Quantized feedback sliding-mode control: An event-triggered approach

Zheng, Bo‐Chao; Yu, Xinghuo; Xue, Yuncan

doi:10.1016/j.automatica.2018.01.007

Cited by 125 publications

(52 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(H1) in Assumption is mild here in that we allow a large enough

{\overset{f}{true}}_{k}

. Different from the work of Zheng et al, as it will be shown in the design in Section 3,

{\overset{f}{true}}_{k}

is not contained in the control input. (H2) in Assumption is commonly used in the literature for ESO design if one targets the zero steady‐state estimation error .…”

Section: Problem Formulationmentioning

confidence: 99%

“…(ii) The time‐triggered update policy of quantizer provided in other works becomes inefficient because, in the presence of switches and disturbances, the explicit calculation of time period between any two updates is hard and conservative. (iii) The online detection of quantizer saturation is expected to avoid due to its drawbacks and the disturbance can lead to nonzero steady‐state errors as obtained in the aforementioned works …”

Section: Introductionmentioning

confidence: 99%

“…Thus, the disturbance in the finite‐level quantization design becomes a challenge to be addressed. For this objective, the results in the works of Ren and Xiong and Zheng et al extend the approach in the work of Liberzon but need to directly detect the quantizer saturation online periodically. For systems with both switches and disturbances, the recent work of Yang and Liberzon uses the approach of reachable‐set approximation and propagation from the work of Liberzon to design a dynamic quantization algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…(iii) The online detection of quantizer saturation is expected to avoid due to its drawbacks and the disturbance can lead to nonzero steady-state errors as obtained in the aforementioned works. [24][25][26] Motivated by the above discussion, we aim to solve the stabilization problem for a switched system with disturbances and finite-level quantized output. Based on the estimates of state and disturbance from an extended state current estimator (ESCE), a quantized output feedback controller (QOFC) is presented at first.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Stabilization of switched nonlinear systems with dynamic output quantization and disturbances

Lian

2020

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

Summary This paper studies the problem of quantized output feedback stabilization for a disturbed discrete‐time switched system. With the quantized output measurement, an extended state current estimator is constructed to estimate the state and disturbance. In the presence of switches and disturbances, the design of quantization scheme becomes a big challenge to avoid the quantizer saturation and guarantee the control precision. In this setup, the well‐applied time‐triggered method to design the update policy of dynamic quantization parameter is hard to implement. We solve the above problem by proposing a novel event‐triggered update policy of quantization parameter, by which the quantizer update is adaptive to the switching signal and the bound of disturbance difference. Consequently, the quantizer saturation is avoided and, combined with the designed dwell‐time switching law, the system state can converge to the origin. The proposed method is illustrated by a numerical example.

show abstract

“…(H1) in Assumption is mild here in that we allow a large enough

{\overset{f}{true}}_{k}

. Different from the work of Zheng et al, as it will be shown in the design in Section 3,

{\overset{f}{true}}_{k}

is not contained in the control input. (H2) in Assumption is commonly used in the literature for ESO design if one targets the zero steady‐state estimation error .…”

Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Stabilization of switched nonlinear systems with dynamic output quantization and disturbances

Lian

2020

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

show abstract

“…In reference [30], the variable index approach law is applied to sliding-mode variable structure control, which can effectively reduce the chattering of the system at one time. However, these papers mentioned above only consider how to reduce chattering, without taking the overall performance optimization of sliding-mode control into consideration, and there is no influence of packet dropout and time-delay on the research objects [31].…”

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

View full text Add to dashboard Cite

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.

show abstract

Output‐feedback‐based sliding mode control for networked control systems subject to packet loss and quantization

Niu

2019

Asian Journal of Control

View full text Add to dashboard Cite

In this paper, observer‐based sliding mode control is considered for networked control systems subject to quantization, in which packet dropout may happen when the measurement output is transmitted from the sensor to the controller. Firstly, a compensating scheme is proposed to deal with the effect of packet loss modelled as a Bernoulli process. Then, by means of available output information and state observer, the desirable sliding mode controller is designed. Furthermore, both the reachability and the stochastic stability of sliding mode dynamics can be attained and the corresponding sufficient conditions are derived. Finally, numerical simulation results are provided to illustrate the proposed control law.

show abstract

Quantized feedback sliding-mode control: An event-triggered approach

Cited by 125 publications

References 32 publications

Stabilization of switched nonlinear systems with dynamic output quantization and disturbances

Stabilization of switched nonlinear systems with dynamic output quantization and disturbances

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

Output‐feedback‐based sliding mode control for networked control systems subject to packet loss and quantization

Contact Info

Product

Resources

About