INTRODUCTION: The indirect adaptive robust control (IARC) can effectively improve servo performance under external disturbances, including thrust ripple. In the conventional IARC method, the recursive least squares (RLS) treats the low-pass-filtered total control signal as the measured output. Such filtering introduces parameter estimation errors caused by phase lag in the measured signal, whereas direct filter removal results in algebraic loop issues. OBJECTIVES: To address these limitations, a novel method using the indirect adaptive robust control with a measured-output-reconfigured RLS (IARC-MORRLS) is proposed in this paper. METHODS: Firstly, the proposed method utilizes the feedback control signal directly as the measured output of RLS, thereby improving the accuracy of parameter estimation by mitigating phase-lag-induced errors. Then, a variable-gain mechanism is developed to guarantee the complete convergence of parameter estimates. Finally, a slope-judgment mechanism is incorporated into projection mapping to suppress oscillations in initial parameter estimation. RESULTS: Simulations and experimental validations were used on a permanent magnet linear motor (PMLM) driven motion system. The validation experiment proves that IARC-MORRLS method proposed in this paper exhibits better performance in thrust ripple suppression over the conventional IARC method. CONCLUSION: It will help PMLM system reach better performance under external disturbances.