Neurodynamics-Based Model Predictive Control of Continuous-Time Under-Actuated Mechatronic Systems

被引：46
作者：
Wang, Jiasen [1 ]
Wang, Jun [1 ,2 ,3 ]
Han, Qing-Long [4 ]
机构：
[1] City Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China
[2] City Univ Hong Kong, Sch Data Sci, Hong Kong, Peoples R China
[3] City Univ Hong Kong, Shenzhen Res Inst, Shenzhen 518057, Peoples R China
[4] Swinburne Univ Technol, Sch Software & Elect Engn, Melbourne, Vic 3122, Australia
来源：
IEEE-ASME TRANSACTIONS ON MECHATRONICS | 2021年 / 26卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Optimization; Mechatronics; Neurodynamics; Predictive control; Collaboration; Control systems; Space vehicles; Model predictive control (MPC); neurodynamic optimization; under-actuated mechatronic systems; RECURRENT NEURAL-NETWORK; TRACKING CONTROL; NONHOLONOMIC SYSTEMS; TRAJECTORY-TRACKING; NONLINEAR-SYSTEMS; GLOBAL TRACKING; STABILIZATION; SPACECRAFT; OPTIMIZATION; DYNAMICS;
D O I：
10.1109/TMECH.2020.3016757
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This article addresses neurodynamics-based model predictive control of continuous-time under-actuated mechatronic systems. The control problem is formulated as a global optimization problem based on sampled data, which is solved by using a collaborative neurodynamic approach. The closed-loop system is proven to be asymptotically stable. Specific applications on control of autonomous surface vehicles and unmanned wheeled vehicles are elaborated to substantiate the efficacy of the approach.
引用
收藏
页码：311 / 322
页数：12
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