Predictor-Based Disturbance Rejection Control for Sampled Systems With Input Delay

Liu, Tao; Hao, Shoulin; Li, Dewei; Chen, Wenhua; Wang, Qing‐Guo

doi:10.1109/tcst.2017.2781651

Cited by 47 publications

(26 citation statements)

References 24 publications

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“…Assuming that the control input u(t) is bounded, that is u(t) ≤ u d . Consider the nonlinear system Equation (11) and the present adaptive RBF neural network observer system Equation (24), Equation (22) and Equation (34), then the state estimated error x(t) and the neural network weight estimated errors W 1 (t) and W 2 (t) are UUB. See the Appendix B for the proof.…”

Section: Assumption 2 [43]mentioning

confidence: 99%

“…The predictive approach is very efficient for calculating the time delay [7,8]. To predict the state parameter precisely, different predictive methods have been investigated, such as the Smith predictive method [9][10][11], a Kalman estimator [12,13], feed-forward control [14], reference model based control [15,16], and a state space observer approach [17,18]. Thurling and Greene [19] gave a detailed description of the composition of the delay of a low speed unmanned aerial vehicle (UAV) remotely piloted system and analyzed the result of time delay impacting on the handling qualities; they described how the greater time delay could cause the pilot to over-operate the UAV and even lead to UAV instability.…”

Section: Introductionmentioning

confidence: 99%

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Universal Adaptive Neural Network Predictive Algorithm for Remotely Piloted Unmanned Combat Aerial Vehicle in Wireless Sensor Network

Fang

Fan

et al. 2020

Sensors

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Remotely piloted unmanned combat aerial vehicle (UCAV) will be a prospective mode of air fight in the future, which can remove the physical restraint of the pilot, maximize the performance of the fighter and effectively reduce casualties. However, it has two difficulties in this mode: (1) There is greater time delay in the network of pilot-wireless sensor-UCAV, which can degrade the piloting performance. (2) Designing of a universal predictive method is very important to pilot different UCAVs remotely, even if the model of the control augmentation system of the UCAV is totally unknown. Considering these two issues, this paper proposes a novel universal modeling method, and establishes a universal nonlinear uncertain model which uses the pilot’s remotely piloted command as input and the states of the UCAV with a control augmentation system as output. To deal with the nonlinear uncertainty of the model, a neural network observer is proposed to identify the nonlinear dynamics model online. Meanwhile, to guarantee the stability of the overall observer system, an adaptive law is designed to adjust the neural network weights. To solve the greater transmission time delay existing in the pilot-wireless sensor-UCAV closed-loop system, a time-varying delay state predictor is designed based on the identified nonlinear dynamics model to predict the time delay states. Moreover, the overall observer-predictor system is proved to be uniformly ultimately bounded (UUB). Finally, two simulations verify the effectiveness and universality of the proposed method. The results indicate that the proposed method has desirable performance of accurately compensating the time delay and has universality of remotely piloting two different UCAVs.

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Section: Assumption 2 [43]mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Universal Adaptive Neural Network Predictive Algorithm for Remotely Piloted Unmanned Combat Aerial Vehicle in Wireless Sensor Network

Fang

Fan

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…It is worth mentioning that the existing error between the exact and the approximated predictions in a disturbed system cannot be removed, even in case of time-constant disturbances using integral action [34]. To overcome this limitation, predictor-feedback approaches were recently combined with Extended State Observer (ESO) to actively reject external disturbances in the presence of time-constant delays [25,17], and further extended to timevarying delays [14]. These last works considered disturbances that affect the state through channels in which the input has no direct influence (mismatched disturbances), which are generally more difficult to handle for disturbance rejection purposes [3].…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…Note that the matrix inequalities (25) are not linear if the control and observer gains K and L are defined as LMI decision variables. Therefore, the problem of control synthesis by means of Theorem (1) or (28) renders non convex.…”

Section: Control Synthesismentioning

confidence: 99%

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Event-triggered predictor-based control with gain-Scheduling and extended state observer for networked control systems

González

Cuenca

Balaguer

et al. 2019

Information Sciences

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Anti‐windup design of active disturbance rejection control for sampled systems with input delay

Geng

Liu

Hao

et al. 2019

Intl J Robust & Nonlinear

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SummaryA novel anti‐windup design of active disturbance rejection control (ADRC) is proposed for industrial sampled systems with input delay and saturation. By using a generalized predictor to estimate the delay‐free system output, a modified extended state observer is designed to simultaneously estimate the system state and disturbance, which could become an anti‐windup compensator when the input saturation occurs. Accordingly, a feedback controller is analytically designed for disturbance rejection. By proposing the desired closed‐loop transfer function for the set‐point tracking, a prefilter is designed to tune the tracking performance while guaranteeing no steady‐state output tracking error. A sufficient condition for the closed‐loop system stability is established with proof for practical application subject to the input delay variation. Illustrative examples from the literature are used to demonstrate the effectiveness and merit of the proposed control design.

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Predictor-Based Disturbance Rejection Control for Sampled Systems With Input Delay

Cited by 47 publications

References 24 publications

Universal Adaptive Neural Network Predictive Algorithm for Remotely Piloted Unmanned Combat Aerial Vehicle in Wireless Sensor Network

Universal Adaptive Neural Network Predictive Algorithm for Remotely Piloted Unmanned Combat Aerial Vehicle in Wireless Sensor Network

Event-triggered predictor-based control with gain-Scheduling and extended state observer for networked control systems

Anti‐windup design of active disturbance rejection control for sampled systems with input delay

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