CAJ | 학술논문

针对无轴承异步电机多变量、非线性、强耦合等特点，为实现其稳定悬浮控制，提出了一种基于自适应模糊神经网络推理系统（adaptive neuro-fuzzy inference system，ANFIS）的控制新策略。在分析无轴承异步电机径向悬浮力产生机理的基础上，推导出无轴承异步电机数学模型，基于ANFIS控制原理，完成了控制器设计，包括控制变量和隶属函数的选取、通过PID控制对输入输出数据的采集、根据选定的误差准则修正隶属函数参数以及采用Sugeno型ANFIS控制器训练FIS（fuzzy inference system）模型。基于MATLAB/Simulink仿真平台，对转速为6000 r/min 的无轴承异步电机控制系统的悬浮、转速、转矩响应进行了仿真分析。仿真结果表明该控制策略能在0.12 s内实现转子的稳定悬浮，且当负载转矩突变时，转子的悬浮性能并没有受到影响，转子径向偏移小于0.001 mm。在转速突变后，控制系统也能较好的跟踪给定转速，稳定时的转速误差小于20 r/min，控制系统具有良好的动、静态性能。最后在无轴承异步电机控制系统试验平台上对所提策略开展了试验研究，试验结果同样表明，该控制策略能实现无轴承异步电机的稳定悬浮工作，转子径向位移峰峰值范围可以保持在80μm以内，系统响应快，鲁棒性强，控制精度较高，验证了该文提出的ANFIS控制方法的正确性和有效性。
침대무축승이보전궤다변량、비선성、강우합등특점，위실현기은정현부공제，제출료일충기우자괄응모호신경망락추리계통（adaptive neuro-fuzzy inference system，ANFIS）적공제신책략。재분석무축승이보전궤경향현부력산생궤리적기출상，추도출무축승이보전궤수학모형，기우ANFIS공제원리，완성료공제기설계，포괄공제변량화대속함수적선취、통과PID공제대수입수출수거적채집、근거선정적오차준칙수정대속함수삼수이급채용Sugeno형ANFIS공제기훈련FIS（fuzzy inference system）모형。기우MATLAB/Simulink방진평태，대전속위6000 r/min 적무축승이보전궤공제계통적현부、전속、전구향응진행료방진분석。방진결과표명해공제책략능재0.12 s내실현전자적은정현부，차당부재전구돌변시，전자적현부성능병몰유수도영향，전자경향편이소우0.001 mm。재전속돌변후，공제계통야능교호적근종급정전속，은정시적전속오차소우20 r/min，공제계통구유량호적동、정태성능。최후재무축승이보전궤공제계통시험평태상대소제책략개전료시험연구，시험결과동양표명，해공제책략능실현무축승이보전궤적은정현부공작，전자경향위이봉봉치범위가이보지재80μm이내，계통향응쾌，로봉성강，공제정도교고，험증료해문제출적ANFIS공제방법적정학성화유효성。
Bearingless induction motors, which were multivariable, were strongly coupled, along with a higher order nonlinear system. To obtain the stable suspension control of a bearingless induction motor, a new control strategy based on Adaptive Neuro Fuzzy Inference System was proposed. First, in the analysis of the generation mechanism of a bearingless induction motor’s radial suspension force, the mathematical model of a bearingless induction motor was achieved. Based on the control principle of an Adaptive Neuro Fuzzy Inference System, the Adaptive Neuro Fuzzy Inference System had been built to design the controller, including the option of control variables and membership functions. By the PID control, the input data and output data could be collected. The selected criterion of error was set to correct the membership function parameters. In addition, the Fuzzy Inference System (FIS) model was trained by a Sugeno type Adaptive Neuro Fuzzy Inference System controller. Then, aiming at the performances of rotor suspending, speed, and torque response, the simulation and analysis of the control system for bearingless induction motors had been carried out on the basis of MATLAB/Simulink simulation platform. Moreover, the motor speed was set to 6000r/min. The simulation results showed that the stable suspension of a bearingless induction motor can be quickly achieved by this presented control strategy. Through the comparison with PID control, the speed response was faster, and the speed overshoot was smaller in the Adaptive Neuro Fuzzy Inference System control. Further, the suspension performance of the rotor was not affected by the sudden change in the load torque. When the rotor speed suddenly changed from 6000r/min to 3000r/min at the time of 0.5 seconds, the speed response of the control system could track the given speed well, and with a very small steady state error. The control system has a fine dynamic and static performance. Finally, the control system test platform of a bearingless induction motor was built based on Adaptive Neuro Fuzzy Inference System controller. The experimental results of the control system also showed that this control strategy could achieve the stable suspension of a bearingless induction motor. The control system has a quickly response, a high control precision, and the strong robustness to load torque disturbance. The correctness and effectiveness of the Adaptive Neuro Fuzzy Inference System control method was verified in this paper.