Adaptive Sliding Model Controller Design of Carlike Robot Speed and Steering Angle Based on Characteristic Model

Time-varying dynamical parameters, which exist in carlike robot’s formation control under complex tasks or rugged terrain environment, bring inaccuracies or even instability. This paper presents an adaptive sliding model controller based on characteristic model to realize high precision carlike robot formation control. The main contributions are as follows. First, one can consider the carlike robot dynamical system model as a Multi-Input Multi-Output (MIMO) system, rather than two independent variables. Complementarily, the new controller, considering the dynamical inner loop, has better adaptive ability to the time-varying system, such as sprinkler, mine layer and so on. The improvement of the performance is verified by MATLAB for time-varying kinetic parameters and MRDS4 three-dimensional physics engine for the rugged terrain environment.

[1]  Francisco Rodríguez,et al.  Adaptive control for a mobile robot under slip conditions using an LMI-based approach , 2009, 2009 European Control Conference (ECC).

[2]  Domenico Prattichizzo,et al.  Discussion of paper by , 2003 .

[3]  Maria Letizia Corradini,et al.  Experimental testing of a discrete-time sliding mode controller for trajectory tracking of a wheeled mobile robot in the presence of skidding effects , 2002, J. Field Robotics.

[4]  Zhihao Xu,et al.  Formation control of car-like autonomous vehicles under communication delay , 2012, Proceedings of the 31st Chinese Control Conference.

[5]  Yingmin Jia,et al.  Adaptive leader-follower formation control of non-holonomic mobile robots using active vision , 2015 .

[6]  Xiao Yang,et al.  A chattering-free adaptive second-order non-singular fast terminal sliding mode control scheme for a class of nonlinear uncertain systems , 2018, Int. J. Model. Identif. Control..

[7]  Guangming Xie,et al.  Leader-Following Formation Control of Multiple Mobile Robots , 2005, Proceedings of the 2005 IEEE International Symposium on, Mediterrean Conference on Control and Automation Intelligent Control, 2005..

[8]  Weicun Zhang,et al.  Towards a unified stability analysis of continuous-time T-S model-based fuzzy control systems , 2019 .

[9]  Maarouf Saad,et al.  Nonlinear coordination control for a group of mobile robots using a virtual structure , 2011 .

[10]  D. Rosenthal,et al.  Quantitative Determination of Δ9-Tetrahydrocannabinol in Cadaver Blood , 1979 .

[11]  Bachir Nail,et al.  On λ-matrices and their applications in MIMO control systems design , 2018, Int. J. Model. Identif. Control..