Luận văn thạc sĩ Kỹ thuật điều khiển và tự động hóa: Điều khiển hệ phi tuyến có trễ sử dụng bộ dự báo Smith và bộ điều khiển pi mờ

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NGUYӈN THANH HÀ

&KѭѫQJ7ӘNG QUAN TÌNH HÌNH NGHIÊN CӬU 3

6ѫOѭӧc nhӳng nghiên cӭu liên quan 3

ѬӟFOѭӧng thӡi gian trӉ cӫa hӋ thӕng vӟi bӝ ÿLӅu khiӇn Mӡ 7

2.2.1 Giҧi thuұWѭӟFOѭӧng tham sӕ ÿӕLWѭӧng 18

2.2.2 Giҧi thuұWѭӟFOѭӧng thӡi gian trӉ cӫa hӋ thӕng 23

2.3 Bӝ ÿLӅu khiӇn PI ± Mӡ 25

2.3.1 Cҩu trúc bӝ ÿLӅu khiӇn Mӡ 25

2.3.2 ThiӃt kӃ bӝ ÿLӅu khiӇn Mӡ dӵa vào kinh nghiӋm 28

2.3.3 ThiӃt kӃ bӝ ÿLӅu khiӇn PI-Mӡ 28

2.4 KӃt quҧ mô phӓng 33

2.4.1 Nhұn dҥng tham sӕ ÿӕLWѭӧng dùng thuұWWRiQÿӋ qui 33

ĈLӅu khiӇQÿӕLWѭӧng dùng bӝ ÿLӅu khiӇn PI-Mӡ 34

2.4ĈLӅu khiӇn hӋ thӕng có trӉ sӱ dөng bӝ ÿLӅu khiӇn Smith predictor Mӡ 35

2.5 KӃt luұQFKѭѫQJ 38

&KѭѫQJ0Ð+Î1+7+ӴC NGHIӊM 39

6ѫOѭӧc vӅ mô hình thӵc nghiӋm 39

3.2.1 PLC S7-1200 41

3.2.2 BiӃn tҫn Shihlin SS2-021-0.75K 42

3.2.3 Radar measurement Time-of-Flight Micropilot FMR51 45

0i\EѫP3DQDVRQLF*3-129JXK 46

3.3 KӃt nӕi PLC S7-1200 vӟi Matlab thông qua KEPServerEx 47

3.3.1 KӃt nӕi S7-1200 vӟi KEPServerEx 47

3.3.2 KӃt nӕi Matlab vӟi KEPServerEx 50

DANH MӨC HÌNH ҦNH SӰ DӨNG TRONG LUҰ19Ă1

Hình 1.1: K͇t qu̫ ÿL͉u khi͋n khi dùng Smith predictor và conventional PID 3

Hình 1.2: K͇t qu̫ ÿL͉u khi͋n cͯa decoupled predictor và Smith predictor 4

Hình 1.3: C̭XWU~FÿL͉u khi͋n thích nghi c̵p nh̵t thông s͙ cho b͡ ÿL͉u khi͋n 5

Hình 1.4: C̭u trúc b͡ ÿL͉u khi͋Q6PLWKSUHGLFWRUWU˱ͫt 6

+uQK6˯ÿ͛ kh͙i cͯa b͡ ÿL͉u khi͋n Self-adaptive Smith Mͥ 6

Hình 1.6: C̭XWU~FÿL͉u khi͋n h͏ th͙ng 7

+uQKĈiSͱng ngõ ra y, ym và A(k) khi thͥi gian tr͍ P{KuQKșm =0 8

Hình 1.8: C̭u trúc h͏ th͙ng logic mͥ 10

Hình 1.9: Giá tr͓ ngôn ngͷ bi͇n vào/ra 11

+uQK6˯ÿ͛ kh͙i mô ph͗ng trong Matlab 12

+uQKĈiSͱng ngõ ra vͣLÿ͙LW˱ͫQJÿ˱ͫF˱ͣFO˱ͫng chính xác 12

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+uQKĈiSͱng ngõ ra khi mô hình có sai s͙ 13

