Model Predictive Speed Control And Steady-state Performance Improvement Of Induction Motor

Model predictive control(MPC)is widely used in induction motor drive because of its simple principle,fast transient response,and the ability to handle multiple variables simultaneously.In this thesis,MPC is deeply studied for the application of the adjustable-speed induction motor(IM)drive fed by a two-level inverter,and the main results are achieved such as less debugging parameters,enhanced parameter robustness,and improved steady-state performance.Model predictive torque control generally gets the reference torque through the speed outer loop,while the torque and stator flux tracking are achieved through the model predictive control of the inner loop.Although this double closed-loop structure ensures the stability of the system,there are disadvantages such as the influence of the inner and outer loops on each other and the large number of debugging parameters.To address this problem,this thesis proposes a model predictive direct speed control method with a single control loop,which introduces the stator flux and speed in the cost function to realize the simultaneous control of speed and flux in different time scales,with the advantages of simple structure and less debugging parameters,and verifies the effectiveness of the proposed method through simulation and experiment.Conventional single vector model predictive control is based on the cost function selected voltage vector applying on the whole fixed control period,and the steady-state performance is poor.To address this problem,a variable period single vector model predictive control is proposed in this thesis.Through mathematical analysis and derivation,the control period is made variable so that it has better steady-state performance,but the switching frequency is increased.In order to solve the problem of large current fluctuation and poor steady-state performance caused by the limitation of the total control period of the traditional arbitrary double vector model predictive control,a variable period arbitrary double vector model predictive control is designed.By expanding the optimization range of the control period,the cost function is constructed to minimize the current error,and the optimal voltage vector and the optimal control period are obtained by minimizing the cost function,which makes the control period flexible and adjustable.The simulation results show that the proposed variable period arbitrary double vector model predictive control method has lower current ripple and current harmonics than the conventional method without increasing the switching frequency,and also has excellent dynamic performance.Conventional deadbeat predictive current control(DBPCC)based on space vector modulation(SVM)shows quick dynamic responses and a good steady-state performance in induction motor(IM)drives.However,the robustness of the DBPCC is poor.Furthermore,DBPCC uses a fixed vector sequence of 0127 over the entire speed range,which results in large current harmonics at high modulation indices.To address the above issues,this paper proposes a robust deadbeat predictive current control(RDBPCC)for IM drives.Based on an ultra-local model,the proposed method updates the input voltage gain and unknown system components online according to the voltage and current of the previous two control cycles,does not depend on the motor parameters,and has strong robustness.The steady-state performance is significantly improved by selecting the optimal vector sequence according to the modulation index based on the principle of current harmonic minimization.The experimental results confirm that,compared to vector control and conventional DBPCC,the proposed method achieves a strong parameter robustness and reduces the current total harmonic distortion(THD)by more than 10%and 20%at high-modulation indices.