gm演算法
Ⅰ 閲嶅姏鍔犻熷害鎬庝箞綆
閲嶅姏鍔犻熷害綆楁硶濡備笅錛
閲嶅姏鍔犻熷害鎸囩殑鏄鑷鐢辮惤浣撹繍鍔ㄤ腑鐗╀綋鍦ㄩ噸鍔涗綔鐢ㄤ笅鐨勫姞閫熷害錛岄氬父鐢╣琛ㄧず銆傚湪鍦扮悆琛ㄩ潰闄勮繎錛岄噸鍔涘姞閫熷害鐨勬爣鍑嗗間負9.8綾蟲瘡浜屾℃柟縐掋備笅闈㈡槸鍏充簬閲嶅姏鍔犻熷害鐨勮︾粏浠嬬粛銆
1銆佷粈涔堟槸閲嶅姏鍔犻熷害錛
5銆侀噸鍔涘姞閫熷害鐨勫簲鐢
鍦ㄧ墿鐞嗗︺佸湴璐ㄥ﹀拰鑸絀鴻埅澶╃瓑棰嗗煙錛岄噸鍔涘姞閫熷害鍏鋒湁騫挎硾鐨勫簲鐢ㄤ環鍊箋備緥濡傦紝閫氳繃嫻嬮噺閲嶅姏鍔犻熷害鍙浠ユ帹鏂鎵鍦ㄤ綅緗鐨勫湴璐ㄦ瀯閫狅紝浜嗚В鍦扮悆鍐呴儴鐨勭粨鏋勫拰瀵嗗害鍙樺寲錛涘湪鑸絀恆佸煎脊鍒跺肩瓑鏂歸潰錛岄噸鍔涘姞閫熷害涔熸槸榪涜岀簿紜璁$畻鐨勫熀紜銆
Ⅱ 楂樹腑鐗╃悊 - 瀹囧畽鑸琛岋細鐗╀綋闅忓湴鐞冭嚜杞鐨勫悜蹇冨姞閫熷害涓庣幆緇曞湴鐞冭繍琛岀殑鍏杞鍚戝績鍔犻熷害鐨勭畻娉曟瘮杈冦
棣栧厛瑙i噴涓嬪悜蹇冨姞閫熷害銆傛垜浠閮界煡閬撳悜蹇冨姞閫熷害a=v^2/R=w^2R,鑰岀嚎閫熷害v=2蟺R/T,瑙掗熷害w=2蟺/T錛岀墿浣撻殢鍦扮悆鑷杞鐨勫悜蹇冨姞閫熷害a1 = w^2R0 = (2蟺/T)^2R0,姝ゅ叕寮忓氨寰堝ソ瑙i噴浜嗐
褰撳崼鏄熺幆緇曞湴鐞冭繍琛屾椂錛屽湴鐞冧笌鍗鏄熺殑涓囨湁寮曞姏鍏呭綋浜嗗崼鏄熺幆緇曞湴鐞冭繍琛岀殑鍚戝績鍔涳紝鍗矲寮=m鍗鏄焌=G M鍦扮悆m鍗鏄/r^2,鐢變簬m鍗鏄>0,m鍗鏄熷湪絳夊紡涓よ竟鍚屾椂娑堝幓,鍗沖緱a=GM鍦扮悆/r^2,榪欏氨鏄鍗鏄熺幆緇曞湴鐞冭繍琛岀殑鍚戝績鍔犻熷害銆備笉鐭ラ亾鎴戣В閲婃竻妤氫簡娌℃湁?