+uQKĈiSͱng y và ym , A(k) và thͥi gian tr͍ cͯDP{KuQKșm 14

+uQK6˯ÿ͛ ÿL͉u khi͋QGQJP{KuQKÿ͙LW˱ͫng 16

+uQK6˯ÿ͛ ÿL͉u khi͋n h͏ th͙QJW˱˯QJÿ˱˯QJYͣi hình 2.1 16

+uQK6˯ÿ͛ ÿL͉u khi͋n vͣi Smith predictor 17

Hình 2.4: Tín hi͏u vào, ra cͯDÿ͙LW˱ͫng 18

Hình 2.5:Mô hình Hammerstein 19

Hình 2.6:Mô hình Wiener 19

+uQK6˯ÿ͛ kh͙LÿL͉u khi͋n thích nghi 20

+uQK6˯ÿ͛ ˱ͣFO˱ͫQJEuQKSK˱˯QJW͙i thi͋u 20

+uQK6˯ÿ͛ kh͙LÿL͉u khi͋n Smith predictor Mͥ 25

+uQK6˯ÿ͛ kh͙i b͡ ÿL͉u khi͋n Mͥ 25

Hình 2.20: K͇t qu̫ nh̵n d̩ng dùng thu̵WWRiQÿ͏ qui 34

Hình 2.21: S˯ÿ͛ 6LPXOLQNÿL͉u khi͋n PI-Mͥ cho b͛n chͱDÿ˯Q 35

Hình 2.23: Tín hi͏XÿL͉u khi͋QYjÿiSͱng h͏ th͙ng khi có thͥi gian tr͍ 36

+uQK6˯ÿ͛ 6LPXOLQNÿL͉u khi͋n Smith predictor Mͥ 36

Hình 2.25: Tín hi͏XÿL͉u khi͋QYjÿiSͱng h͏ th͙ng khi s͵ dͭng b͡ ÿL͉u khi͋n Smith predictor Mͥ 37

Hình 2.26: K͇t qu̫ ˱ͣFO˱ͫng thͥi gian tr͍ h͏ th͙ng 37

+uQK6˯ÿ͛ mô hình th͹c nghi͏m 39

+uQK6˯ÿ͛ ÿ̭u n͙i dây tín hi͏XÿL͉u khi͋n cho bi͇n t̯n 43

Hình 3.9: Radar measurement Time-of-Flight Micropilot FMR51 45

+uQK0i\E˯P3DQDVRQLF*3-129JXK 46

Hình 3.11: Ch͕n chu̱n k͇t n͙i vͣi S7-1200 47

+uQKĈ̿t tên cho kênh k͇t n͙i 47

Hình 3.18: K͇t qu̫ k͇t n͙i S7-1200 vͣi KEPServerEx 49

Hình 3.19: Ki͋m tra tr̩ng thái k͇t n͙i giͷa S7-1200 vͣi KEPServerEx 50

Hình 3.20: OPC Toolbox trong Matlab Simulink 50

Hình 3.21: C̭u hình cho block OPC Configuration 51

Hình 3.22: C̭u hình cho block OPC Read 51

Hình 3.23: C̭u hình cho block OPC Write 52

Hình 4.12: Tín hi͏XÿL͉u khi͋n u 61 +uQKĈiSͱng ngõ ra h͏ th͙ng và tín hi͏Xÿ̿t 61 Hình 4.14: Thͥi gian tr͍ ˱ͣFO˱ͫng cͯa h͏ th͙ng 62

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Hình 1.1: K͇t qu̫ ÿL͉u khi͋n khi dùng Smith predictor và conventional PID

y(t)-dat

y(t)-conventional PID y(t)-Smith predictor

Hình 1.2: K͇t qu̫ ÿL͉u khi͋n cͯa decoupled predictor và Smith predictor

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y(t)-Smith predictor y(t)-decoupled predictor

