Co-designed of network scheduling and sliding mode control for underwater shuttle based on adaptive genetic algorithm

Li, Zeyu; Liu, Weidong; Li, Le; Li, Zhi; Zhang, Feihu

doi:10.1108/aa-11-2017-149

Cited by 5 publications

(3 citation statements)

References 30 publications

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“…As the AUV attitude is controlled by the rudders and propeller, the motion also has kinematic constraints in horizontal plane. The linear equation is referred from [40] as follows:…”

Section: B Kinematic Cosntraintmentioning

confidence: 99%

Path Planning Method for AUV Docking Based on Adaptive Quantum-Behaved Particle Swarm Optimization

Liu

Gao

et al. 2019

IEEE Access

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AUV docking requires the platform to have both wide range cruising and accurate operating abilities, to against challenges of ocean currents, obstacles, and constraints. This paper proposed an evolutionary-based method, for the purpose of docking path optimization. First, the ocean environment and constraints are analyzed and modeled. Next, the control points are designed to satisfy the model constraints. Then, the adaptive law and quantum behavior are introduced in particle swarm optimization (PSO), to achieve global time-optimization. Finally, the proposed approach is evaluated via Monte-Carlo trials, which demonstrates a significant improvement with respect to the state-of-the-art approaches.INDEX TERMS AUV docking, path optimization, quantum behaved evolutionary algorithm.

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“…As the AUV attitude is controlled by the rudders and propeller, the motion also has kinematic constraints in horizontal plane. The linear equation is referred from [40] as follows:…”

Section: B Kinematic Cosntraintmentioning

confidence: 99%

Path Planning Method for AUV Docking Based on Adaptive Quantum-Behaved Particle Swarm Optimization

Liu

Gao

et al. 2019

IEEE Access

Self Cite

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“…As industrial systems tend to be of the characteristics of nonlinearity, time-delay and coupling, etc., large overshoots occur in most cases or even lead to the control system instability (Li et al , 2018; Kaldma et al , 2020; Ma et al , 2020)[1]. So, control of nonlinear time-delay systems has become one of the focuses in the control community (Aryankia and Selmic, 2021; Zheng et al , 2018; Wang et al , 2017; Zhao et al , 2017; Yu et al , 2020; Cui et al , 2020), and they have been used in many fields, such as chemical field (Jia et al , 2017) and intelligence system (Sharma et al , 2011).…”

Section: Introductionmentioning

confidence: 99%

MTN-based recursive d-step-ahead predictive control of MIMO nonlinear systems with unknown input time-delay in industrial process

Yan

Li²

2022

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Purpose This paper aims to provide a precise tracking control scheme for multi-input multi-output “MIMO” nonlinear systems with unknown input time-delay in industrial process. Design/methodology/approach The predictive control scheme based on multi-dimensional Taylor network (MTN) model is proposed. First, for the unknown input time-delay, the cross-correlation function is used to identify the input time-delay through just the input and output data. And then, the scheme of predictive control is designed based on the MTN model. It goes as follows: a recursive d-step-ahead MTN predictive model is developed to compensate the influence of time-delay, and the extended Kalman filter (EKF) algorithm is applied for its learning; the multistep predictive objective function is designed, and the optimal controlled output is determined by iterative refinement; and the convergence of MTN predictive model and the stability of closed-loop system are proved. Findings Simulation results show that the proposed scheme is of desirable generality and capable of performing the tracking control for MIMO nonlinear systems with unknown input time-delay in industrial process effectively, such as the continuous stirred tank reactor (CSTR) process, which provides a considerably improved performance and effectiveness. The proposed scheme promises strong robustness, low complexity and easy implementation. Research limitations/implications For the limitations of proposed scheme, the time-invariant time-delay is only considered in time-delay identification and control schemes. And the CSTR process is only introduced to prove that the proposed scheme can adapt to practical industrial scenario. Originality/value The originality of the paper is that the proposed MTN control scheme has good tracking performance, which solves the influence of time-delay, coupling and nonlinearity and the real-time performance for MIMO nonlinear systems with unknown input time-delay.

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“…Human-robot interaction (HRI) refers to the process of communication between a user and a robot, in which the user interacts with the robot via a certain “dialogue” language (Sloman, 2009; Nishiguchi et al, 2017; Khan et al, 2018). To date, HRI methods have been widely applied to the design of service robots, amusement robots, healthcare robots, and so on (Royakkers and van Est, 2015; Li et al, 2018; Zhang et al, 2019). Those robots are able to perfectly execute interactions with the user; however, they have a limited ability to adjust the interaction mode in response to the user's psychological states owing to their weak emotion perception (Pessoa, 2017; Cavallo et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Adolescents Environmental Emotion Perception by Integrating EEG and Eye Movements

et al. 2019

Front. Neurorobot.

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Giving a robot the ability to perceive emotion in its environment can improve human-robot interaction (HRI), thereby facilitating more human-like communication. To achieve emotion recognition in different built environments for adolescents, we propose a multi-modal emotion intensity perception method using an integration of electroencephalography (EEG) and eye movement information. Specifically, we first develop a new stimulus video selection method based on computation of normalized arousal and valence scores according to subjective feedback from participants. Then, we establish a valence perception sub-model and an arousal sub-model by collecting and analyzing emotional EEG and eye movement signals, respectively. We employ this dual recognition method to perceive emotional intensities synchronously in two dimensions. In the laboratory environment, the best recognition accuracies of the modality fusion for the arousal and valence dimensions are 72.8 and 69.3%. The experimental results validate the feasibility of the proposed multi-modal emotion recognition method for environment emotion intensity perception. This promising tool not only achieves more accurate emotion perception for HRI systems but also provides an alternative approach to quantitatively assess environmental psychology.

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Co-designed of network scheduling and sliding mode control for underwater shuttle based on adaptive genetic algorithm

Cited by 5 publications

References 30 publications

Path Planning Method for AUV Docking Based on Adaptive Quantum-Behaved Particle Swarm Optimization

Path Planning Method for AUV Docking Based on Adaptive Quantum-Behaved Particle Swarm Optimization

MTN-based recursive d-step-ahead predictive control of MIMO nonlinear systems with unknown input time-delay in industrial process

Adolescents Environmental Emotion Perception by Integrating EEG and Eye Movements

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