An adaptive repetitive control scheme is presented for a class of nonlinearly parameterized systems based on the fuzzy basis function network （FBFN）. The parameters of the fuzzy rules are tuned with adaptive schemes...An adaptive repetitive control scheme is presented for a class of nonlinearly parameterized systems based on the fuzzy basis function network （FBFN）. The parameters of the fuzzy rules are tuned with adaptive schemes. To attenuate chattering effectively, the discontinuous control term is approximated by an adaptive PI control structure. The bound of the discontinuous control term is assumed to be unknown and estimated by an adaptive mechanism. Based on the Lyapunov stability theory, an adaptive repetitive control law is proposed to guarantee the closed-loop stability and the tracking performance. By means of FBFNs, which avoid the nonlinear parameterization from entering into the adaptive repetitive control, the controller singularity problem is solved. The proposed approach does not require an exact structure of the system dynamics, and the proposed controller is utilized to control a model of permanent-magnet linear synchronous motor subject to significant disturbances and parameter uncertainties. The simulation results demonstrate the effectiveness of the proposed method.展开更多
基金supported by the National Natural Science Foundation of China (61203041)the Chinese National Post-doctor Science Foundation (2011M500217)
文摘An adaptive repetitive control scheme is presented for a class of nonlinearly parameterized systems based on the fuzzy basis function network （FBFN）. The parameters of the fuzzy rules are tuned with adaptive schemes. To attenuate chattering effectively, the discontinuous control term is approximated by an adaptive PI control structure. The bound of the discontinuous control term is assumed to be unknown and estimated by an adaptive mechanism. Based on the Lyapunov stability theory, an adaptive repetitive control law is proposed to guarantee the closed-loop stability and the tracking performance. By means of FBFNs, which avoid the nonlinear parameterization from entering into the adaptive repetitive control, the controller singularity problem is solved. The proposed approach does not require an exact structure of the system dynamics, and the proposed controller is utilized to control a model of permanent-magnet linear synchronous motor subject to significant disturbances and parameter uncertainties. The simulation results demonstrate the effectiveness of the proposed method.
文摘为了提高永磁同步电机(permanent magnet synchronous motor,PMSM)矢量控制系统的响应速度和抗干扰能力,提出一种分数阶模糊反步控制方法(fractional order fuzzy backstepping control,FOFB),以保证永磁同步电机更好的控制性能。首先,根据反步控制的原理,对系统分解,并在每一步中利用模糊逻辑系统来逼近系统的未知部分。其次,引入分数阶理论并选取符合系统规律的Lyapunov函数,得出合适的控制律和参数自适应律。最后,分别对比例积分微分调节(proportional integral derivative,PID)、模糊PID(fuzzy PID,F-PID)、整数阶模糊反步法(integer order fuzzy backstepping control,IOFB)、分数阶模糊反步法(fractional order fuzzy backstepping,FOFB)控制下的PMSM进行仿真。仿真和试验结果表明,FOFB控制在转速突变过程中能够实现转速的实时跟踪。相较于其他控制策略,加入负载转矩FOFB的下降转速为40 r/min、超调量为4.7%时的响应性能更好、抗干扰能力更优,这证明了FOFB控制方法的合理性和有效性。