Ⅲ 錛堟案紓佸悓姝ヤ氦嫻佺數鏈猴紝浣嶇疆鎺у埗錛屽炲姞闈炵嚎鎬ф帶鍒舵敼榪涙柟妗堬級 鏈夋病鏈夌浉鍏崇殑璁烘枃鍟婏紝瑕佸寘鎷浣嶇疆鎺у埗鐨剘~
緇欎綘涓鐐瑰弬鑰冨惂
閽堝規案紓佸悓姝ョ數鍔ㄦ満闈炵嚎鎬у姩鎬佹暟瀛︽ā鍨嬶紝閲囩敤鐩存帴鍙嶉堢嚎鎬у寲鎺у埗錛屽緩絝嬮棴鐜緋葷粺鐨勮緭鍏-杈撳嚭妯″瀷錛岄氳繃綰挎у寲妯″瀷鏉ヨ捐℃帶鍒跺櫒錛岃ユ柟娉曠畝鍗曢傜敤錛涘悓鏃訛紝涓轟簡鍏嬫湇姝ゅ弽棣堢嚎鎬у寲鎺у埗瀵規ā鍨嬭佹眰綺劇『鍖栬繖涓涓嶈凍錛屾枃涓鎻愬嚭浜嗗熀浜庣伆鑹茬悊璁虹殑涓嶇『瀹氶勬祴鍣錛屽畠鑳藉湪綰塊勬祴姘哥佸悓姝ョ數鏈虹殑涓嶇『瀹氬洜緔犲苟鐩稿簲鐨勮皟鏁村弽棣堢嚎鎬у寲鎺у埗娉曞垯錛屼粠鑰屾彁楂樹簡緋葷粺鐨勫姩鎬佹ц兘銆備豢鐪熺粨鏋滆〃鏄庯紝璇ユ柟娉曞規案紓佸悓姝ョ數鏈洪熷害鎺у埗鍏鋒湁寰堝ソ鐨勮窡韙鎬ц兘鍜岄瞾媯掓ц兘銆
鍏抽敭璇嶏細鐏拌壊鐞嗚洪勬祴鍙嶉堢嚎鎬у寲姘哥佸悓姝ョ數鍔ㄦ満
姘哥佸悓姝ョ數鍔ㄦ満錛圥MSM錛変互鍏朵紭鑹鐨勬ц兘鍦ㄤ己鏈嶆帶鍒剁郴緇熻幏寰椾簡騫挎硾鐨勫簲鐢ㄣ傚湪姘哥佸悓姝ョ數鍔ㄦ満鐨勬帶鍒朵腑錛岀敱浜庤漿瀛愯漿閫熷拰瀹氬瓙鐢墊祦鐨勯潪綰挎ц﹀悎浣垮緱緋葷粺鍏鋒湁寰堝己鐨勯潪綰挎э紝鐗瑰埆鍦ㄧ郴緇熷瓨鍦ㄤ笉紜瀹氭ф椂錛岃繖縐嶉潪綰挎т嬌寰楃郴緇熼毦浜庤揪鍒伴珮綺懼害浼烘湇銆傚湪姘哥佸悓姝ョ數鍔ㄦ満榪愯岃繃紼嬩腑錛岀數鏈虹殑瀹氬瓙鐢甸樆銆佺矘婊炴懇鎿︾郴鏁板拰璐熻澆杞鐭╅兘鍙鑳藉彂鐢熷緢澶х殑鍙樺寲錛岃繖浜涘弬鏁扮殑鍙樺寲蹇呯劧褰卞搷鍒扮郴緇熺殑浼烘湇綺懼害銆備負浜嗚В鍐蟲案紓佸悓姝ョ數鍔ㄦ満綺劇『浼烘湇鎺у埗闂棰橈紝褰撳墠閲囩敤鐨勯潪綰挎ф帶鍒舵柟娉曚富瑕佹湁鍙樼粨鏋勬帶鍒躲佸井鍒嗗嚑浣曞拰鏃犳簮鎬х悊璁虹瓑銆
