Dynamic Modeling and Control of Dual Wheeled Mobile Robots Compliantly Coupled to a Common Payload

1999 ◽  
Vol 121 (3) ◽  
pp. 457-461 ◽  
Author(s):  
Thurai Vinay ◽  
Bradley Postma ◽  
Theo Kangsanant

Lagrange formalism is applied to derive a dynamic model, and design a nonlinear controller for two nonholonomic, differentially steered, wheeled mobile robots compliantly linked to a common payload. The resulting multivariable system model is of a large order and can be block decoupled by selective state feedback into five independent subsystems, two of which effectively represent the deviation dynamics of the individual robots from a prescribed path; two others represent their forward motion dynamics; while the fifth describes the payload dynamics. Controllers for each of the robot subsystems, including self-tuning adaptive controllers for the nonlinear deviation dynamics subsystems, are designed by the pole-placement technique. System performance is then evaluated via simulation for the case where each robot is undergoing curvilinear motion.

2016 ◽  
pp. 1235-1266 ◽  
Author(s):  
Claude Samson ◽  
Pascal Morin ◽  
Roland Lenain

2000 ◽  
Author(s):  
Xuanyin Wang

Abstract This paper researches on the hydraulic servo system by using ordinary on-off valves. The mathematic model of an asymmetric hydraulic cylinder servo control system is built, and its characteristic is analysed here. To reduce the static and dynamic characteristic differences between forward and reverse motion of asymmetric cylinder, and improve system’s performance, a self-tuning linear quadratic gaussian optimum controller (SLQG) is designed successful. In the end, an asymmetric hydraulic cylinder servo system of paint robot is researched. The result shows that the above method is effective.


Author(s):  
Dao Phuong Nam ◽  
Nguyen Hoang Ha ◽  
Vu Anh Tran ◽  
Do Duy Khanh ◽  
Nguyen Dinh Khue ◽  
...  

Author(s):  
Dejan Milutinovic´ ◽  
Devendra P. Garg

Motivated by the close relation between estimation and control problems, we explore the possibility to utilize stochastic sampling for computing the optimal control for a large-size robot population. We assume that the individual robot state is composed of discrete and continuous components, while the population is controlled in a probability space. Utilizing a stochastic process, we can compute the state probability density function evolution, as well as use the stochastic process samples to evaluate the Hamiltonian defining the optimal control. The proposed method is illustrated by an example of centralized optimal control for a large-size robot population.


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