Research on Active Disturbance Rejection Control with Parameter Tuning for Permanent Magnet Synchronous Motor Based on Improved PSO Algorithm
Abstract
:1. Introduction
- Logistic chaotic mapping initialization. Replacing random initialization with logistic chaotic mapping optimizes the distribution of population particles within the value range, ensuring a more uniform distribution and improving the convergence speed.
- Linearly decreasing inertia weight function. Introducing a linearly decreasing inertia weight function to adaptively adjust the inertia weight improves the acceleration constants by incorporating a dynamic learning function, which adjusts the acceleration constants corresponding to the number of population iterations, thus enhancing adaptability.
- Genetic algorithm integration. Combining genetic algorithms to select particles for crossover and mutation based on fitness and introducing a Gaussian probability function to help escape local optima, thereby improving optimization accuracy.
2. Mathematical Model of ADRC and PMSM
2.1. SPMSM Mathematical Model
2.2. ADRC Mathematical Model
3. Improved PSO Algorithms
3.1. Basic PSO Algorithms
Algorithm 1 Particle swarm optimization algorithm. |
Input: Population size, N, dimension, d, cognition constants, c1 and c2, inertia weight, w. Output: The best solution, gbest(t) 1: Initialization 2: while Terminate condition has not been met do 3: for j ← 1 to N do 4: Compute v(j) according to Equation (16) 5: Compute x(j) according to Equation (17) 6: Evaluate particle fitness, f(x(j)) 7: Update the best personal solution, p(j) 8: Update the best global solution, g(j) 9: end for 10: end while 11: Select the best solution, gbest(t) |
3.2. IPSO Algorithms
3.2.1. Particle Initialization Method Based on Logistic Chaos Initialization
3.2.2. Adaptive Weights and Dynamic Cognitive Factors
3.2.3. Particle Update Method Based on Genetic Algorithm Cross-Mutation
4. Implementation of IPSO Algorithm in ADRC
- Initialize the IPSO algorithm and determine the inertia weight, dynamic learning factor function, and population interval. Perform logistic chaotic initialization for the three-dimensional population particles within the given parameter range. Calculate each particle’s velocity, current position, and fitness, then determine the initial optimal position and optimal fitness of the population.
- Calculate the population’s average fitness, , and minimum fitness, , and assess whether the particle’s fitness is less than the average fitness. Compute the corresponding inertia weight for the particle, and update its velocity and position based on the calculated fitness.
- Select particles for crossover operations, evaluate both parent and offspring particles, update their respective positions, calculate the particle fitness, and update the individual and population historical optimal fitness.
- Perform particle mutation using a Gaussian probability function, replace the parent particles with mutated particles, calculate their fitness, and update the individual and population historical optimal fitness.
- Check if the maximum number of iterations has been reached. If reached, terminate the iteration; otherwise, return to step 2.
- Output the optimal fitness value and position of the particles and obtain the parameters required for ADRC.
Algorithm 2 IPSO algorithm. |
Input: Population size, N, dimension, d, cognition constants, c1 and c2, inertia weight, w, crossover probability, pc, mutation probability, pm. Output: The best solution, gbest(t) 1: Logistic chaos initialization according to Equation (18) 2: while Terminate condition has not been met do 3: Compute c1(j) and c2(j) according to Equation (21) 4: for i ← 1 to N do 5: if (f(x(i))<fa) then 6: Compute w(i) according to Equation (19) 7: end if 8: Compute v(i) according to Equation (16) 9: Compute x(i) according to Equation (17) 10: Evaluate particle fitness, f(x(i)) 11: Update the best personal solution, p(i) 12: Update the best global solution, g(i) 13: end for 14: for j ← 1 to cross-pool do 15: Crossover (seed1, seed2) according to (22) 16: Evaluate particle fitness, f(x(j)) 17: Update the best global solution, g(j) 18: end for 19: for k ← 1 to mutation pool do 20: Mutation (seed3) according to (24) 21: Evaluate particle fitness, f(x(k)) 22: Update the best global solution, g(k) 23: end for 24: end while 25: Select the best solution, gbest(t) |
5. Experimental Results and Analysis of PMSM Control System
5.1. Simulation Model Construction
5.2. Experimental Results and Analysis
5.3. Experiment Verification
- (1)
- Speed-tracking test
- (2)
- Anti-turbulence test
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
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PSO | APSO | CPSO | IPSO | |
---|---|---|---|---|
ITAE (10−3) | 9.4 | 9.0 | 9.3 | 7.4 |
Motor Parameters | Parameter Value | Unit |
---|---|---|
Flux linkage | 0.175 | Wb |
Moment of inertia | 0.003 | kg·m2 |
Damping coefficien | 0.008 | N·m·s |
Stator resistance | 2.875 | Ω |
Stator inductance | 8.500 | mH |
Number of pole pairs | 4 |
PSO | APSO | CPSO | IPSO | |
---|---|---|---|---|
Speed value (r/min) | 757 | 760 | 762 | 768 |
Fluctuated value | 43 | 40 | 38 | 32 |
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Zhou, Z.; Wang, L.; Wang, Y.; Zhou, X.; Tong, Y. Research on Active Disturbance Rejection Control with Parameter Tuning for Permanent Magnet Synchronous Motor Based on Improved PSO Algorithm. Electronics 2024, 13, 3436. https://doi.org/10.3390/electronics13173436
Zhou Z, Wang L, Wang Y, Zhou X, Tong Y. Research on Active Disturbance Rejection Control with Parameter Tuning for Permanent Magnet Synchronous Motor Based on Improved PSO Algorithm. Electronics. 2024; 13(17):3436. https://doi.org/10.3390/electronics13173436
Chicago/Turabian StyleZhou, Ziyang, Liming Wang, Yang Wang, Xinlei Zhou, and Yipin Tong. 2024. "Research on Active Disturbance Rejection Control with Parameter Tuning for Permanent Magnet Synchronous Motor Based on Improved PSO Algorithm" Electronics 13, no. 17: 3436. https://doi.org/10.3390/electronics13173436