Comparative Application of Model Predictive Control Strategies to a Wheeled Mobile Robot

2017 ◽  
Vol 87 (1) ◽  
pp. 81-95 ◽  
Author(s):  
Hoai Nam Huynh ◽  
Olivier Verlinden ◽  
Alain Vande Wouwer
Keyword(s):  
Model Predictive Control ◽  
Mobile Robot ◽  
Predictive Control ◽  
Control Strategies ◽  
Wheeled Mobile Robot

scholarly journals Trajectory Tracking of an Omni-Directional Wheeled Mobile Robot Using a Model Predictive Control Strategy

2018 ◽  
Vol 8 (2) ◽  
pp. 231 ◽  
Author(s):  
Chengcheng Wang ◽  
Xiaofeng Liu ◽  
Xianqiang Yang ◽  
Fang Hu ◽  
Aimin Jiang ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Mobile Robot ◽  
Predictive Control ◽  
Trajectory Tracking ◽  
Control Strategy ◽  
Wheeled Mobile Robot

scholarly journals Robust Stabilization of a Wheeled Mobile Robot Using Model Predictive Control Based on Neurodynamics Optimization

2017 ◽  
Vol 64 (1) ◽  
pp. 505-516 ◽  
Author(s):  
Hanzhen Xiao ◽  
Zhijun Li ◽  
Chenguang Yang ◽  
Lixian Zhang ◽  
Peijiang Yuan ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Mobile Robot ◽  
Predictive Control ◽  
Robust Stabilization ◽  
Wheeled Mobile Robot

scholarly journals Stochastic Time-Varying Model Predictive Control for Trajectory Tracking of a Wheeled Mobile Robot

2021 ◽  
Vol 9 ◽  
Author(s):  
Weijiang Zheng ◽  
Bing Zhu
Keyword(s):  
Model Predictive Control ◽  
Mobile Robot ◽  
Predictive Control ◽  
Linear Time ◽  
Tracking Error ◽  
Wheeled Mobile Robot ◽  
Reference Trajectory ◽  
Time Varying ◽  
Probability Constraints ◽  
And Control

In this paper, a stochastic model predictive control (MPC) is proposed for the wheeled mobile robot to track a reference trajectory within a finite task horizon. The wheeled mobile robot is supposed to subject to additive stochastic disturbance with known probability distribution. It is also supposed that the mobile robot is subject to soft probability constraints on states and control inputs. The nonlinear mobile robot model is linearized and discretized into a discrete linear time-varying model, such that the linear time-varying MPC can be applied to forecast and control its future behavior. In the proposed stochastic MPC, the cost function is designed to penalize its tracking error and energy consumption. Based on quantile techniques, a learning-based approach is applied to transform the probability constraints to deterministic constraints, and to calculate the terminal constraint to guarantee recursive feasibility. It is proved that, with the proposed stochastic MPC, the tracking error of the closed-loop system is asymptotically average bounded. A simulation example is provided to support the theoretical result.

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