Vibration control and boundary tension constraint of an axially moving string system

Zhao, Zhijia; Liu, Yu; Guo, Fang; Fu, Yun

doi:10.1007/s11071-017-3595-x

Cited by 43 publications

(26 citation statements)

References 38 publications

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“…It is easily seen that the increase in the control gain k and disturbance observer design parameters 1 and 2 will result in larger 3 ∼ 5 , which will lead a greater 3 . Then the value of will increase, which will decrease the value of z(x, t) in (43) and produce a better disturbance rejection control performance. However, increasing the control gain k will bring a high gain control scheme.…”

Section: Proof Applying Lemma 1 Onmentioning

confidence: 99%

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Disturbance Rejection Control for a Vibrating String System

Zhao

Xiao

et al. 2018

Asian Journal of Control

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This paper is concerned with disturbance rejection control for a vibrating flexible string system in the presence of unknown disturbances. First, the boundary disturbance observer is proposed to deal with the boundary disturbance and the infinite dimensional disturbance observer is introduced to mitigate the effects of the distributed disturbance. Subsequently, the boundary control and distributed control are developed to suppress the vibration and globally stabilize the string system at its equilibrium position. Under the control proposed, the uniformly bounded stability of the controlled system is testified employing rigorous analysis without resorting to discretization of the partial differential equation dynamics depending on the time and space. Finally, the simulation and comparison results verify the performance of the derived control.

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Section: Proof Applying Lemma 1 Onmentioning

confidence: 99%

“…In this section, we make use of finite difference method [43][44][45] to approximate the numerical solution of the flexible string system. The parameters of the flexible string system are: = 0.1 kg/m, m = 2.0 kg, L = 1.0m, and T = 5N, and the initial conditions are:…”

Section: Simulationsmentioning

confidence: 99%

Disturbance Rejection Control for a Vibrating String System

Zhao

Xiao

et al. 2018

Asian Journal of Control

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“…Ant colony algorithm easily leads to premature and local convergences. To solve this problem and improve the global convergence of ant colony algorithm and search speed, many studies proposed different strategies to update existing information [19][20][21][22][23][24][25]; for example, in the standard ant colony algorithm, when information is updated and as long as the ant is traversed, selecting the path can update the information on the path. This enhances the information on the path for optimal fitness and weakens the information on other paths; the other algorithms based on level change enable the ant to set several paths with relatively good fitness, and the degree of excellence of its solution determines the magnitude of information.…”

Section: Adaptive Information Update Strategymentioning

confidence: 99%

Multiconstrained Network Intensive Vehicle Routing Adaptive Ant Colony Algorithm in the Context of Neural Network Analysis

Chen

Yang

et al. 2017

Complexity

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Neural network models have recently made significant achievements in solving vehicle scheduling problems. Adaptive ant colony algorithm provides a new idea for neural networks to solve complex system problems of multiconstrained network intensive vehicle routing models. The pheromone in the path is changed by adjusting the volatile factors in the operation process adaptively. It effectively overcomes the tendency of the traditional ant colony algorithm to fall easily into the local optimal solution and slow convergence speed to search for the global optimal solution. The multiconstrained network intensive vehicle routing algorithm based on adaptive ant colony algorithm in this paper refers to the interaction between groups. Adaptive transfer and pheromone update strategies are introduced based on the traditional ant colony algorithm to optimize the selection, update, and coordination mechanisms of the algorithm further. Thus, the search task of the objective function for a feasible solution is completed by the search ants. Through the division and collaboration of different kinds of ants, pheromone adaptive strategy is combined with polymorphic ant colony algorithm. It can effectively overcome some disadvantages, such as premature stagnation, and has a theoretical significance to the study of large-scale multiconstrained vehicle routing problems in complex traffic network systems.

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“…A model reduction approach is employed in different ways to extract a finite‐dimensional subsystem to be controlled while showing robustness to neglecting the remaining infinite dimensional dynamics in the design , while boundary control is to conduct control design based on infinite dimensional system dynamical model, and it is generally considered to be physically more realistic due to nonintrusive actuation and sensing . In the past decades, remarkable results have been obtained regarding the dynamical analysis and boundary control scheme design for the string‐type structure . The control design for a flexible cable with output constraint and tension constraint is presented in , where the barrier Lyapunov function is used for ensuring performance or safety specifications.…”

Section: Introductionmentioning

confidence: 99%

Boundary control for a flexible string system with input saturation constraint

Zhao

Ren

2018

Asian Journal of Control

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In this study, we focus on the boundary control problem for a vibrating string system with saturated input under the condition of external disturbances. Based on the backstepping approach, a boundary vibration control scheme is proposed to globally stabilize the string around the equilibrium position. A smooth hyperbolic tangent function is exploited to restrict the control input, an auxiliary system and a Nussbaum function are adopted to cope with the nonlinear term derived from the input saturation, and a disturbance observer is employed to tackle the boundary disturbance. The developed controller can assure the convergence of the closed‐loop system state to a small neighbourhood of zero. By the appropriate choice of control design parameters, numerical simulation is conducted to show the effectiveness of the derived control.

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Vibration control and boundary tension constraint of an axially moving string system

Cited by 43 publications

References 38 publications

Disturbance Rejection Control for a Vibrating String System

Disturbance Rejection Control for a Vibrating String System

Multiconstrained Network Intensive Vehicle Routing Adaptive Ant Colony Algorithm in the Context of Neural Network Analysis

Boundary control for a flexible string system with input saturation constraint

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