Abstract
Electromagnetic actuator systems composed of an induction servo motor (ISM) drive system and a rice milling machine system have widely been used in agricultural applications. In order to achieve a finer control performance, a witty control system using a revised recurrent Jacobi polynomial neural network (RRJPNN) control and two remunerated controls with an altered bat search algorithm (ABSA) method is proposed to control electromagnetic actuator systems. The witty control system with finer learning capability can fulfill the RRJPNN control, which involves an attunement law, two remunerated controls, which have two evaluation laws, and a dominator control. Based on the Lyapunov stability principle, the attunement law in the RRJPNN control and two evaluation laws in the two remunerated controls are derived. Moreover, the ABSA method can acquire the adjustable learning rates to quicken convergence of weights. Finally, the proposed control method exhibits a finer control performance that is confirmed by experimental results.
Highlights
Compared to other three-phase motors, three-phase induction motors (IMs) are widely used in many industrial and commerce applications due to their simple structures and easy maintenance
In order to achieve better control performance, IMs have served as induction servo motors (ISMs) via structural improvement and encoder installation
ISMs have been broadly applied to various servo fields such as computer numerical control (CNC) machine tools and milling machines [1,2,3,4]
Summary
Compared to other three-phase motors, three-phase induction motors (IMs) are widely used in many industrial and commerce applications due to their simple structures and easy maintenance. In order to achieve better control performance, IMs have served as induction servo motors (ISMs) via structural improvement and encoder installation. ISMs have been broadly applied to various servo fields such as computer numerical control (CNC) machine tools and milling machines [1,2,3,4]. Li et al [1] proposed a new intelligent adaptive CNC system design for a milling machine by using the neural network controller to achieve better control characteristics. Huang et al [2] proposed an approach for cutting the force control of CNC machines. This approach with a state estimator was executed by using the observed variables and cutting force to achieve robust control. Gomes and Sousa [3]
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