Table 10 Optimum gains of proposed and other controllers.

From: Hybrid adaptive ant lion optimization with traditional controllers for driving and controlling switched reluctance motors to enhance performance

Controllers − algorithms

Parameters

KPS

KIS

KDS

\(\lambda\) S

\(\mu\) S

KPC

KIC

KDC

\(\lambda\) C

\(\mu\) C

Hbup

Hbdown

PI-HAALO

30

8.299

0

1

0

20

0.8517

0

1

0

0

0

PID-PSO12

8.2724

19.1082

0.5159

1

1

16.8093

8.2129

0.3467

1

1

2.8493

− 3.9185

PID-PPSO12

20

19.9364

1.0257

1

1

18.9230

0.4408

0

1

1

11.0747

− 18.1291

PID-ALO12

4.8662

1.0845

0.0577

1

1

4.3679

0.0351

9.4898

1

1

0.2482

− 9.1854

PID-RIME

17.501

5.69854

874e−7

1

1

9.03235

0.0979

0.256

1

1

0

0

PID-GEO

20

3.199

0

1

1

1.5845

5.36412

9.6521

1

1

1.2907

0

PID-GA12

4.5341

10.4119

0.0773

1

1

10.5768

13.9666

15.9050

1

1

8.1984

− 15.8896

FOPID-HAALO

11.0214

8.0854

18.7841

0.9924

3448e−13

11.7069

7.7887

15.5277

0.82317

0.0071

3.2514

− 1.0254

FOPID-PSO12

3.0006

12.2041

1.0619

0.9351

0.3260

3.3328

14.0403

1.3530

0.3080

0.0719

20

− 17.7777

FOPID-PPSO12

0.3659

11.3479

2.0341

0.5613

0.3577

8.4507

14.4807

1.0683

0.7041

0.0177

6.5840

 − 8.0318

FOPID-ALO12

2.2623

8.0692

1.7222

0.7845

0.3448

13.7069

17.7887

18.5277

0.2317

5.0788e−5

12.3755

− 0.7385

FOPID-RIME

2.0145

12.2543

10.5412

0.94125

0.00147

15.15

0.6455

1.0365

0.5412

0.00014

1.23

− 0.145

FOPID-GEO

1.02145

16.9854

3.78412

0.9924

0.3448

18.7069

5.7887

10.257

0.3234

75e−7

18.3755

− 0.7385

FOPID-GA12

1.5312

12.7514

1.9267

0.9283

0.3410

13.0542

19.0341

0.6248

0.3180

0.0737

4.3712

− 12.1320

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