Hình 1.3: C̭XWU~FÿL͉u khi͋n thích nghi c̵p nh̵t thông s͙ cho b͡ ÿL͉u khi͋n

x ĈLӅXNKLӇQ*OXFRVHYӟLEӝÿLӅXNKLӇQ6PLWKSUHGLFWRUWUѭӧWFKREӋQKQKkQWLӇXÿѭӡQJ[5@1KӳQJEӋQKQKkQWLӇXÿѭӡQJSKөWKXӝFYjRYLӋFFXQJFҩSLQVXOLQÿӇJLӳOѭӧQJÿѭӡQJWURQJPiXQҵPWURQJJLӟLKҥQFKRSKpS7URQJQJKLrQFӭXQj\QKyPWiFJLҧWKLӃWNӃKDLEӝÿLӅXNKLӇQYzQJOһSNtQÿӇFKӕQJOҥLQKӳQJQJX\FѫKҥÿѭӡQJKX\ӃW%ӝÿLӅXNKLӇQÿѭӧFWKLӃWNӃEӣLYLӋFVӱGөQJÿLӅXNKLӇQWUѭӧWYjEӝGӵEiR6PLWK%ӝGӵEiR6PLWKFyWiFGөQJÿLӅXNKLӇQGӵEiRFKRKӋWKӕQJFyWUӉGRÿyFҫQP{KuQKÿӝQJOӵFKӑFYjѭӟFOѭӧQJWKӡLJLDQWUӉFӫDKӋWKӕQJ.ӃWTXҧP{SKӓQJFKRWKҩ\YLӋFÿLӅXNKLӇQOѭӧQJ ÿѭӡQJWURQJPiXFKRNӃWTXҧWӕWNӇFҧNKLFyQKLӉX 6ѫÿӗÿLӅXNKLӇQÿѭӧFWKӇKLӋQӣKuQK

Hình 1.4: C̭u trúc b͡ ÿL͉u khi͋Q6PLWKSUHGLFWRUWU˱ͫt

x 1JKLrQFӭXEӝÿLӅXNKLӇQ6HOI-DGDSWLYH6PLWK0ӡFKRKӋWKӕQJÿLӅXNKLӇQPҥQJSKkQWiQ>@7URQJÿyEӝÿLӅXNKLӇQORJLF0ӡVӁFKӍQKQKӳQJWKDP

VӕKp, Ki, Kd FӫDEӝÿLӅXNKLӇQ3,'%ӝÿLӅXNKLӇQ6HOI-DGDSWLYH6PLWK0ӡVӁFyQKӳQJѭXÿLӇPFӫDEӝGӵEiR6PLWKYjEӝÿLӅXNKLӇQ0ӡ-PID, nên FyWKӇNKҳFSKөFÿѭӧFQKӳQJKҥQFKӃNKLP{KuQKѭӟFOѭӧQJNK{QJFKtQKxác YӟLP{KuQKWKӵFGRÿyVӁFҧLWKLӋQÿѭӧFFKҩWOѭӧQJÿLӅXNKLӇQFӫDKӋWKӕQJ6ѫÿӗÿLӅXNKLӇQÿѭӧFWKӇKLӋQӣKuQK

+uQK6˯ÿ͛ kh͙i cͯa b͡ ÿL͉u khi͋n Self-adaptive Smith Mͥ

1KѭYұ\ÿmFyQKLӅXQJKLrQFӭXYӅÿLӅXNKLӇQKӋWKӕQJFyWUӉFKRWKҩ\YLӋFѭӟFOѭӧQJFiFWKDPVӕYjWKӡLJLDQWUӉFӫDKӋWKӕQJUҩWTXDQWUӑQJ6DXNKLFyQKӳQJWKDPVӕYjWKӡLJLDQWUӉFӫDKӋWKӕQJNӃWKӧSYӟLQKӳQJSKѭѫQJSKiSÿLӅXNKLӇQEWUӉQKѭEӝGӵEiR6PLWKGHFRXSOHGSUHGLFWRUEӝÿLӅXNKLӇQ6PLWKSUHGLFWRUWUѭӧW EӝÿLӅXNKLӇQ6HOI-DGDSWLYH6PLWK0ӡÿӇÿLӅXNKLӇQÿѭӧFKӋWKӕQJFyWUӉÿҥWÿѭӧFFKҩWOѭӧQJPRQJPXӕQ YjORҥLWUӯҧQKKѭӣQJFӫDQKLӉX

