The permanent magnet synchronous motor (PMSM) is widely used in the robotics and motion control systems due to its compact structure, low noise and high torque to inertia ratio. PMSM is a typical multivariable and nonlinear system with strong coupling and uncertain model parameters. Therefore, traditional linear control methods such as PID control cannot guarantee high control performance for PMSM systems.
Though many advanced nonlinear control methods including adaptive control, nonlinear optimal control, fuzzy logic control and neural network control have been used for PMSM systems, they have their own advantages and shortcomings.
Ri Tae Hyong, a researcher at the Robotics Institute, has proposed a full order terminal sliding mode (FOTSM) controller to solve the speed tracking problem for a PMSM system by combining RBF neural network and FOTSM control scheme.
First, in order to eliminate chattering in conventional sliding mode control (SMC), he developed a continuous FOTSM technique. Then, he designed a FOTSM speed controller based on RBF neural network, in consideration of the presence of external disturbance.
The simulations and PMSM speed control experiments by MATLAB have proved that the proposed method is effective.
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