A comparison of continuous and discrete tracking-error model-based predictive control for mobile robots

2017 ◽  
Vol 87 ◽  
pp. 177-187 ◽  
Author(s):  
Igor Škrjanc ◽  
Gregor Klančar
Keyword(s):  
Mobile Robots ◽  
Predictive Control ◽  
Tracking Error ◽  
Error Model ◽  
Model Based

Tracking-error model-based PDC control for mobile robots with acceleration limits

2009 ◽  
Author(s):  
El-Hadi Guechi ◽  
Jimmy Lauber ◽  
Michel Dambrine ◽  
Saso Blazic ◽  
Gregor Klancar
Keyword(s):  
Mobile Robots ◽  
Tracking Error ◽  
Error Model ◽  
Model Based

Tracking Error Modeling of the Theodolite Based on GRNN Method

2011 ◽  
Vol 121-126 ◽  
pp. 4870-4874
Author(s):  
Miao Li ◽  
Hui Bin Gao
Keyword(s):  
Evaluation Method ◽  
Tracking Error ◽  
High Accuracy ◽  
Error Model ◽  
Error Modeling ◽  
General Regression Neural Network ◽  
Engineering Practice ◽  
Tracking Accuracy ◽  
Model Based ◽  
General Regression

To meet the requirement of high tracking accuracy as well as develop more reasonable evaluation method, in this paper, the General Regression Neural Network (GRNN) has been applied to build the tracking error model of the theodolite. First, we analyze the nonlinear factors in the theodolite. Second, we discuss the principle of GRNN, including its structure, the function as well as its priors. Third, we build the tracking error model based on GRNN and verify the model through the different parameters. The result indicated that the network model based on GRNN has high accuracy and good generalization ability. It could instead the real system to a certain extent. The research in this paper has important value to the engineering practice.

scholarly journals Stable Model-Based Predictive Control for Wheeled Mobile Robots using Linear Matrix Inequalities

2015 ◽  
Vol 48 (19) ◽  
pp. 33-38 ◽  
Author(s):  
Jonatas R. Pitanga ◽  
Humberto X. Araújo ◽  
André G.S. Conceição ◽  
Gustavo H.C. Oliveira
Keyword(s):  
Mobile Robots ◽  
Linear Matrix Inequalities ◽  
Predictive Control ◽  
Linear Matrix ◽  
Stable Model ◽  
Matrix Inequalities ◽  
Wheeled Mobile Robots ◽  
Model Based

Compensatory model predictive control for post-impact trajectory tracking via active front steering and differential torque vectoring

2020 ◽  
pp. 095440702097908
Author(s):  
Mingcong Cao ◽  
Chuan Hu ◽  
Rongrong Wang ◽  
Jinxiang Wang ◽  
Nan Chen
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Trajectory Tracking ◽  
Tracking Error ◽  
Collision Probability ◽  
Critical Conditions ◽  
Physical Analysis ◽  
Compensatory Model ◽  
Torque Vectoring ◽  
Post Impact

This paper investigates the trajectory tracking control of independently actuated autonomous vehicles after the first impact, aiming to mitigate the secondary collision probability. An integrated predictive control strategy is proposed to mitigate the deteriorated state propagation and facilitate safety objective achievement in critical conditions after a collision. Three highlights can be concluded in this work: (1) A compensatory model predictive control (MPC) strategy is proposed to incorporate a feedforward-feedback compensation control (FCC) method. Based on the definite physical analysis, it is verified that adequate reverse steering and differential torque vectoring render more potentials and flexibility for vehicle post-impact control; (2) With compensatory portions, the deteriorated states after a collision are far beyond the traditional stability envelope. Hence it can be further manipulated in MPC by constraint transformation, rather than introducing soft constraints and decreasing the control efforts on tracking error; (3) Considering time-varying saturation on input, input rate, and slip ratio, the proposed FCC-MPC controller is developed to improve faster deviation attenuation both in lateral and yaw motions. Finally two high-fidelity simulation cases implemented on CarSim-Simulink conjoint platform have demonstrated that the proposed controller has the advanced capabilities of vehicle safety improvement and better control performance achievement after severe impacts.

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