1.2 ѬӟFOѭӧng thӡi gian trӉ cӫa hӋ thӕng vӟi bӝ ÿLӅu khiӇn Mӡ

TKHR>@QKyPWiFJLҧÿmJLҧ VӱWKDPVӕP{KuQKÿѭӧFѭӟFOѭӧQJWѭѫQJÿӕLFKtQK[iFVӱGөQJEӝÿLӅXNKLӇQORJLF0ӡÿӇѭӟFOѭӧQJWKӡLJLDQWUӉFKѭDELӃWFӫDKӋWKӕQJ6ѫÿӗNKӕLÿLӅXNKLӇQÿѭӧFWKӇKLӋQӣKuQK

Hình 1.6: C̭XWU~FÿL͉u khi͋n h͏ th͙ng

7URQJFҩXWU~FQj\EDRJӗPÿӕLWѭӧQJEӝGӵEiR6PLWKQҵPWURQJÿѭӡQJQpWÿӭW YjEӝÿLӅXNKLӇQORJLF0ӡÿӇFKӍQKWKӡLJLDQWUӉFӫDP{KuQK%ӝGӵEiR6PLWKGQJÿӇORҥLEӓҧQKKѭӣQJWKӡLJLDQWUӉOrQKӋWKӕQJEӝÿLӅXNKLӇQ0ӡGQJÿӇѭӟFOѭӧQJWKӡLJLDQWUӉFӫDÿӕLWѭӧQJ7ӯVѫÿӗFҩXWU~FKӋWKӕQJWDFy

7URQJÿyș OjWKӡLJLDQWUӉFӫDKӋWKӕQJșm OjWKӡLJLDQWUӉP{KuQKy là ngõ UDKӋWKӕQJym OjQJ}UDFӫDP{KuQKYju OjQJ}YjREӝÿLӅXNKLӇQ1ӃXT Tz m thì EӝGӵEiR6PLWKVӁNK{QJFyWiFGөQJĈһW

QӃXÿiSӭQJFӫDy và ym không trùng nhau, và T Tm WKuÿiSӭQJQJ}UDy và ym VӁ

1x 10

Y Ym

00.511.522.533.544.5x 10

A(k)

.KLFKѭDFKӍQKWKӡLJLDQWUӉFӫDP{KuQKTm zT, ngõ ra y FӫDKӋWKӕQJYjym

FӫDP{KuQKNK{QJWUQJQKDXGүQÿӃQA(k) WăQJQKDQKYjÿk\OjNӃWTXҧNK{QJ

PXӕQ0ӛLNKLTm #T thì A(k) VӁNK{QJWăQJQӳDWҥRWKjQKPӝWÿѭӡQJWKҷQJQҵPQJDQJ  9u Yұ\ A(k) WăQJ QӃX y, ym NK{QJ WUQJ QKDX KRһF A(k) QҵP QJDQJ NKL

m #

TT7ӯQKӳQJWUҥQJWKiLWUrQWDFyWKӇÿѭDUDOXұW0ӡ

&ҩXWU~FFѫEҧQFӫDÿLӅXNKLӇQ0ӡÿѭӧFWKӇKLӋQWURQJKuQK7URQJÿyFѫEӕQWKjQKSKҫQFKӫ\ӃXFӫDÿLӅXNKLӇQ0ӡOjPӡKyDSKѭѫQJSKiSVX\GLӉQKӋTXLWҳFYjJLҧLPӡ[8]

