1. Real-time and Deterministic Requirements

The current loop of a servo system is the innermost control loop and must complete the following operations within microseconds (μs):

High-Speed Sampling: Feedback signals from current sensors (e.g., Hall sensors or shunt resistors) need to be sampled at frequencies of 100 kHz to 1 MHz.
Fast Computation: Real-time computation of PID or other advanced control algorithms (e.g., Model Predictive Control) to generate PWM signals that drive power devices (e.g., IGBTs or SiC MOSFETs).
Low-Latency Response: The delay from signal sampling to outputting control signals must be controlled within 1 to 5 μs; otherwise, it can lead to motor torque ripple or loss of synchronization.

Advantages of FPGA:

Hardware Parallelism: FPGAs can process multiple signals simultaneously (e.g., three-phase currents, encoder A/B/Z pulses) without the need for task scheduling.
Deterministic Latency: Circuit path delays implemented through hardware logic are fixed, ensuring strict synchronization of control cycles.
Nanosecond-Level Response: FPGA logic gate circuits directly drive PWM generation modules, avoiding uncertainties introduced by software interrupts or operating system scheduling.

Limitations of ARM in Comparison:

ARM's task scheduling and interrupt response, based on operating systems (e.g., Linux or RTOS), exhibit microsecond-level jitter, making it difficult to meet the real-time requirements of high-precision current loops.
Even with ARM's hardware-accelerated peripherals (e.g., PWM timers), their flexibility and parallel processing capabilities are still inferior to FPGAs.

2. High-Speed Processing of Encoder Feedback

Modern servo systems use high-resolution encoders (e.g., 23-bit absolute encoders or linear scales) whose output signals require real-time decoding:

Incremental Encoders: Require capturing A/B pulse edges within every microsecond to calculate position and velocity.
Absolute Encoders: Require parsing high-speed serial protocols (e.g., EnDat2.2, BiSS-C) and verifying data integrity.
Multi-Axis Synchronization: In multi-axis coordinated control scenarios, multiple encoder signals must be processed simultaneously while maintaining phase synchronization.

FPGA Implementation:

Hardware Decoders: Encoder protocol parsing (e.g., SSI, BiSS) is implemented via state machines or dedicated logic, directly outputting position/velocity values.
Timestamp Recording: Precisely records the arrival time of pulse edges (nanosecond-level resolution) for velocity estimation and dynamic compensation.
Multi-Channel Parallelism: FPGAs can simultaneously process dozens of encoder signals, suitable for multi-axis robotics or CNC machine tool applications.

Limitations of ARM:

Relies on software interrupts or DMA transfers to process encoder signals, which are susceptible to interrupt delays, leading to accumulation of position estimation errors.
Data rates from high-resolution encoders may exceed the throughput capabilities of ARM peripherals (e.g., SPI or UART).

3. Hardware Acceleration for Current Loop

Current loop control algorithms frequently require floating-point operations and matrix operations (e.g., Clarke/Park transforms, Space Vector Pulse Width Modulation):

Clarke Transform: Converts three-phase currents into a two-phase stationary coordinate system (α-β).
Park Transform: Rotates the α-β coordinate system to the d-q coordinate system synchronized with the rotor magnetic field.
PID Control: Real-time calculation of d-axis and q-axis current errors to generate voltage commands.
SVPWM Generation: Converts voltage commands into PWM duty cycles to drive the inverter.

Optimized FPGA Implementation:

Parallel Pipelining: Decomposes algorithms into multi-stage pipelines, with each stage processed by dedicated hardware modules to improve throughput.
Fixed-Point Optimization: Uses fixed-point arithmetic instead of floating-point arithmetic to reduce resource consumption and increase computation speed.
Lookup Table (LUT) Method: Pre-stores sine tables or non-linear compensation parameters to reduce real-time computation load.
Dedicated IP Cores: Utilizes mathematical operation IP cores provided by FPGA vendors (e.g., CORDIC, complex multipliers) to accelerate transformation processes.

Limitations of ARM:

Even with NEON instruction sets or FPU units, ARM's serial computing architecture struggles to match FPGA's parallel processing capabilities.
High-frequency control loops (e.g., 100 kHz) consume significant CPU resources, affecting the execution of higher-level tasks (e.g., communication, trajectory planning).

4. System Architecture Division of Labor: ARM and FPGA Collaboration

In an ARM + FPGA architecture, the two have clear divisions of labor, leveraging their respective strengths:

ARM Core:
- Runs higher-level control logic (e.g., position loop, velocity loop, trajectory planning).
- Handles communication protocols (EtherCAT, CANopen).
- Manages file systems, user interfaces, and fault diagnostics.
FPGA Logic:
- Executes low-level real-time tasks (current loop, encoder feedback, PWM generation).
- Implements high-speed peripheral interfaces (encoders, ADCs, digital I/O).
- Provides hardware protection functions (overcurrent, overvoltage, short-circuit protection).

Typical Data Flow:

The FPGA real-time acquires current and encoder data, completes current loop calculations, and outputs PWM.
The ARM reads processed data from the FPGA via a high-speed bus (e.g., AXI), and executes velocity loop and position loop algorithms.
The ARM sends target current commands to the