کنترل ردیابی با اشباع ورودی
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کنترل ردیابی با اشباع ورودی

عنوان فارسی مقاله: کنترل ردیابی با اشباع ورودی و محدودیت های کامل برای کشتی های سطحی
عنوان انگلیسی مقاله: Tracking Control With Input Saturation and Full-State Constraints for Surface Vessels
مجله/کنفرانس: دسترسی – IEEE Access
رشته های تحصیلی مرتبط: مهندسی کامپیوتر، مهندسی فناوری اطلاعات
گرایش های تحصیلی مرتبط: شبکه های کامپیوتری
کلمات کلیدی فارسی: کشتی های سطحی، پسگام، محدودیت های کامل، اشباع ورودی، شبکه عصبی
کلمات کلیدی انگلیسی: Surface vessels, backstepping, full-state constraints, input saturation, neural network
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus – Master Journals List – JCR
شناسه دیجیتال (DOI): https://doi.org/10.1109/ACCESS.2019.2945501
دانشگاه: College of Automation, Harbin Engineering University, Harbin 150001, China
ناشر: آی تریپل ای - IEEE
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 4.641 در سال 2018
شاخص H_index: 56 در سال 2019
شاخص SJR: 0.609 در سال 2018
شناسه ISSN: 2169-3536
شاخص Quartile (چارک): Q2 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 15
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E13851
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

ABSTRACT


I. INTRODUCTION


II. PROBLEM FORMULATION AND PRELIMINARIES


III. CONTROL DESIGN


IV. SIMULATIONS


V. CONCLIUSION


REFERENCES

نمونه متن انگلیسی مقاله

ABSTRACT


To solve the problems of full-state constraints in trajectory tracking of surface vessels, a backstepping technique combining a novel integral barrier Lyapunov function (iBLF) with neural network and sliding mode is proposed. Moreover, the control law is extended to the control problem with input saturation. First, the iBLF-based control approach is applied to the control design. The purpose of the iBLFbased approach is to deal with the constraints without transforming the constraints bound into the tracking errors bound. Second, the Neural Networks (NN) is used to handle with the system uncertainties, and a single parameter online adjustment is used instead of the weights online adjustment of the neural networks to realize the adaptive estimation of a single parameter. Third, defining an auxiliary analysis system to deal with the effect of input saturation on the system, an effective control approach under input saturation is realized. Furthermore, it is proved that the designed control law can guarantee the uniformly ultimately bounded stability of closed-loop system and system state can not violate the constraints. Finally, the simulation results of trajectory tracking control of the surface vessel show that the proposed control approach can effectively solve the control problem of nonlinear systems with full-state constraints, system uncertainties and input saturation.


INTRODUCTION


In recent years, with the increasing needs of the marine engineering [1], the higher accuracy of the trajectory tracking control of surface vessels for different mission requirements is strongly needed. Research on the nonlinear control approaches for surface vessels have become a hot topic [2]–[۵]. State constraints is a challenge in trajectory tracking of surface vessels. Once the system violates the constraints during the operation, the system dynamic performance degradation may occur, and it is difficult to meet the control requirements. In order to stabilize the system under the constraints, artificial potential field [6], [7], model predictive control [8], [9] and invariant set [10], [11] are applied. Compared to these approaches, the barrier Lyapunov function (BLF) approach is used to handle the system constrains by Lyapunov-based control design technique, which averts the need for explicit solutions. Ren et al. [12] proposed a class of constraint control approach based on BLFs. By constructing the explicit BLFs, the controller for constrained control system can be designed by combining Lyapunov direct method with other mature control approaches. Tee et al. [13], [14] used the BLFs to solve nonlinear system control problems with constraints. Ren et al. [15] applied the BLF control approach to the control design of nonlinear systems with state constraints. However, most of the references using BLF-based approaches adopt a log-type BLF to deal with the state constraints of nonlinear systems.

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