.KӕLÿҫXWLrQWURQJKӋPӡFѫEҧQOjNKӕLPӡKyDNKӕLQj\FyFKӭFQăQJELӃQÿәLJLiWUӏU}VDQJJLiWUӏQJ{QQJӳKD\QyLFiFKNKiFVDQJWұSPӡYuKӋTXLWҳFPӡFKӍFyWKӇVX\GLӉQWUrQFiFWұSPӡ4XL WҳFPӡOjSKiWELӇXQӃX-WKuWURQJÿyPӋQKÿӅÿLӅXNLӋQYjPӋQKÿӅNӃWOXұQOjFiFPӋQKÿӅPӡ

Hình 1.8: C̭u trúc h͏ th͙ng logic mͥ

ĈӏQKQJKƭDFiFJLiWUӏQJ{QQJӳFӫDELӃQe, ǻe, ǻT QKѭKuQK

(y )

' ¦¦

6ѫÿӗNKӕLP{SKӓQJWURQJPDWODE

Hình 1.106˯ÿ͛ kh͙i mô ph͗ng trong Matlab

Y dat Y

Hình 1.12ĈiSͱng y và ym , A(k), thͥi gian tr͍ cͯDP{KuQKșm

YYm

A(k)

theta

Y dat Y

Hình 1.14ĈiSͱng y và ym , A(k) và thͥi gian tr͍ cͯDP{KuQKșm

- ѬӟF OѭӧQJ WKDP Vӕ ÿӕL WѭӧQJ EҵQJ WKXұW WRiQ ÿӋ TXL NK{QJ WtQK PD WUұQQJKӏFKÿҧR>9]

YYm

A(k)

theta

- 6ӱGөQJJLҧLWKXұWѭӟFOѭӧQJWKӡLJLDQWUӉFӫDKӋWKӕQJWKHR>10].

- 6ӱGөQJEӝÿLӅXNKLӇQ3,-0ӡNӃWKӧSYӟLEӝGӵEiR6PLWKÿӇÿLӅXNKLӇQKӋWKӕQJ

1.4 KӃt luұQFKѭѫQJ

&KѭѫQJÿmSKkQWtFKWәQJTXDQWuQKKuQKQJKLrQFӭXNӃWOXұQU~WUDWӯYLӋFSKkQWtFKQKѭVDX

1rXNKiLTXiWPӝWVӕF{QJWUuQKQJKLrQFӭXFyOLrQTXDQÿӃQÿLӅXNKLӇQKӋWKӕQJFyWKӡLJLDQWUӉ

;k\GӵQJEӝѭӟFOѭӧQJ0ӡGQJÿӇѭӟFOѭӧQJWKӡLJLDQWUӉFKѭDELӃWFӫDKӋWKӕQJQKѭQJYүQFKѭDѭӟFOѭӧQJÿѭӧFWKDPVӕ ÿӕLWѭӧQJ 7X\QKLrQSKѭѫQJSKiSѭӟFOѭӧQJWKӡLJLDQWUӉQj\FҫQFyP{KuQKFKtQK[iFFӫDÿӕLWѭӧQJGRÿyNKyiSGөQJYjRWKӵFWӃ

3ĈѭDUDÿѭӧFJLҧLSKiSWKӵFKLӋQPөFWLrXÿӅWjLJӗPJLҧLWKXұWѭӟFOѭӧQJWKӡLJLDQWUӉFӫDKӋWKӕQJJLҧLWKXұWQKұQGҥQJWKDPVӕÿӕLWѭӧQJYjEӝÿLӅXNKLӇQ

Hình 2.2: 6˯ÿ͛ ÿL͉u khi͋n h͏ th͙QJW˱˯QJÿ˱˯QJYͣi hình 2.1

Ngõ ra quá trình lúc này là: y(s) y (t* T) YӟLWKӡLJLDQWUӉOjș9ӟLVѫÿӗ

NKӕLӣWUrQWKuFyWKӇORҥLEӓҧQKKѭӣQJFӫDWKӡLJLDQWUӉUDNKӓLKӋWKӕQJEӣLYuNK{QJFyWKӡLJLDQWUӉWURQJYzQJOһS

