Guangzhou University

Kai Wan

Yingyu Chen

Zhijia Zhao

Yunshan Wei

Qingyuan Xu

An iterative learning-based energy-saving control method for a piezoelectric motor, comprising: setting a sampling period of a piezoelectric motor (); obtaining an expected output trajectory of the piezoelectric motor, and performing sampling according to the sampling period to obtain a sampled expected output sequence (); setting an initial control input signal of the piezoelectric motor (); obtaining an actual control input signal according to a mapping relation of the initial control input signal, and transmitting the actual control input signal to the piezoelectric motor to obtain an actual output position (); obtaining a mapping relation of the output position of the piezoelectric motor according to the actual output position, and sampling the mapping relation according to the sampling period to obtain a sampled actual output sequence (); calculating the difference between the sampled expected output sequence and the sampled actual output sequence to obtain a sampling error function sequence (); obtaining an iterative learning gain parameter by calculation according to a preset convergence condition (); and obtaining, by calculation according to the sampling error function sequence and the iterative learning gain parameter, an iterative input voltage required by iteration of the piezoelectric motor, and transmitting the iterative input voltage to the piezoelectric motor ().

Piezoelectric motor energy-saving control method based on an iterative learning

The invention relates to a field off artificial intelligence (AI) technologies, and discloses a method and an apparatus for high-order iterative self-learning control for a robotic fish, and a storage medium; the control method performs preferential iterative calculation on control gain elements in the control gain set to obtain a target control gain set; and performs high-order iterative calculation according to the target control gains, the first control input thrust and the first tracking error to obtain a target control input thrust, and then controls a robotic fish to swing according to the target control input thrust to obtain an expected speed. In this way, complete tracking and rapid convergence of a swim speed of a robotic fish in the whole operation space may be achieved.

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Huizhou, China

Kai Wan