation

Grey Wolf Optimization-Based Second Order Sliding Mode Control for Inchworm Robot

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