Iron-core linear motors have been widely used in high-speed/high-accuracy positioning systems due to the elimination of mechanical transmissions. Many control methodologies have been developed for linear motor motion control, such as H∞ control, adaptive control and sliding mode control. Compensations of various nonlinearities such as frictions and cogging forces have also been carried out to obtain better tracking performance. However, the relationship between the driving current and the resulting motor force has been assumed to be linear, which is invalid for high driving coil currents due to the saturating electromagnetic field effect. This paper focuses on the effective compensation of nonlinear electromagnetic field effect so that the system can be operated at even higher acceleration or heavier load without losing achievable control performance. Specifically, cubic polynomials with unknown weights are used for an effective approximation of the unknown nonlinearity between the electromagnetic force and the driving current. The effectiveness of such an approximation is verified by off-line identification experiments. An adaptive robust control (ARC) algorithm with online tuning of the unknown weights and other system parameters is then developed to account for various uncertainties. Theoretically, the proposed ARC algorithm achieves a guaranteed transient and steady-state performance for position tracking, as well as zero steady-state tracking error when subjected to parametric uncertainties only. Comparative experiments of ARC with and without compensation of electromagnetic nonlinearity done on a linear-motor-driven industrial gantry will be shown. The results show that the proposed ARC algorithm achieves better tracking performance than existing ones, validating the effectiveness of the proposed approach in practical applications.
Simplified Adaptive Robust Motion Control with Varying Boundary Discontinuous Projection of Hydraulic Actuator
