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Flexible swing arm system (FSAS) is one of the most important components in the LED packaging industry. The trajectory tracking performance of the FSAS will directly affect the efficiency and accuracy of the LED packaging equipment. In order to meet the high precision and high speed requirements, this paper proposes an adaptive fractional order proportional integral (AFOPI) control method based on enhanced virtual reference feedback tuning (EVRFT). In this method, the AFOPI controller is applied to handle the fractional order characteristics of the FSAS. EVRFT is used to tune the AFOPI controller in a real-time way to accommodate the time-varying operating conditions. The proposed method is facilitated with two advantages: 1) only input/output measured data are fully utilized during the recursive tuning process without using model information of the controlled FSAS; 2) an improved adaptive law is incorporated in EVRFT to reduce the computation burden and provide an unbiased estimate for the ideal controller simultaneously. Thus, the conventional VRFT is enhanced both in efficiency and accuracy. The stability of the proposed method is guaranteed by rigorous theoretical analysis. Finally, experimental results are presented to verify the effectiveness of the EVRFT-based AFOPI controller.

This paper investigates a model-free tuning method of a fractional-order proportional–integral (FOPI) controller and its application for the speed regulation of a permanent magnet synchronous motor (PMSM). Firstly, the presented practical FOPI tuning method formulates the FOPI controller parameter identification problem via virtual reference feedback tuning (VRFT). Under the lack of accurate models, the proposed model-free method depends only on the measured input–output data of the closed-loop PMSM servo system. Secondly, Bode’s ideal transfer function is incorporated into the VRFT with consideration of the systematic fractional dynamics. Thus, the properties of the resulting system may be approximated to the desired fractional-order reference model. Thirdly, the proposed method fully considers optimal performance constraints on the stability requirements, sensitivity criteria, frequency-domain and time-domain characteristics. Then, the comprehensive optimization problem is derived and solved. Using suitably tuned parameters, the robustness and disturbance rejection ability of the VRFT-based FOPI control system are enhanced to achieve optimal performance. The convergence of the proposed method is proved by theoretical analysis. Finally, experimental results are presented to illustrate the effectiveness of the proposed model-free FOPI control method for the PMSM servo system.

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Industrial robots can be found in many manufacturing applications that suffer from imprecise position control of their own drive systems due to unknown external disturbances and parametric uncertainties. To address this problem, this paper proposes a robust cascade path-tracking control method to achieve better position control performance for a networked industrial robot. In the joint task space, the cascade control framework is formulated for the developed robotic actuation system, which consists of an inner speed loop and an outer position loop. Instead of exploring the conventional model-based approaches, a multiple degree-of-freedom constrained iterative feedback tuning (CIFT) method is presented to regulate the cascade controller by utilizing the monitored process data straightforwardly. With the integration of the normalized input constraints and position tracking error, the proposed CIFT method seeks an optimal solution to track the desired position profiles with satisfactory accuracy and improved robustness. Theoretical analysis is performed to verify the asymptotical convergence of the closed-loop system. Implemented on a real-time networked industrial robot, experimental results demonstrate that the proposed method can enhance the dynamic path tracking and system robustness during various operating situations. INDEX TERMS Cascade control, path-tracking, networked industrial robot, constrained iterative feedback tuning, input constraints.

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