Tuy nhiên, P{KuQKOX{QOX{QFyVDLVӕVRYӟLÿӕLWѭӧQJYjFNJQJNK{QJWKӇWiFKWKӡLJLDQWUӉUDNKӓLKӋWKӕQJ6ѫÿӗÿLӅXNKLӇQ EVDLVӕP{KuQKYjORҥLEӓҧQKKѭӣQJFӫDWKӡLJLDQWUӉOrQKӋWKӕQJÿѭӧFWKӇKLӋQWURQJKuQK

Hình 2.3: 6˯ÿ͛ ÿL͉u khi͋n vͣi Smith predictor

7ӯVѫÿӗNKӕLEӝGӵEiR6PLWKWURQJKuQK7DÿѭӧF SKѭѫQJWUuQKÿһFWUѭQJFӫDKӋWKӕQJlà:

%ӝGӵEiR6PLWKÿLӅXNKLӇQKӋWKӕQJFyWUӉEiPWKHRWtQKLӋXÿһWWӕWNKLYLӋFѭӟFOѭӧQJWKDPVӕmô hình FӫDÿӕLWѭӧQJ là WѭѫQJÿӕLFKtQK[iFVDLVӕQKӓFyWKӇGүQGӃQVӵPҩWәQÿӏQKFӫDKӋWKӕQJNKLÿӝOӧLFӫDEӝÿLӅXNKLӇQOӟQ%ӝGӵEiR

6PLWKFyWKӇÿѭӧFFKӍQKNKL[HP[pWP{KuQKVDLVӕWKHR/HHet al 1999 [1]

1KѭYұ\PXӕQÿLӅXNKLӇQKӋWKӕQJFyWUӉYӟLEӝGӵEiR6PLWKWKuFҫQFyQKӳQJSKѭѫQJSKiSѭӟFOѭӧQJWKDPVӕYjWKӡLJLDQWUӉFӫDKӋWKӕQJ

2.2 ѬӟFOѭӧng tham sӕ hӋ thӕng

2.2.1 Giҧi thuұWѭӟFOѭӧng tham sӕ ÿӕi Wѭӧng

2.2.1.1 Mô hình h͛i qui tuy͇n tính

&KRÿӕLWѭӧQJFyWtQKLӋX vào là u(k),WtQKLӋXUDOjy(k) QKѭKuQK

Hình 2.4: Tín hi͏u vào, ra cͯDÿ͙LW˱ͫng

*LҧVӱTXDQKӋJLӳDWtQKLӋXYjRYjWtQKLӋXUD FӫDKӋWKӕQJFyWUӉÿѭӧF P{WҧEҵQJSKѭѫQJWUuQKVai phân:

2.2.1.2 C̭u trúc mô hình h͏ phi tuy͇n

7KHR>@FyWKӇVӱGөQJPӝWWURQJQKӳQJSKѭѫQJSKiSVDXÿk\ÿӇQKұQGҥQJP{KuQKKӋSKLWX\ӃQ

- Mô hình Hammerstein: khâu phi tX\ӃQWƭQKJKpSQӕLWLӃSNKkXWX\ӃQWtQK QKѭhình 2.5

y k T M k T

7URQJÿyFiFSKҫQWӱKӗLTXLOjKjP SKLWX\ӃQ EҩWNǤFӫDWtQKLӋXYjRYjWtQKLӋXUDWURQJTXiNKӭ

7\WKXӝFYjRFiFKFKӑQ vHFWRUKӗLTXLij(k) WӯWtQKLӋXYào YjWtQKLӋXUDWURQJTXiNKӭYjKjPSKLWX\ӃQg(ij(k),ș) mjWDFyFiFGҥQJP{KuQKSKLWX\ӃQNKiFQKDX

1JRjLUDFzQFyWKӇWKDPNKҧRQKұQGҥQJP{KuQKKӋSKLWX\ӃQEҵQJFiFKGQJPҥQJWKҫQNLQKKRһFP{KuQK0ӡWURQJ>]

