Adaptive Learning Gain-Based Control for Nonlinear Systems With External Disturbances: Application to PMSM

The performance of backstepping control with an external disturbance guarantees the upper bound of the tracking error, which is determined by an unknown upper bound of the disturbance and design parameters. Therefore, tuning the design parameters is necessary to obtain a satisfactory control performance. Generally, a high gain is used to reduce the tracking error under the unknown disturbance in the previous methods. However, the use of an unnecessarily high gain may amplify the high-frequency measurement noise and peaking phenomenon. In this study, we propose an adaptive learning gain (ALG)-based nonlinear control for a strict-feedback nonlinear system with an external disturbance to achieve the desired control performance without any information about the disturbance and gain tuning. An ALG-based nonlinear controller using a backstepping procedure is designed to track the desired trajectory. The ALG update law is established to suppress the tracking error within the desired bound. The amplification of the measurement noise in the control input is reduced, as the ALG is updated to avoid the use of an unnecessarily high gain when the controller achieves more than the desired performance.