Distributed adaptive control for vehicular platoon with unknown dead‐zone inputs and velocity/acceleration disturbances

Guo, Xiang-Gui; Wang, Jianliang; Liao, Fang; Teo, Rodney

doi:10.1002/rnc.3720

Cited by 19 publications

(25 citation statements)

References 46 publications

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“…Remark Comparison of our previous results in the work of Guo et al three points should be highlighted. First of all, the requirement of matching conditions and satisfaction of some Lipschitz constraints for the uncertain nonlinear functions f i ( v i , a i , t ) in the aforementioned work are removed in this paper. Second, the deadzone nonlinearity and mismatched quantization simultaneously considered lead to more complexity control design.…”

Section: Neuroadaptive Quantized Pidsm Control Design and String Stabmentioning

confidence: 54%

“…The unknown input deadzone nonlinearity

D_{i} false(u_{i}^{q} false)

can be described by

u_{i}^{d} = ({, \begin{matrix} array n_{i}^{r} (u_{i}^{q} - b_{i}^{r}), & array if u_{i}^{q} \geq b_{i}^{r} \\ array 0, & array if - b_{i}^{l} < u_{i}^{q} < b_{i}^{r} \\ array n_{i}^{l} (u_{i}^{q} + b_{i}^{l}), & array if u_{i}^{q} \leq - b_{i}^{l}, \end{matrix})

where

n_{i}^{r}

and

n_{i}^{l}

stand for the right and the left slope of the deadzone characteristic, respectively, and the parameters

b_{i}^{r}

and

b_{i}^{l}

represent the breakpoints of the input nonlinearity. The unknown deadzone model with the input

u_{i}^{q}

is shown in Figure .…”

Section: Vehicle‐following Platoon Model and Preliminariesmentioning

confidence: 99%

“…In order to achieve string stability, most of the above‐mentioned literature always requires that the initial spacing and velocity (sometimes also including acceleration) errors be zero. This is clearly undesirable as the initial condition uncertainties may lead to string instability . Moreover, in most existing results on string stability, reducing a nonlinear system to a linear one via feedback linearization is extensively used to simplify the control problem .…”

Section: Introductionmentioning

confidence: 99%

“…"> 3.In order to improve the road capacity and reduce congestion, and simultaneously remove the common restriction of zero initial spacing, velocity, and acceleration errors in other works, a new modified constant time headway (MCTH) policy is proposed. This policy is an obvious extension compared with the one in the work of Guo et al 4.It is known that good approximated performance requires more NN nodes.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Neuroadaptive quantized PID sliding‐mode control for heterogeneous vehicular platoon with unknown actuator deadzone

Guo

Wang

Liao

et al. 2018

Intl J Robust & Nonlinear

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Summary This paper focuses on the problem of neuroadaptive quantized control for heterogeneous vehicular platoon when the follower vehicles suffer from external disturbances, mismatch input quantization, and unknown actuator deadzone. The PID‐based sliding‐mode (PIDSM) control technique is used due to its superior capability to reduce spacing errors and to eliminate the steady‐state spacing errors. Then, a neuroadaptive quantized PIDSM control scheme with minimal learning parameters is designed not only to guarantee the string stability of the whole vehicular platoon and ultimate uniform boundedness of all adaptive law signals but also to attenuate the negative effects caused by external disturbance, mismatch input quantization, and unknown actuator deadzone. Furthermore, optimizing the interspacing between consecutive vehicles is very important to reduce traffic congestion on highways, and a new modified constant time headway policy is proposed to not only increase traffic density but also address the negative effect of nonzero initial spacing, velocity, and acceleration errors. Compared with most existing methods, the proposed method does not linearize the system model and neither requires precise knowledge of the system model. Finally, the effectiveness and advantage of the proposed method are demonstrated by comparative simulation studies.

