| The DC motor system plays an important role in the precision speed and position regulation in the fields of new energy generation,electric vehicle and so on.It is of great theoretical significance and practical application value to study and design the control algorithm of DC motor with good dynamic and static characteristics.In this paper,the DC motor system driven by Buck converter is taken as the research object,and the advanced model predictive control strategy for disturbance suppression is studied.The main contents of this paper are as follows:Firstly,the basic principle of DC motor driven by Buck converter is introduced and the mathematical models of Buck converter and DC motor system are established respectively.According to the interaction between the two parts,mathematical model of the DC motor driven by Buck converter system is established.The state space model of the system is established by the assumption of the state quantity of the system and the corresponding coordinate transformation.Secondly,a hardware in the loop simulation of DC motor control algorithm testing platform based on DSPACE is designed.The design of hardware platform is based on the hardware resources and interface selection of DS1103 card,mainly including DC motor drive module,speed detection module,voltage and current detection module,PWM output module and so on.The design of the software platform includes the building of the MATLAB/Simulink simulation block diagram and the selection of the modules in the RTI library and the setting of the parameters.Thirdly,based on the state space model of the drive and speed regulating integrated DC motor,an output feedback incremental model predictive controller based on the state observer is designed,which achieves the static error free tracking control of the angular velocity of DC motor.The control system block diagram is built in MATLAB/Simulink to simulate the control algorithm.The results prove the feasibility of the method and the effectiveness of dealing with load torque disturbance.The controller is applied to speed regulation of the DC motor system and shows to exhibit satisfactory dynamic and static performance.Fourthly,based on the four order state space model of the drive and speed regulating integrated DC motor,a new discrete-time model predictive controller is designed to directly control the DC motor.First,based on the state space model,the reduced order generelized proportional integral observer is designed to estimate the state volume and the lumped disturbance in the state space model.The states and lumped disturbances estimated by GPI observer are then applied to the speed prediction,and the corresponding discrete-time model predictive controller with input constraints is designed for the four order state space model.The feasibility of the proposed control method and the effectiveness of improving the anti-disturbance performance of the system are verified via simulation studies.Finally,the above two kinds of model predictive controllers are tested in the experimental platform of DC motor driven by Buck converter,which is built on DSPACE real-time control system.The experimental results show that compared with standard model predictive control and incremental model predictive control methods,the model predictive control method integrating GPI observer presented in this paper shows better robustness to various disturbances and uncertainties. |
