Design a PID Controller for Suspension System by Back Propagation Neural Network

Table 10

Comparison between actual gain parameters of PID and the BPN model (with newcf function).

Number	𝐾𝑃			𝐾𝐼			𝐾𝐷
	Actual	Predicted	Percentage of error	Actual	Predicted	Percentage of error	Actual	Predicted	Percentage of error
1	12614.31	12811.09	1.56	16743.32	18136.36	8.32	28345.20	29921.19	5.56
2	14590.41	14996.02	2.78	15234.90	15496.94	1.72	27670.82	29635.44	7.10
3	12090.59	12562.12	3.90	16045.61	17080.55	6.45	26674.96	27019.06	1.29
4	14278.66	14305.78	0.19	16396.63	16832.78	2.66	27869.42	29062.23	4.28
5	14797.23	15872.98	7.27	16853.16	17139.66	1.70	27001.56	29674.71	9.90
6	14856.34	15869.54	6.82	16166.90	16186.30	0.12	25438.94	26995.80	6.12
7	14879.61	15498.60	4.16	15879.61	15881.19	0.01	26730.38	28144.41	5.29
8	14898.49	15090.68	1.29	15990.53	17563.99	9.84	28593.77	28633.80	0.14
9	14478.19	15029.80	3.81	15069.52	16107.80	6.89	27433.71	29831.41	8.74
10	13967.83	14997.25	7.37	16649.37	17528.45	5.28	28082.82	31056.79	10.59
11	12889.63	13584.38	5.39	16298.48	17493.15	7.33	29782.51	32376.56	8.71
12	12794.88	13714.83	7.19	15589.92	16480.10	5.71	25967.69	26996.01	3.96
13	12797.34	14129.54	10.41	15590.29	16605.21	6.51	27420.55	28363.81	3.44
14	13312.90	14621.55	9.83	15690.33	16237.92	3.49	28890.69	30566.35	5.80