# Intelligent Parameter Identification for Robot Servo Controller Based on Improved Integration Method

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## Abstract

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## 1. Introduction

## 2. Classical Integration Method of Moment of Inertia and Viscous Friction Coefficient Identification

#### 2.1. Mechanical System Model

#### 2.2. Moment of Inertia Identification

#### 2.3. Viscous Friction Coefficient Identification

#### 2.4. Speed Loop Controller Parameter Auto-Tuning

## 3. Improved Integration Identification Method and IPNN-IGSA Optimization Method

- 1.
- The most important condition of the conventional integration method is that the speed and accelerated speed need to be periodic to ensure the system reach a steady state relatively, meaning that the sampling period is fixed. However, in fact, this condition is too rigorous, because the commands of the servomotor equipment are irregular under normal circumstances.
- 2.
- Several key parameters in the conventional integration method have preconditions. For example, the load torque ${T}_{L}$ needs to be a constant value or vary slowly during the identification period. Similarly, the moment of inertia ${J}_{m}$ and viscous friction coefficient ${B}_{m}$ also need to be constant or vary slowly. The motor torque constant ${K}_{t}$ needs to be accurate.
- 3.
- Although the conventional integration method has ability of noise reduction to decrease the quantization error from encoder, if the accelerated speed changes slowly, the quantization error will cover the real speed change information and make the system instability.

#### 3.1. Improved Integration Identification Method for Inertia and Friction Coefficient

#### 3.2. IPNN-IGSA Optimization Method for Speed Measurement Error

## 4. Simulation Results and Discussion

#### 4.1. Performance of the Improved Integration Method

#### 4.2. Performance of the Improved Integration Method combine with IGSA-IPNN

#### 4.3. Stability Analysis of the Proposed Method

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 1.**Comparisons in the case of $J={J}_{m},J=2{J}_{m},B=5{B}_{m}$. (

**a**) Speed response. (

**b**) Torque response.

**Figure 7.**Comparison of inertia and friction coefficient identification results at the sinusoidal reference speed of 10 Hz, 1500 r/min which the blue line represents the classical integration method and red line represents the improved integration method. (

**a**) Motor speed. (

**b**) Electrical torque. (

**c**) Acceleration. (

**d**) Moment of inertia. (

**e**) Viscous friction coefficient.

**Figure 8.**Comparison of inertia and friction coefficient identification results at the sinusoidal reference speed of 20 Hz, 3000 r/min which the blue line represents the classical integration method and red line represents the improved integration method. (

**a**) motor speed. (

**b**) electrical torque. (

**c**) acceleration. (

**d**) moment of inertia. (

**e**) viscous friction coefficient.

**Figure 10.**Comparison of inertia and friction coefficient identification results at the sinusoidal reference speed of 10 Hz, 1500 r/min which the blue line represents the single improved integration method and red line represents the improved integration method combine with IGSA-IPNN. (

**a**) moment of inertia. (

**b**) viscous friction coefficient.

**Figure 11.**The anti-disturbance performance of the inertia and friction coefficient identification results: (

**a**) Motor speed. (

**b**) Moment of inertia. (

**c**) Viscous friction coefficient.

Parameter | Value |
---|---|

Rated power | 600 W |

Rated torque | 1.91 N·m |

Rated current | 3.5 A |

DC link voltage | 220 V |

Pole pairs | 4 |

Stator resistance | 0.643 Ω |

d-axis inductance | 5.25 mH |

q-axis inductance | 12 mH |

Flux linkage | 0.175 Wb |

Inertia of servo motor | 2 × 10^{−3} kg·m^{2} |

Viscous friction coefficient of servo motor | 8 × 10^{−3} N·m·s |

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**MDPI and ACS Style**

Li, Y.; Wang, D.; Zhou, S.; Wang, X. Intelligent Parameter Identification for Robot Servo Controller Based on Improved Integration Method. *Sensors* **2021**, *21*, 4177.
https://doi.org/10.3390/s21124177

**AMA Style**

Li Y, Wang D, Zhou S, Wang X. Intelligent Parameter Identification for Robot Servo Controller Based on Improved Integration Method. *Sensors*. 2021; 21(12):4177.
https://doi.org/10.3390/s21124177

**Chicago/Turabian Style**

Li, Ye, Dazhi Wang, Shuai Zhou, and Xian Wang. 2021. "Intelligent Parameter Identification for Robot Servo Controller Based on Improved Integration Method" *Sensors* 21, no. 12: 4177.
https://doi.org/10.3390/s21124177