Portfolio of Jang Seok-Won, Age 60 - Founder of DeepNetwork, a one-person startup preparing for the commercialization of LLM-based AI and robotic joint control technology.

Portfolio of Jang Seok-Won, Age 60
Founder of DeepNetwork, a one-person startup preparing for the commercialization of LLM-based AI and robotic joint control technology.

CEO of DeepNetwork, a specialized one-person IT development startup
Contact:

• Email: sayhi7@daum.net

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Controlling d-axis and q-axis Currents of a PMSM with a PI Control Loop in Robotic Joint Control

The control of d-axis and q-axis currents in Permanent Magnet Synchronous Motors (PMSMs) through a PI control loop plays a pivotal role in robotic joint control. By employing Maximum Torque Per Ampere (MTPA) and Field Oriented Control (FOC) using Pulse Width Modulation (PWM), motor efficiency is maximized and performance is optimized. Below is a detailed explanation of the process, how torque and position control are managed, and how to tune the PI control gains.

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1. Overview of FOC (Field Oriented Control)

FOC is used to independently control the flux and torque of a motor by transforming motor control variables into orthogonal axes, namely:

• d-axis: Flux direction
• q-axis: Torque direction

Through FOC, motor currents are separated into d-axis and q-axis components and controlled individually to regulate the flux and torque independently.

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2. PI Control Loop and MTPA

The PI (Proportional-Integral) control loop manages the d-axis and q-axis currents to maintain desired values:

• d-axis current (Id): Affects flux control. For PMSMs, the Id is usually designed to remain close to zero to optimize flux control.
• q-axis current (Iq): Generates torque. It is adjusted to produce the desired torque.

MTPA is applied to achieve maximum torque output for a given current.

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3. Robotic Joint Control

In robotic joints, torque control and position control serve distinct purposes:

1. Torque Control
   • PI Control Loop: Manages the q-axis current (Iq) to control motor torque.
   • PI Control Gain Tuning:
     • Proportional Gain (Kp): Adjusts response speed. Excessive values may cause instability, while too low values result in slower response.
     • Integral Gain (Ki): Enhances precision. Excessively high Ki can cause overshoot or oscillations, while too low Ki reduces accuracy.
     • Tuning Method: Gains are adjusted using techniques like the Ziegler-Nichols method or through experimental optimization.
2. Position Control
   • Position Control Loop: Combines a velocity control loop with a torque control loop to manage motor position. Velocity control is usually achieved via q-axis current, which then supports position control.
   • PI Control Gain Tuning:
     • Proportional Gain (Kp): Affects position accuracy. High values may induce oscillations, while low values slow down response.
     • Integral Gain (Ki): Compensates for positional error. Excessively high values may cause instability, while low values reduce error compensation.
     • Tuning Method: Similar to PID tuning, adjustments depend on the system dynamics and motor characteristics.

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Kalman Filter Algorithm for Attitude Control: 85% Analysis Complete

Prediction Step:

1. Data Collection: Real-time acquisition of accelerometer and gyroscope data using the ICM20948 9-axis sensor.
2. State Prediction: Forecast the next state of the missile using the Kalman filter's state transition matrix (A) and control input matrix (B).
3. Error Covariance Prediction: Use the process noise covariance matrix (Q) to predict the error covariance matrix.

Update Step:

1. Measurement Update: Refine the state using sensor measurements.
2. State Correction: Adjust the state for precise missile trajectory.
3. Error Covariance Update: Update the error covariance matrix using the corrected state.

Noise Modeling and Compensation:

1. Noise Analysis: Analyze sensor noise characteristics to develop a noise model.
2. Compensation Algorithm: Enhance accuracy by applying a data correction algorithm based on the noise model.

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Phased Array Antenna System and Beamforming Control

DeepNetwork has successfully analyzed the detailed implementation of beamforming control in phased array antenna systems. This technology precisely adjusts the direction of signals in real time using multiple independently controlled antenna elements. It is essential for optimal signal quality and performance in radar, satellite communication, and military communication systems.

Key Features:

1. Precise Beam Steering: Controls the phase of each phase shifter to focus signals in specific directions, maximizing communication quality.
2. Dolph-Chebyshev Window Function Application: Minimizes interference by controlling the gain of the main lobe and side lobes, enhancing beam performance while optimizing frequency band usage.

Design and Optimization:

1. Phased Array Design: Develop high-performance phased arrays suited for defense and communication systems, ensuring reliability and stability in diverse applications.
2. Beamforming Control Algorithm: Real-time signal direction adjustment guarantees optimal performance in various communication environments.

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Role of Phase Shifters

Phase shifters adjust the phase of transmitted or received signals to combine or separate waves from multiple modules, enabling beamforming and multidirectional signal transmission.

1. Transmission: Adjusts wave phases from all T/R modules to focus transmission in specific directions.
2. Reception: Analyzes the phases of reflected signals to determine the position and strength of received signals.