Adaptive hybrid control for linear piezoelectric ceramic motor drive using diagonal recurrent CMAC network.
This paper presents an adaptive hybrid control system using a diagonal recurrent cerebellar-model-articulation-computer (DRCMAC) network to control a linear piezoelectric ceramic motor (LPCM) driven by a two-inductance two-capacitance (LLCC) resonant inverter. Since the dynamic characteristics and motor parameters of the LPCM are highly nonlinear and time varying, an adaptive hybrid control system is therefore designed based on a hypothetical dynamic model to achieve high-precision position control. The architecture of DRCMAC network is a modified model of a cerebellar-model-articulation-computer (CMAC) network to attain a small number of receptive-fields. The novel idea of this study is that it employs the concept of diagonal recurrent neural network (DRNN) in order to capture the system dynamics and convert the static CMAC into a dynamic one. This adaptive hybrid control system is composed of two parts. One is a DRCMAC network controller that is used to mimic a conventional computed torque control law due to unknown system dynamics, and the other is a compensated controller with bound estimation algorithm that is utilized to recover the residual approximation error for guaranteeing the stable characteristic. The effectiveness of the proposed driving circuit and control system is verified with hardware experiments under the occurrence of uncertainties. In addition, the advantages of the proposed control scheme are indicated in comparison with a traditional integral-proportional (IP) position control system.
Wai
Rong-Jong
RJ
Department of Electrical Engineering, Yuan Ze University, Chung Li 32026, Taiwan, ROC. rjwai@saturn.yzu.edu.tw
Lin
Chih-Min
CM
Peng
Ya-Fu
YF
D000465:Algorithms; D001185:Artificial Intelligence; D002516:Ceramics; D003198:Computer Simulation; D003661:Decision Support Techniques; D004563:Electrochemistry / Q000295:instrumentation; D004867:Equipment Design; D019544:Equipment Failure Analysis; D005246:Feedback; D016015:Logistic Models; D009038:Motion; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D014159:Transducers