Adaptive Fractional Order PI Controller Design for a Flexible Swing arm System Via Enhanced Virtual Reference Feedback Tuning

Abstract

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.

Robust Mittag‐Leffler stabilisation of fractional‐order systems

“…Fractional calculus is an old branch of mathematics that dates back since the early foundations of conventional integer-order calculus, three centuries ago. Nonetheless, the very first engineering applications of fractional-order tools were made available in last decades [1][2][3][4][5][6]. Furthermore, the study of nonlinear effects, as well as the implementation of non-smooth techniques in observation and stabilisation of fractional-order systems has been addressed in recent years [7].…”

Robust Cascade Path-Tracking Control of Networked Industrial Robot Using Constrained Iterative Feedback Tuning

“…Modeling a plant is an approximation of the real system with unmodeled dynamics and parametric uncertainties, and therefore the resulting path-tracking errors of model-based methods cannot converge to a small attenuation level if the identified model is inaccurate. Moreover, recent studies have revealed inherent fractional-order features in the servo drive systems, which increases the difficulties to design a model-based cascade controller for such servo-controlled robots [19], [20]. Another underlying problem is that the existing cascade controllers are usually tuned sequentially via different techniques, i.e., the inner secondary controller is tuned first to give a faster tracking response, followed by the optimization of the outer primary controller [21].…”

Computation of controllability and observability Gramians in modeling of discrete‐time noncommensurate fractional‐order systems

“…viscoelastic systems [12], electrochemistry [13,14], thermal systems and heat conduction [15][16][17], mechanical systems [18] and biology [19]. Furthermore, fractional-order controllers have been implemented to enhance the robustness and the performance of closed-loop control systems [20][21][22][23].…”