Investigation of Different Servo Motor Designs for Servo Cycle Operations and Loss Minimizing Control Performance

Flieh, Huthaifa; Lorenz, Robert D.; Totoki, Eigo; Yamaguchi, S.; Nakamura, Yuichiro

doi:10.1109/tia.2018.2849725

Cited by 26 publications

(21 citation statements)

References 22 publications

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“…al, [19] revealed loss reduction and Flieh et. al, demonstrated loss minimization [20] and dead-beat control [21] in addition to self-sensing [22,23], the precursor to using the physics-based dynamics for virtual sensoring [24] following the illustration of optimality in [25] and selfsensoring [26] specifically applied to DC motors.…”

Section: Learning Teachniquesmentioning

confidence: 99%

“…The momentum continued, and the nonlinear output regulation has been further explored by numerous authors including Cheng, Tarn, and Spurgeon [39], Khalil [40], and Wang and Huang [41] across autonomous and nonautonomous systems. The lineage emphasized in this manuscript stems from a heritage in vehicle guidance and control techniques [8][9][10][11][12][13][14][15][16] extended to apply to motor controllers [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37] This manuscript proposes a preferred instantiation of adaptive and learning systems [28,29] by evaluating the efficacy of motor control techniques based on iterated computational rates and system discretization. The materials and methods in section 2 first describe model discretization and then introduces the two compared: one adaptive and one learning each with interconnected lineage of research in the literature.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Adaptive and Learning in Unmanned Systems

Koo¹,

Travis²,

Sands³

2022

Preprint

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This study determines the threshold for the computational rate of actuator motor controllers for unmanned underwater vehicles necessary to accurately follow discontinuous square wave commands. Motors must track challenging square-wave inputs, and identification of key computational rates permit application of deterministic artificial intelligence (D.A.I.) to achieve tracking to a machine-precision degree of accuracy in direct comparison to other state-of-art approaches. All modeling approaches are validated in MATLAB simulations where the motor process is discretized at varying step-sizes (inversely proportional to computational rate). At a large step-size (fast computational rate), discrete D.A.I. shows a mean error more than three times larger than that of a ubiquitous model-following approach. Yet, at a smaller step size (slower computational rate), the mean error decreases by a factor of 10, only three percent larger than that of continuous D.A.I. Hence, the performance of discrete D.A.I. is critically affected by the sampling period for discretization of the system equations and computational rate. Discrete D.A.I. should be avoided when small step-size discretization is unavailable. In fact, continuous D.A.I. has surpassed all modeling approaches which makes it the safest and most viable solution to future commercial applications in unmanned underwater vehicles.

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Section: Learning Teachniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of Adaptive and Learning in Unmanned Systems

Koo¹,

Travis²,

Sands³

2022

Preprint

View full text Add to dashboard Cite

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“…And the combination of the level taken by each factor making the S/N ratio of UMP η 2 largest is A(2)B(3)C(1)D (1). The combination of the level taken by each factor making the S/N ratio of torque fluctuation and S/N ratio of UMP largest is different.…”

Section: Ratio Of Torque Fluctuation η 1 Largest Is A(2)b(3)c(1)d(3)mentioning

confidence: 99%

“…The effects of B and D on the UMP are larger than those on the torque fluctuation, hence the levels taken by factors B and D are to make the UMP smallest. From the above, the levels taken by each factor are A(2)B(3)C(1)D (1). The final optimized structure is shown in Fig.…”

Section: Calculation Of Ump Optimization Scheme Of the Improved Pmmentioning

confidence: 99%

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Optimal Design of Permanent Magnet Structure to Reduce Unbalanced Magnetic Pull in Surface-Mounted Permanent-Magnet Motors

Wang

Liu

et al. 2020

IEEE Access

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For the surface-mount permanent magnet synchronous motors (SPMSM) with fractional slot concentrated windings, the winding distribution of per phase under different poles is uneven, which results in unbalanced magnetic pull (UMP) between the stator and rotor. UMP has an important impact on the performance and service life of the motor. In this paper, a new permanent magnet (PM) structure is proposed to reduce UMP and torque fluctuation of the fractional slot SPMSM. Furthermore, a 48p/54s SPMSM is taken as an example, the proposed PM structure is optimized by the Taguchi method which can comprehensively consider the control factors and noise factors. The effects of various variables and process error on UMP, torque fluctuation and robustness of results are analyzed respectively. Finally, the optimized PM structure is obtained, which improves the operation stationarity. INDEX TERMS Fractional slot SPMSM, unbalanced magnetic pull(UMP), torque fluctuation, Taguchi method.

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