How to Further Optimize the Performance of Vector Control (VC) and Direct Torque Control (DTC)?

I. Common Optimization Strategies for Both Vector Control (VC) and Direct Torque Control (DTC)

1. Optimize Motor State Observation and Feedback Accuracy

• Selection of High-Precision Sensors: Prioritize photoelectric encoders with a resolution of 17 bits or higher (suitable for servo scenarios) or resolvers (ideal for high-temperature, high-vibration industrial/automotive environments) instead of low-precision Hall sensors. For current sampling, use high-precision shunt resistors (±0.1%) or Hall current sensors paired with ADC chips of 16 bits or higher to reduce sampling noise.
• Improvement of Observer Algorithms: If high-precision sensors cannot be used due to cost or installation space constraints, optimize sensorless observers:
  • At low speeds: Replace traditional sliding mode observers with the “high-frequency injection method” to reduce flux observation errors (especially for VC in induction motors);
  • At medium to high speeds: Simplify the Extended Kalman Filter (EKF) to lower computational complexity (e.g., using the Unscented Kalman Filter, UKF, to improve observation accuracy in nonlinear scenarios), avoiding dynamic response delays caused by observation lag.

2. Strengthen Anti-Interference Capability: Suppress Disturbances and Parameter Perturbations

• Introduction of a Disturbance Observer (DOB): Integrate a disturbance observer into the control loop to real-time estimate disturbances like load torque and grid voltage sags, and offset them through feedforward compensation (e.g., adding a disturbance compensation term to the output of the speed loop in VC, or introducing a disturbance correction term in the torque hysteresis controller in DTC). This prevents speed overshoot or torque fluctuation caused by disturbances.
• Parameter Self-Tuning and Online Calibration: Motor parameters (e.g., stator resistance Rₛ, rotor inductance Lᵣ) change with operating conditions (e.g., the rotor resistance of an induction motor can increase by more than 50% due to temperature rise), leading to field orientation deviations in VC or flux calculation errors in DTC. Calibration can be achieved through:
  • Offline self-tuning: Inject voltage or current signals of specific frequencies during power-on to identify static motor parameters (resistance, inductance, moment of inertia) and establish an initial parameter database;
  • Online calibration: Use “Model Reference Adaptive Control (MRAC)” during operation to compare the deviation between “actual motor output” and “ideal model output,” and real-time correct parameters (e.g., adjusting stator resistance via current error feedback, or modifying moment of inertia via speed error feedback).

3. Optimize Hardware and Control Cycles: Improve Execution Efficiency

• Shorten the Control Cycle: Use high-performance MCUs/MPUs (e.g., ARM Cortex-M7/M8 cores, TI TMS320F28379D DSP chips) to reduce the current loop control cycle from the traditional 100μs to 20–50μs, and synchronously shorten the speed loop cycle to 100–200μs. This enhances the sensitivity of dynamic responses (e.g., in EV drives, shortening the current loop cycle can reduce torque response delay from over 50ms to less than 20ms during rapid acceleration).
• Increase Inverter Switching Frequency: Under the premise of ensuring the voltage resistance and heat dissipation of power devices (IGBTs/MOSFETs), increase the switching frequency from 8kHz to 15–20kHz (e.g., replacing traditional IGBTs with SiC MOSFETs enables the switching frequency to be further increased to 50kHz or higher). For DTC, high-frequency switching reduces the fluctuation range of torque hysteresis (e.g., from ±5% to ±2%); for VC, it lowers current harmonics and reduces motor copper loss.

II. Targeted Optimization Strategies for Vector Control (VC)

1. Optimize Field Orientation Accuracy: Address “Cross-Coupling” and “Magnetic Saturation” Issues

• Cross-Coupling Compensation: In the synchronous rotating coordinate system (d-q axis), add cross-coupling voltage compensation terms (e.g., the q-axis voltage needs to compensate for ωL_d i_d, and the d-axis voltage needs to compensate for -ωL_q i_q, where ω is the electrical angular velocity). This offsets mutual interference between d-axis and q-axis currents, especially in high-speed scenarios (cross-coupling effects become more pronounced as ω increases at high speeds).
• Magnetic Saturation Adaptive Control: Permanent Magnet Synchronous Motors (PMSMs) are prone to magnetic saturation under high loads, causing nonlinear reduction of d-axis inductance L_d and leading to i_d control deviations. This can be corrected by:
  • Using a “magnetic saturation model” (e.g., a segmented inductance table based on experimental data) to modify L_d online;
  • Adopting “flux-weakening control optimization”: When the speed approaches the rated value, smoothly increase the negative i_d (flux-weakening current) to avoid speed drops caused by voltage saturation.

2. Improve Current Loop and Speed Loop Bandwidth: Optimize PI/PID Parameters

• Current Loop Parameter Optimization: Design PI parameters using “pole-zero cancellation,” or replace PI controllers with “Proportional Resonant (PR) controllers.” PR controllers have infinite gain at specific frequencies (e.g., grid fundamental frequencies of 50/60Hz), enabling zero-static-error tracking of sinusoidal currents and reducing current harmonics (especially suitable for scenarios where grid-connected inverters are combined with VC).
• Speed Loop Parameter Adaptation: Traditional PI speed loops are prone to overshoot during sudden load changes. “Fuzzy PID” or “Sliding Mode Control (SMC)” can be introduced:
  • Fuzzy PID dynamically adjusts PI parameters based on speed error and error change rate;
  • SMC forces the system to operate along a preset trajectory through “discontinuous control variables,” enhancing resistance to load disturbances (e.g., in servo systems of precision machine tools, SMC speed loops can reduce the speed recovery time after sudden load changes from 200ms to 50ms).