7URQJÿӅWjLQj\FKӑQSKѭѫQJSKiS GӵEiRWtQKLӋXUDFӫDKӋSKLWX\ӃQEҵQJEӝGӵEiRKӗLTXLWX\ӃQWtQK

2.2.1.3 Thu̵WWRiQÿ͏ TXL˱ͣFO˱ͫng tham s͙

7KXұWWRiQѭӟFOѭӧQJÿӋTXLWKѭӡQJÿѭӧFVӱGөQJWURQJFiFKӋWKӕQJÿLӅXNKLӇQWKtFKQJKLQKҵPÿҧPEҧRFKҩWOѭӧQJÿLӅXNKLӇQNKLWKDPVӕP{ KuQKWKD\ÿәL, hình 2.7

+uQK6˯ÿ͛ kh͙LÿL͉u khi͋n thích nghi

9LӋFWtQKWRiQWKDPVӕP{KuQKWUӵFWX\ӃQSKҧLÿѭӧFWKӵFKLӋQVDRFKRYLӋF[ӱOêGӳOLӋXÿRWҥLPӛLWKӡLÿLӇPOҩ\PүXSKҧLFKҳFFKҳQKRjQWҩWWURQJNKRҧQJWKӡLJLDQQKӓKѫQFKXNǤOҩ\PүX 9ӟLVѫÿӗѭӟFOѭӧQJEuQKSKѭѫQJWӕLWKLӇXÿѭӧFWKӇKLӋQWURQJKuQK

+uQK6˯ÿ͛ ˱ͣFO˱ͫQJEuQKSK˱˯QJW͙i thi͋u

&KӍWLrXѭӟFOѭӧQJEuQKSKѭѫQJWӕLWKLӇXFyWUӑQJVӕOj

&{QJWKӭFWUrQNK{QJWKӇiSGөQJWKӡLJLDQWKӵFYuNKLWKӡLJLDQKӋWKӕQJKRҥWÿӝQJFjQJGjLVӕPүXGӳOLӋXVӁWăQJOrQGүQÿӃQWăQJWKӡLJLDQWtQKWRiQYjWUjQEӝQKӟ'RÿyFҫQF{QJWKӭFÿӋTXLNK{QJFҫQOѭXWUӳWRjQEӝFiFPүXGӳOLӋXYjNKӕLOѭӧQJWtQKWRiQNK{QJWăQJOrQWKHRWKӡLJLDQ

7KXұWWRiQѭӟFOѭӧQJÿӋTXL

 

O  M  M

Ö( ) Ö( 1) ( ) ( )Ö

 MO  M  M

ª    M M  º

O¬ O  M  M ¼

9HFWRUWKDPVӕ ÖT FyWKӇѭӟFOѭӧQJWUӵFWX\ӃQWӯF{QJWKӭFWUrQ'Rÿyngõ UDFӫDTXiWUuQKFyWKӇѭӟFOѭӧQJRQOLQH

2.2.2 Giҧi thuұWѭӟFOѭӧng thӡi gian trӉ cӫa hӋ thӕng

mnb

( ) Ö( )Ö( )

d i

Ö( )

d k QrQFKX\ӇQWKjQKOjPӝWVӕQJX\rQ

6DXNKLѭӟFOѭӧQJÿѭӧFWKDPVӕÿӕLWѭӧQJYjWKӡLJLDQWUӉKӋWKӕQJNӃWKӧSYӟLEӝÿLӅXNKLӇQ3,± 0ӡVѫÿӗÿLӅXNKLӇQ6PLWKSUHGLFWRU0ӡFӫDKӋWKӕQJÿѭӧFWKӇKLӋQWURQJKuQK

