Trajectory tracking control of unconstrained objects based on the SIRMs dynamically connected fuzzy inference model

2002 ◽  
Author(s):  
N. Yubazaki ◽  
Jianqiang Yi ◽  
M. Otani ◽  
N. Unemura ◽  
K. Hirota
Keyword(s):  
Trajectory Tracking ◽  
Tracking Control ◽  
Fuzzy Inference ◽  
Inference Model ◽  
Trajectory Tracking Control

Trajectory Tracking Control of Unconstrained Object Using the SIRMs Dynamically Connected Fuzzy Inference Model

2000 ◽  
Vol 4 (4) ◽  
pp. 302-312 ◽  
Author(s):  
Jianqiang Yi ◽  
◽  
Naoyoshi Yubazaki ◽  
Kaoru Hirota ◽  
Keyword(s):  
Trajectory Tracking ◽  
Tracking Control ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Tracking Error ◽  
Control Object ◽  
Tennis Ball ◽  
Table Tennis ◽  
Inference Model ◽  
Trajectory Tracking Control

A trajectory tracking experiment system taking an unconstrained table-tennis ball as the control object is constructed, and a fuzzy controller based on the SIRMs dynamically connected fuzzy inference model is proposed. For each of the three input items of the fuzzy controller, a SIRM (Single Input Rule Module) is established and an importance degree is defined. Especially for the input item corresponding to ball velocity, its importance degree is tuned dynamically according to moving conditions. The summation of the products of the importance degree and the fuzzy inference result of the SIRMs is calculated to control the angles of a table, making the ball on the table move along a desired trajectory. A virtual spiral asymptotic trajectory is also introduced to give the object an adequate desired position at each sampling time. Tracking experiment results for three kinds of circles and one kind of ellipses show that in more than 80% of the experiments performed under the SIRMs dynamically connected fuzzy inference model, the maximum tracking error is smaller than 0.05m and the unevenness of the sampling steps necessary for each round is very small. Compared with conventional fuzzy controller, the SIRMs dynamically connected fuzzy inference model is proved to be effective in tracking control of unconstrained objects.

scholarly journals Trajectory Tracking Control for Seafloor Tracked Vehicle By Adaptive Neural-Fuzzy Inference System Algorithm

2018 ◽  
Vol 13 (4) ◽  
pp. 465-476 ◽  
Author(s):  
Yu Dai ◽  
Xiang Zhu ◽  
Haibo Zhou ◽  
Zuoli Mao ◽  
Wei Wu
Keyword(s):  
Dynamic Model ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Neural Control ◽  
Fuzzy Inference ◽  
Tracked Vehicle ◽  
Trajectory Tracking Control ◽  
Inference System ◽  
Multi Body ◽  
Body Dynamic

Trajectory tracking control strategy and algorithm for the tracked vehicle moving on the seafloor has aroused much concerns due to the commonly occurred serious slip and trajectory deviation caused by the seafloor extremely soft and cohesive sediment. An improved multi-body dynamic model of a seafloor tracked vehicle (STV) has been established in a simulation code RecurDyn/Track. A particular terramechanics model with a dynamic shear displacement expression for the vehicle-sediment interaction has been built and integrated into the multi-body dynamic model. The collaborative simulation between the mechanical multi-body dynamic model in Recur- Dyn/Track and the control model in MATLAB/Simulink has been achieved. Different control algorithms performances including a PID control, a fuzzy control and a neural control, have been compared and proved the traditional or individual intelligent controls are not particularly suitable for the tracked vehicle on the seafloor. Consequently, an adaptive neural-fuzzy inference system (ANFIS) control algorithm with hybrid learning method for parameter learning which is an integrated control method combined with the fuzzy and neural control, has been adopted and designed. A series of collaborative simulations have been performed and proved the ANFIS algorithm can achieve a better trajectory tracking control performance for the STV as its trajectory deviation can be maintained within a permissible range.

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