控制理论(社会学)
模型预测控制
运动学
移动机器人
弹道
控制器(灌溉)
二次规划
计算机科学
李雅普诺夫函数
非完整系统
车辆动力学
人工神经网络
控制工程
机器人
工程类
数学
数学优化
人工智能
控制(管理)
非线性系统
物理
生物
经典力学
汽车工程
量子力学
农学
天文
作者
Chen Yao,Zhijun Li,Haiyi Kong,Kebin Fan
出处
期刊:IEEE Transactions on Industrial Informatics
[Institute of Electrical and Electronics Engineers]
日期:2019-06-01
卷期号:15 (6): 3196-3205
被引量:79
标识
DOI:10.1109/tii.2018.2874182
摘要
This paper addresses a trajectory-tracking control problem for mobile robots by combining tube-based model predictive control (MPC) in handling kinematic constraints and adaptive control in handling dynamic constraints. In order to handle kinematic constraints, the tube-based MPC scheme is introduced, which includes the state feedback controller to suppress the external disturbance in the velocity level. The tube-based MPC is transformed to a constrained quadratic programming (QP) problem, and then the QP problem can be efficiently solved by a primal-dual neural network over a finite receding horizon so as to obtain the optimal control velocity. Besides, an adaptive controller employing the neural network technology is proposed to acquire the approximation of the uncertain robotic dynamics. Moreover, an auxiliary control is developed in order to deal with actuator saturation, and a disturbance observer is designed to reject the external disturbance online in the dynamic level. Subsequently, through Lyapunov function synthesis, the stability of the closed-loop system have been guaranteed. Finally, in order to verify the effectiveness, the experimental studies are carried out using an actual mobile robot.
科研通智能强力驱动
Strongly Powered by AbleSci AI