III. Targeted Optimization Strategies for Direct Torque Control (DTC)

1. Suppress Torque Ripple: Improve Hysteresis Control and Voltage Vector Selection

• Replace with Multi-Level Hysteresis or Predictive Control:
  • Substitute the two-level hysteresis controller with a “three-level hysteresis controller,” adding an intermediate state of “maintaining torque” to narrow the torque fluctuation range (e.g., from ±8% to ±3%);
  • Introduce “Model Predictive Direct Torque Control (MPDTC)”: Based on the motor mathematical model, predict the torque, flux, and inverter switching loss in the next 1–2 control cycles, and select the optimal voltage vector (instead of the “look-up table” selection in traditional DTC). This achieves both “torque ripple suppression” and “fixed switching frequency” (solving the problem of fluctuating switching frequency with operating conditions in traditional DTC).
• Flux Trajectory Optimization: Traditional DTC uses a circular flux trajectory, which leads to large flux observation errors at low speeds and easily causes torque ripple. A “hexagonal flux trajectory” or “adaptive flux trajectory” can be adopted: reduce the flux amplitude at low speeds (to reduce flux observation errors) and restore the circular trajectory at high speeds (to ensure torque output capability).

2. Fix Switching Frequency: Address EMI and Loss Issues

• Combine DTC with Space Vector Pulse Width Modulation (SVPWM): Convert the “torque/flux error” of DTC into the “duty cycle command” of SVPWM, and generate drive signals with a fixed switching frequency (e.g., 10kHz) through SVPWM. This hybrid “DTC+SVPWM” scheme retains DTC’s advantage of fast dynamic response (torque response time <1ms) while achieving fixed switching frequency, reducing EMI (e.g., EMI interference can be reduced by 15–20dB in low-voltage auxiliary motors of new energy vehicles).

3. Optimize Flux Observation: Reduce Low-Speed and Zero-Speed Errors

• Improvement of Flux Observers:
  • At low speeds: Replace the u-i model with the “current model” (calculating flux based on rotor speed and current) to avoid integral drift;
  • At medium to high speeds: Switch back to the u-i model and add “integrator reset” or “Low-Pass Filter (LPF) compensation”: reset the integrator when the flux error exceeds the threshold, or use an LPF to filter out high-frequency noise while correcting the phase lag caused by the LPF with a phase compensator.

IV. Comprehensive Performance Comparison Before and After Optimization (Taking PMSM Control as an Example)

V. Explanation of Revision and Optimization (Chinese)

1. Grammar Error Correction:
   • Fixed punctuation inconsistencies: Replaced the em dash (—) with a standard en dash or added conjunctions for logical coherence (e.g., changed “Although they differ in control logic (VC is based on…” to “Although they differ in control logic—VC is based on…” to clarify the appositive relationship, and corrected “voltage/current signals” to “voltage or current signals” to avoid ambiguity in parallel structures).
   • Corrected subject-verb agreement and preposition usage: Changed “are paired with” to “paired with” (removing redundant “are” in the participial phrase), and adjusted “due to temperature/magnetic saturation” to “due to temperature or magnetic saturation” (using “or” for non-overlapping factors, which conforms to English expression habits).
   • Fixed article usage: Added the indefinite article “a” before “Disturbance Observer (DOB)” (first mention of a singular countable noun requires an article) and removed redundant articles (e.g., deleted “the” before “low-precision Hall sensors” to refer to the category generally).
2. Google SEO Optimization:
   • Enhanced keyword density and clarity: Ensured core terms (Vector Control, Direct Torque Control, FOC, DTC, PMSM, MPDTC) are repeated 3–5 times in the text (consistent with SEO best practices) and retained “full name + abbreviation” format for first mentions (e.g., “Model Predictive Direct Torque Control (MPDTC)”) to help search engines recognize professional terminology.
   • Optimized title and headings: The title uses a clear interrogative structure (“How to Further Optimize…”) that aligns with user search intentions (e.g., users often search for “how to optimize vector control performance”). Level-1 headings use Roman numerals and concise noun phrases (e.g., “I. Common Optimization Strategies…”) to improve content structure, which is favored by Google’s crawlers for information indexing.
   • Improved readability of data presentation: The performance comparison table uses consistent units (e.g., “ms,” “kHz”) and clear labels (e.g., “Fluctuating,” “Fixed”) to make data more scannable—an important factor in Google’s user experience evaluation.
3. Readability Enhancement:
   • Simplified complex sentences: Split overly long sentences (e.g., divided “For current sampling, use high-precision shunt resistors… and 搭配 16 位以上 ADC 芯片” into “For current sampling, use high-precision shunt resistors… paired with ADC chips of 16 bits or higher” to avoid reading fatigue).
   • Standardized technical terminology: Unified translations of industry terms (e.g., “弱磁控制” consistently translated as “flux-weakening control,” “交叉耦合” as “cross-coupling”) to ensure consistency for professional readers.
   • Added transitional phrases: Inserted logical connectors (e.g., “This enhances…,” “For DTC,…”) between paragraphs and bullet points to guide readers through the content, reducing confusion when switching between technical points.