The analysis of electric power steering base on fuzzy-PI control

In this paper, the process of building the controller for an EPS system is investigated based on the fuzzy logic. Unlike the tradition PI controller, the fuzzy controllers have a good control effect for the nonlinear and time-varying systems. Motivated by this observation, this paper designs the fuzzy-PI controller for an EPS system. In proposed control strategy, we establish the fuzzy-PI closed-loop control law, which aims that the actual current of the motor is accurately tracking of motor target current. To obtain this problem, we combine the current deviation and the deviation rate of current change to design the fuzzy controller for adjusting the online of PI parameters. Therefore, our control strategy can overcome the limitation of conventional PI controller with the fixing parameters. In addition, the mathematical model of the column-type EPS system is built through the dynamic analysis. Finally, to demonstrate the advantages for the proposed control method, the simulation model is established according to the Matlab/Simulink with CarSim. The simulation results indicate that the control method proposed in this paper is effective than that of the tradition PI controller.

[1]  B. Nagaraj,et al.  A comparative study of PID controller tuning using GA, EP, PSO and ACO , 2010, 2010 INTERNATIONAL CONFERENCE ON COMMUNICATION CONTROL AND COMPUTING TECHNOLOGIES.

[2]  Huang Juhua,et al.  Modeling and Simulation of the Electric Power Steering System , 2009, 2009 Pacific-Asia Conference on Circuits, Communications and Systems.

[3]  Seul Jung,et al.  Hardware Implementation of a Real-Time Neural Network Controller With a DSP and an FPGA for Nonlinear Systems , 2007, IEEE Transactions on Industrial Electronics.

[4]  Zhicheng Xu.  A novel robust PID controller design method , 2010, 2010 International Conference on Computer Application and System Modeling (ICCASM 2010).

[5]  Yakov Frayman,et al.  A dynamically-constructed fuzzy neural controller for direct model reference adaptive control of multi-input-multi-output nonlinear processes , 2002, Soft Comput..

[6]  A. W. Burton.  Innovation drivers for electric power-assisted steering , 2003 .

[7]  L. Reznik.  Fuzzy Controllers , 2004 .

[8]  Chih-Min Lin,et al.  Supervisory recurrent fuzzy neural network control of wing rock for slender delta wings , 2004, IEEE Trans. Fuzzy Syst..

[9]  Ross Maxwell Stuntz,et al.  Vibration control based on dynamic compensation in an electric power steering system , 1997, 1997 1st International Conference, Control of Oscillations and Chaos Proceedings (Cat. No.97TH8329).

[10]  Peilin Shi,et al.  Optimal Controller Design for Electric Power Steering System Based on LQG , 2009, 2009 International Conference on Information Engineering and Computer Science.