Artificial Potential-Based Adaptive ${{H}_{\infty }}$ Synchronized Tracking Control for Accommodation Vessel

Wen, Guoxing; Ge, Shuzhi Sam; Tu, Fangwen; Choo, Yoo Sang

doi:10.1109/tie.2017.2677330

Cited by 45 publications

(24 citation statements)

References 25 publications

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“…Its basic idea is to fill the working environment with a predefined APF. Recently, researchers have made many breakthroughs in multi-agent formation with APFs [23][24][25]. Wen et al [23] applied the APF method to floating production storage and offloading-accommodation vessel systems for smooth gangway operation.…”

Section: Introductionmentioning

confidence: 99%

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Formation control with obstacle avoidance of second-order multi-agent systems under directed communication topology

Wen

Chen

Dou

et al. 2019

Sci. China Inf. Sci.

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This paper addresses the obstacle avoidance problem of formation control for the multi-agent systems modeled by double integrator dynamics under a directed interconnection topology. The control task is finished by a leader-follower formation scheme combined with an artificial potential field (APF) method. The leader-follower scheme is carried out by taking the desired trajectory with the desired velocity as virtual leader, while the APF method is carried out by dealing with the obstacles as the high potential points. When the obstacle avoidance tasks are finished, the artificial potential forces degrade the formation performance, so their undesired effects are treated as disturbances, which is analyzed by the robust H∞ performance. Based on Lyapunov stability theory, it is proved that the proposed formation approach can realize the control objective. The result is also extended to the switching multi-agent formation. The effectiveness of the proposed formation scheme is further confirmed by simulation studies.

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Section: Introductionmentioning

confidence: 99%

“…Recently, researchers have made many breakthroughs in multi-agent formation with APFs [23][24][25]. Wen et al [23] applied the APF method to floating production storage and offloading-accommodation vessel systems for smooth gangway operation. In [24], APF method and behavior rule were combined for target tracking and obstacle avoidance.…”

Section: Introductionmentioning

confidence: 99%

Formation control with obstacle avoidance of second-order multi-agent systems under directed communication topology

Wen

Chen

Dou

et al. 2019

Sci. China Inf. Sci.

Self Cite

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“…In [14], a constrainedinput system is tackled in combination with the optimal control to ensure a good tradeoff between control performance and energy consumption. For output constraints, artificial potential field [15], prescribed performance control [16,17], model predictive control [18], and reference governor [19] are some of the existing strategies to handle this problem. In [20,21], Barrier Lyapunov Function (BLF) is introduced which needs less initial conditions and does not require explicit system solution.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Based Central Heating System Load Prediction and Constrained Control

Wang

Tu³

et al. 2018

Mathematical Problems in Engineering

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A neural network (NN) based heating system load prediction and control scheme are proposed. Different from traditional physical principle based load calculation method, a multilayer NN is incorporated with selected input features and trained to predict the heating load as well as the desired supply water temperature in heating supply loop. In this manner, a complicated load calculation model can be replaced by simple but efficient data-driven scheme and the response time to outdoor temperature variation can be enhanced. Moreover, in order to handle the input and output constraints in valve opening degree control task to achieve desired supply water temperature, Barrier Lyapunov candidate function and axillary system technique are involved. An additional NN is employed to approximate the system transfer function with reliable accuracy. The stability of the system is guaranteed through rigorous mathematical analysis. The excellent performance of the novelly proposed control over traditional PID is demonstrated via extensive simulation study. A quantitative case study is also conducted to verify the flexibility and validity of proposed load prediction strategy.

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“…Therefore, in this work, the output tracking error constraints are novelly taken into consideration for the control design. Some of the existing researches offer good results such as the ones using artificial potential field [11], prescribed performance control [12,13], model predictive control [14] as well as reference governor [15]. Additionally, Barrier Lyapunov Function (BLF) is introduced in [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

“…Finally, the external disturbance, model uncertainty error, and other unknown factors are modeled as a disturbance term complemented into the system dynamic model. Different from a robust control [11,19], in this paper, we design an NN [20] observer to estimate the disturbance to achieve more accurate control and attenuate the noise. The overall system diagram of the proposed central heating control system is summarized in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Central Heating System Constrained Control with Input Delay Based on Neural Networks

Wang

Guo

et al. 2018

Mathematical Problems in Engineering

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An output constrained control with input delay is proposed for a central heating system. Due to the delay of signal transmission and valves opening time, an input delay is considered into the system and an auxiliary system is employed to handle this issue by converting the delayed input into a delay-free one. Moreover, to ensure the output supply water temperature within a limited range, Barrier Lyapunov algorithm is involved to achieve desired control accuracy. Finally, external disturbance and model uncertainty are incorporated into the dynamic system and neural networks (NN) are trained in an online fashion for the compensation. The stability of the control system is guaranteed through rigorous Lyapunov analysis and the excellent control performance over traditional PID control is demonstrated via numerical simulation study.

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Artificial Potential-Based Adaptive ${{H}_{\infty }}$ Synchronized Tracking Control for Accommodation Vessel

Cited by 45 publications

References 25 publications

Formation control with obstacle avoidance of second-order multi-agent systems under directed communication topology

Formation control with obstacle avoidance of second-order multi-agent systems under directed communication topology

Neural Network Based Central Heating System Load Prediction and Constrained Control

Central Heating System Constrained Control with Input Delay Based on Neural Networks

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