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Section: Neuroadaptive Quantized Pidsm Control Design and String Stabmentioning

confidence: 54%

“…The unknown input deadzone nonlinearity

D_{i} false(u_{i}^{q} false)

can be described by

u_{i}^{d} = ({, \begin{matrix} array n_{i}^{r} (u_{i}^{q} - b_{i}^{r}), & array if u_{i}^{q} \geq b_{i}^{r} \\ array 0, & array if - b_{i}^{l} < u_{i}^{q} < b_{i}^{r} \\ array n_{i}^{l} (u_{i}^{q} + b_{i}^{l}), & array if u_{i}^{q} \leq - b_{i}^{l}, \end{matrix})

where

n_{i}^{r}

and

n_{i}^{l}

stand for the right and the left slope of the deadzone characteristic, respectively, and the parameters

b_{i}^{r}

and

b_{i}^{l}

represent the breakpoints of the input nonlinearity. The unknown deadzone model with the input

u_{i}^{q}

is shown in Figure .…”

Section: Vehicle‐following Platoon Model and Preliminariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Neuroadaptive quantized PID sliding‐mode control for heterogeneous vehicular platoon with unknown actuator deadzone

Guo

Wang

Liao

et al. 2018

Intl J Robust & Nonlinear

Self Cite

View full text Add to dashboard Cite

show abstract

“…And it has been used to solve many kinds of control problems. Such as, SMC of Markov jump systems is discussed in Song et al and Zhu et al Choi et al and Guo et al study the SMC design for uncertain autonomous vehicles. In Li et al and Zhang et al, SMC approach is used to deal with T‐S fuzzy uncertain control systems.…”

Section: Introductionmentioning

confidence: 99%

Quantized sliding mode control in delta operator framework

Zheng

Xue

2017

Intl J Robust & Nonlinear

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Summary This paper is concerned with quantized sliding mode control in the unified delta operator system framework. To solve the quantization measurement saturating problem, a dynamic quantization strategy including discrete on‐line open‐loop zooming‐out and closed‐loop zooming‐in policies is presented. By analyzing the sign relation between the traditional linear switching function and the quantized linear switching function, a novel quantized sliding mode control method is proposed, and both the amplitude of the control gain and the value of the quantization measurement saturating parameter are reduced compared with previous results. Some simulation results are presented to verify the effectiveness of the proposed method.

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Distributed H∞ fuzzy filtering for nonlinear systems interconnected over graphs

Xue

2022

Adaptive Control & Signal

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This article investigates the distributed fuzzy H ∞ filtering problem for a class of Takagi-Sugeno (T-S) fuzzy model-based nonlinear systems interconnected over an undirected graph. The system we consider consists of numbers of heterogeneous nonlinear sub-units interconnected over an undirected graph by sensing, computing, and communicating with each other. First, the system is represented by an undirected graph, a T-S fuzzy model-based state-space equation of each subsystem and an interconnection condition. Based on this model, the concepts of the well-posedness, stability, and contractiveness for the class of systems are introduced, and the distributed fuzzy filtering problem is established. By applying membership-dependent multi-Lyapunov functions, a sufficient condition on the well-posedness, stability, and contractiveness of the open-loop plant is then derived in terms of linear matrix inequalities (LMIs). And then, a distributed fuzzy filter inheriting the interconnected structure of the plant is designed such that the filtering error system is contractive, and a sufficient condition is correspondingly given to obtain the desired fuzzy filter parameters by solving a set of LMIs. Finally, a numerical simulation is exploited to show the validity of the proposed design method.

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Distributed adaptive control for vehicular platoon with unknown dead‐zone inputs and velocity/acceleration disturbances

Cited by 19 publications

References 46 publications

Neuroadaptive quantized PID sliding‐mode control for heterogeneous vehicular platoon with unknown actuator deadzone

Neuroadaptive quantized PID sliding‐mode control for heterogeneous vehicular platoon with unknown actuator deadzone

Quantized sliding mode control in delta operator framework

Distributed H∞ fuzzy filtering for nonlinear systems interconnected over graphs

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