Boundary Control for a Vibrating String System with Bounded Input

Zhao, Zhijia; Liu, Yu; Luo, Fei

doi:10.1002/asjc.1555

Cited by 20 publications

(13 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…At present, some methods, such as linear matrix inequality (LMI) [22][23][24] and the hyperbolic tangent function [25][26][27][28], have been used to deal with the control input constraint problem. In [23], an anti-windup compensator was proposed, which utilized LMI to deal with the input saturation problem for linear systems.…”

Section: Introductionmentioning

confidence: 99%

Backstepping control of flexible joint manipulator based on hyperbolic tangent function with control input and rate constraints

Wang

Shi

Liu

et al. 2018

Asian Journal of Control

View full text Add to dashboard Cite

In this paper, we investigate the trajectory tracking problems of the link angle and angle speed of the flexible joint manipulator model based on external disturbance, the control input and rate constraints. The controller of the flexible joint manipulator model is designed using the backstepping control scheme.To achieve this objective, the smooth hyperbolic tangent function is used to solve the problems of control input and rate constraints, and the stability is proved using Lyapunov function in the design procedure of the backstepping control scheme. Finally, the effectiveness of the proposed backstepping controller is verified by numerical simulation. of flexible joint manipulator based on hyperbolic tangent function with control input and rate constraints. Asian J

show abstract

“…Partial differential equations are more complex forms of differential equations for modeling problems in physics, mathematics, and engineering. They demonstrate the behavior of phenomena with more details and provide better understanding . Optimal control was an active field in PDE control problems.…”

Section: Introductionmentioning

confidence: 99%

A PDE breach to the SDRE

Nekoo¹

2018

Asian Journal of Control

View full text Add to dashboard Cite

The state‐dependent Riccati equation (SDRE) is a nonlinear optimal controller derived from applying optimality conditions on a Hamiltonian equation. A co‐state vector is involved in the derivation process. This has been commonly considered a function of time only, despite the existence of states in the co‐state vector. This has resulted in a series of nonlinear coupled ordinary differential equations (ODEs) with a final boundary condition, known as the SDRE. In this work, for the first time, the co‐state vector is regarded as a function of time and states that results in a partial differential equation (PDE) instead of an ODE. The new governing equation is named partial differential state‐dependent Riccati equation (PDSDRE), and the PDE provides a tensor for gain over domains of time and states. Since the generated PDE is highly nonlinear, the solution to the PDSDRE is proposed based on the method of lines (MOL), which is an extension to the finite difference method (FDM). The proposed approach is implemented on both scalar and second order systems and is compared with an SDRE technique to validate the results and show the advantages of proposed structure.

show abstract

Boundary Control for a Vibrating String System with Bounded Input

Cited by 20 publications

References 51 publications

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

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

Backstepping control of flexible joint manipulator based on hyperbolic tangent function with control input and rate constraints

A PDE breach to the SDRE

Contact Info

Product

Resources

About