Abstract
To get better control performance in motor control, more and more researches tend to apply non-linear control laws in the field of motor control. However, most conventional non-linear control theory is based on explicit model of controlled object and often resulting in complexity. Besides, the control parameters tuning is mainly aiming at stability of the system. No valid direct performance-oriented non-linear control theory has been proposed. Facing the limitations, this paper presents a direct motor position control in an implicit data-driven manner. Unlike conventional non-linear motor controls that are based on explicit models and with stability-based parameters tuning, this study gives performance-oriented non-linear control by mastering non-linear discrete optimal control law in an implicit data-learning manner. Firstly, optimal data of position tracking problem is obtained by solving optimization problem. Secondly, the implicit discrete optimal control law hidden in data is learned by a BP neural network. Finally, the learned control law is implemented in real-time control to reproduce optimal control performance. Simulation and experiment results validated the feasibility of the data-driven controller, which could be helpful for performance-oriented non-linear control designs. The merits and further improvements are also discussed.
Highlights
Conventional motor control designs are based on explicit models or relations
This paper explores the feasibility of directly using an artificial neural network to fully realize position control of a motor, while learning a discrete optimal control law that is difficult to achieve
This section provides a further discussion on the proposed direct position control by artificial neural network through optimal data learning, including comparison with PID controller, the reason for slower response in experiment, as well as the comparison of continuous and discrete control law
Summary
Conventional motor control designs are based on explicit models or relations. For example, the commonly used PID three-loop position control structure in engineering is based on frequency domain model. In [33], fuzzy neural network is used with model reference control for position control of a ultrasonic motor. In the literatures, the neural network has not been directly used as a main controller for motor position servo control, nor is it used for pursuing optimal. This paper explores the feasibility of directly using an artificial neural network to fully realize position control of a motor, while learning a discrete optimal control law that is difficult to achieve. The neural network is used to learn a discrete optimal control law hidden in optimal position tracking data. Inspired by the progress of artificial intelligence applications, numerically obtaining the optimal control data and implicitly mastering the hidden discrete optimal control law by a neural network are proposed in this paper.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