+uQK6˯ÿ͛ kh͙LÿL͉u khi͋n Smith predictor Mͥ

2.3 Bӝ ÿLӅu khiӇn PI ± Mӡ

2.3.1 Cҩu trúc bӝ ÿLӅu khiӇn Mӡ

ӬQJGөQJÿҫXWLrQFӫDÿLӅXNKLӇQ0ӡOjÿLӅXNKLӇQÿӝQJFѫKѫLQѭӟFGQJKӋTXLWҳFPӡ0DPGDQLQăP1Jj\QD\FyUҩWQKLӅXKӋWKӕQJÿLӅXNKLӇQWURQJF{QJQJKLӋSYjGkQGөQJiSGөQJSKѭѫQJSKiSÿLӅXNKLӇQ0ӡQKѭĈLӅXNKLӇQKӋWKӕQJWKҳQJYjWăQJWӕFFӫD[HOӱDKӋWKӕQJOiL[HÿLӅXNKLӇQURERWÿLӅXNKLӇQPi\JLһWPi\ҧQKWӵÿӝQJ«

ĈLӅXNKLӇQ0ӡFXQJFҩSSKѭѫQJ SKiSÿӇELӉXGLӉQ[ӱOêYjWKӵFWKLWULWKӭFWUӵFJLiFFӫDFRQQJѭӡLYjNLQKQJKLӋPFKX\rQJLDÿѭӧFWtFKKӧSYjREӝÿLӅXNKLӇQ0ӡWURQJTXiWUuQKWKLӃWNӃKӋWKӕQJ

Hình 2.10: 6˯ÿ͛ kh͙i b͡ ÿL͉u khi͋n Mͥ

6ѫÿӗNKӕLFӫDEӝÿLӅXNKLӇQ0ӡWUuQKEj\ӣKuQK JӗPWKjQKSKҫQFKtQKOjEӝÿLӅXNKLӇQ0ӡFѫEҧQYӟLEDNKӕLFKӭFQăQJOjPӡKyDKӋTXLWҳFPӡYjJLҧLPӡ7KӵFWӃWURQJPӝWVӕWUѭӡQJKӧSNKLJKpSEӝÿLӅXNKLӇQ0ӡYjRKӋWKӕQJÿLӅX

NKLӇQFҫQWKrPKDLNKӕLWLӅQ[ӱOêYjKұX[ӱOê&KӭFQăQJFӫDWӯQJ NKӕLWURQJVѫÿӗWUrQÿѭӧFP{WҧVDXÿk\

7LӅQ[ӱOê

BӝÿLӅXNKLӇQ0ӡFѫEҧQOjEӝÿLӅXNKLӇQWƭQKĈӇFyWKӇÿLӅXNKLӇQÿӝQJFҫQFyWKrPFiFWtQKLӋXYLSKkQWtFKSKkQFӫDJLiWUӏÿRQKӳQJWtQKLӋXQj\ÿѭӧFWҥRUDEӣLFiFPҥFKYLSKkQWtFKSKkQWURQJ NKӕLWLӅQ[ӱOê 7tQKLӋXYjREӝÿLӅXNKLӇQWKѭӡQJOjJLiWUӏU}WӯFiFPҥFKÿREӝWLӅQ[ӱOêFyFKӭFQăQJ[ӱOêFiFJLiWUӏÿRQj\WUѭӟFNKLÿѭDYjREӝÿLӅXNKLӇQ0ӡFѫEҧQ.KӕLWLӅQ[ӱOêFyWKӇOѭӧQJWӱKyDKRһFOjPWUzQJLiWUӏÿRFKXҭQKyDKRһFWӍOӋJLiWUӏÿRYjRWҫPJLiWUӏFKXҭQOӑFQKLӉX

%ӝÿLӅXNKLӇQ0ӡFѫEҧQ

7KjQKSKҫQFKtQKFӫDEӝÿLӅXNKLӇQ0ӡFѫEҧQOjKӋTXLWҳFÿLӅXNKLӇQKӋTXLWҳFQj\FyWKӇU~WUDWӯNLQKQJKLӋPFKX\rQJLDWURQJYLӋFÿLӅXNKLӇQÿӕLWѭӧQJ

- KkXPӡKyDFKX\ӇQJLiWUӏU}SKҧQKӗLWӯQJ}UDFӫDÿӕLWѭӧQJWKjQKJLiWUӏPӡÿӇKӋTXLWҳFFyWKӇVX\OXұQÿѭӧF

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