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Optimal Distribution Control Of Non‐Linear Tire Force Of Electric Vehicles With In‐Wheel Motors
Author(s) -
Li Boyuan,
Du Haiping,
Li Weihua
Publication year - 2016
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.1145
Subject(s) - actuator , control theory (sociology) , controller (irrigation) , engineering , slip (aerodynamics) , slip angle , vehicle dynamics , tire balance , linear actuator , steering wheel , control system , yaw , optimal control , rotary actuator , control engineering , automotive engineering , computer science , control (management) , mathematics , agronomy , artificial intelligence , aerospace engineering , electrical engineering , biology , mathematical optimization
An over‐actuated control system has the advantage of being able to use redundant actuators to reconfigure the control system and it can realize fault tolerant control. In order to achieve improved vehicle stability and handling performance for electric vehicles with in‐wheel steering and driving motors, the control of the vehicle body slip angle and yaw rate is actually an over‐actuated control problem. To obtain the optimal solution for this control problem, this study proposes a two‐level tire force distribution control method, where the upper level controller calculates the desired lateral and longitudinal forces generated by friction on the tire of each wheel according to the driver's steering and driving inputs. The lower level controller maps the desired tire forces into the input of each steering actuator and driving actuator. Unlike the linear mapping method applied in most of the current research, this study develops a proportional‐integral (PI) controller for each actuator so that the non‐linear tire characteristics can be counteracted. In addition, since the PI controllers for eight actuators (four steering actuators and four driving actuators) have a total of 16 control gains to be determined, a genetic algorithm is applied to accurately determine these control gains. The simulation results are presented to validate the control performance of the proposed tire force allocation method.