Adaptive joint motor control and structural co-optimization strategy for high-precision robots

Abstract

The purpose of this study is to improve the performance of high-precision robot joints in terms of control accuracy and running stability, focusing on the adaptive control and optimal design of permanent magnet synchronous motor. In view of the influence of time-varying disturbance and measurement noise, this study gives a strict stability proof to ensure that the system meets the uniform ultimate boundedness (UUB), and corrects the misjudgment of traditional analysis methods under nonlinear conditions. By constructing a response surface model with sensitive weight of magnetic parameters, the accuracy and optimization range of the model are improved, and the total harmonic distortion of stator current is included in the optimization target. The simulation incorporated the nonlinear characteristics of the inverter. To assess the rationality of the motor structural design, a quantitative evaluation framework was established, and a dedicated simulation module was developed to verify joint control accuracy and operational stability. This study thereby achieved a closed-loop analysis spanning motor optimization and system-level performance validation. These extensions effectively fill the analysis gap in the process of transforming control algorithm and motor performance into actual robot joint application. The results suggest that the improved algorithm empowers accurate parameter identification. The motor efficiency increased from 91.50% to 91.95%, while the cogging torque decreased from 148.51 mN·m to 20.84 mN·m, representing a reduction of 85.99%. In the joint-level simulations, low-speed velocity fluctuations were suppressed to below 1.2%, and the root-mean-square tracking error at high speeds was reduced by 35%. Comparative analyses with recursive least squares and the non-dominated sorting genetic algorithm III (NSGA-III) verified the effectiveness and superiority of the proposed method. These findings provide simulation-based evidence supporting the practical compatibility and potential application value of the proposed approach in high-precision robotic joint systems.