Advanced Control for Applications: Engineering and Industrial Systems

Journal Information
ISSN / EISSN : 2578-0727 / 2578-0727
Current Publisher: Wiley (10.1002)
Total articles ≅ 37
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Asuma Minowa,
Advanced Control for Applications: Engineering and Industrial Systems; doi:10.1002/adc2.77

Abstract:
This paper presents stability analyses on a towed underwater vehicle (TUV) motion control system using movable wings. The control method utilized is an output-feedback controller based on linear state-feedback control and a high-gain observer. In order to analyze the closed-loop stability of the system, the singular perturbation method is employed. Not only the asymptotic stability of the system is proved, but also an estimate of its region of attraction is derived. To describe a region of attraction, we propose a method by employing a hyper rhombus, which is quite suitable for quadratic Lyapunov functions. Further, to obtain a less conservative estimate, a state-space-scaling method is devised and its efficacy is evaluated by comparing with the conventional estimation method. Then, results of regulation simulations with various initial conditions are provided, which explores the consistency and gap between the theoretical estimate and the simulation results, and the robustness of the control system. Finally, a depth-tracking control system via switching controllers is constructed according to the results of the stability analysis which investigates configuration of multiple equilibria with different depths and their regions of attraction, and demonstration simulation results are presented to illustrate the validity of the perspective given by the stability analysis.
, D. V. Rama Koti Reddy
Advanced Control for Applications: Engineering and Industrial Systems; doi:10.1002/adc2.57

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, Mohammed Sami Fadali
Advanced Control for Applications: Engineering and Industrial Systems, Volume 2; doi:10.1002/adc2.55

Abstract:
This study proposes using a wavelet neural network (WNN) with a feedforward component and a model predictive controller (MPC) for online nonlinear system identification and control over a communication network. The WNN performs the online identification of the nonlinear system. The MPC uses the model to predict the future outputs of the system over an extended prediction horizon and calculates the optimal future inputs by minimizing a controller cost function. A computationally efficient formulation for the controller is presented to reduce the computational complexity of the MPC for online implementation and Lyapunov theory is used to prove the stability of the MPC. The methodology is applied to the online identification and control of an unmanned autonomous vehicle. Simulation results show that the MPC with an extended prediction horizon can effectively control the system in the presence of fixed or random network delay.
, , M. Abbaszadeh,
Advanced Control for Applications: Engineering and Industrial Systems; doi:10.1002/adc2.50

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, Pengjuan Ma,
Advanced Control for Applications: Engineering and Industrial Systems; doi:10.1002/adc2.46

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, A. Hernández-Méndez, J. J. Vásquez-Sanjuan, J. F. Guerrero-Castellanos, G. Curiel-Olivares
Advanced Control for Applications: Engineering and Industrial Systems; doi:10.1002/adc2.49

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, Seyed Matin Goshtaei
Advanced Control for Applications: Engineering and Industrial Systems; doi:10.1002/adc2.48

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Youfeng Su, ZhenPan Tu,
Advanced Control for Applications: Engineering and Industrial Systems; doi:10.1002/adc2.47

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, Biplab Bhattacharyya, Jitendra Kumar Saw
Advanced Control for Applications: Engineering and Industrial Systems; doi:10.1002/adc2.45

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