榪戝嶮鍑犲勾鏉ワ紝鍩轟簬鍙嶉堢嚎鎬у寲鎬濇兂鐨勯潪綰挎ф帶鍒剁悊璁鴻幏寰楀緢澶ц繘灞曪紝閫氳繃鍧愭爣鍙樻崲涓庣姸鎬佸弽棣堬紝鍙浠ユ妸闈炵嚎鎬х郴緇熷寲涓虹嚎鎬х郴緇熴傜洿鎺ュ弽棣堢嚎鎬у寲錛圖FL錛夋槸鍩轟簬緋葷粺杈撳叆-杈撳嚭鎻忚堪鐨勪竴縐嶅弽棣堢嚎鎬у寲鏂規硶錛屽凡鎴愬姛瑙e喅浜嗗氱嶉潪綰挎ф帶鍒墮棶棰樸傜洿鎺ュ弽棣堢嚎鎬у寲鐨勪紭鐐規槸鎵鐢ㄦ暟瀛﹀伐鍏風畝鍗曪紝鐗╃悊姒傚康娓呮櫚錛屼究浜庢帉鎻°備絾瀹冨瓨鍦ㄧ潃涓涓鏄庢樉鐨勪笉瓚籌紝褰撶郴緇熷弬鏁板彂鐢熷彉鍖栨椂錛岀郴緇熺殑闈炵嚎鎬т笉鑳藉畬鍏ㄨ漿鎹涓虹嚎鎬э紝浠庤屽紩璧瘋宸銆1982騫達紝閭撹仛榫欐暀鎺堟彁鍑轟簡鐏拌壊鐞嗚篬1]錛屽畠鎴愬姛鐨勫簲鐢ㄥ湪璁稿氱敓浜ц繃紼嬩腑銆傞殢鐫鐏拌壊鐞嗚虹殑涓嶆柇瀹屽杽銆佸井澶勭悊鍣ㄧ殑涓嶆柇鍙戝睍錛岀伆鑹茬悊璁哄湪鎺у埗棰嗗煙鐨勫簲鐢ㄤ篃瓚婃潵瓚婂箍娉涖傛枃涓鎻愬嚭鐏拌壊涓嶇『瀹氶勬祴鍣ㄦ潵鍦ㄧ嚎棰勬祴姘哥佸悓姝ョ數鍔ㄦ満涓嶇『瀹氬洜緔狅紝騫剁浉搴旇皟鏁村弽棣堢嚎鎬у寲鎺у埗娉曞垯錛屼粠鑰屾彁楂樹簡緋葷粺鐨勬ц兘銆傝ユ柟娉曞厠鏈嶄簡鍙嶉堢嚎鎬у寲瀵規ā鍨嬬簿紜鍖栬佹眰鐨勪笉瓚沖強鎶戝埗涓嶇『瀹氬洜緔犲圭郴緇熺殑騫叉壈錛岃揪鍒頒簡棰勬湡鐨勬帶鍒舵晥鏋溿
1銆佹案紓佸悓姝ョ數鍔ㄦ満鍙嶉堢嚎鎬у寲鎺у埗
1.1姘哥佸悓姝ョ數鏈烘暟瀛︽ā鍨
閲囩敤琛ㄩ潰寮忕殑姘哥佸悓姝ョ數鍔ㄦ満錛屽叾鍩轟簬鍚屾ユ棆杞杞瀛愬潗鏍囩殑妯″瀷[2]濡備笅錛
鍏朵腑錛
鍏朵腑錛屾槸杞村畾瀛愮數鍘嬶紱鏄杞村畾瀛愮數嫻侊紱R鏄瀹氬瓙鐢甸樆錛汱鏄瀹氬瓙鐢墊劅錛汿L鏄璐熻澆杞鐭╋紱J鏄杞鍔ㄦ儻閲忥紱B鏄綺樻粸紓ㄦ摝緋繪暟錛汸鏄鏋佸規暟錛浵夋槸杞瀛愭満姊拌掗熷害錛浳f鏄姘哥佺侀氥
1.2鍙嶉堢嚎鎬у寲鎺у埗
涓轟簡瀹炵幇緋葷粺鐨勮В鑰︼紝閬垮厤鍑虹幇闆跺姩鎬佺郴緇熼棶棰榌3]錛岄夋嫨蠅,id涓虹郴緇熺殑杈撳嚭錛屽畾涔夋柊鐨勭郴緇熻緭鍑哄彉閲忎負錛
瀵瑰紡錛2錛夎繘琛屾眰瀵,寰楋細
褰撴椂錛岀嚎鎬ф帶鍒舵硶鍒欎負:
鍏朵腑,鏄鏂扮殑綰挎х郴緇熺殑杈撳叆鐭㈤噺錛屽畠鍙浠ユ寜鐓х嚎鎬х郴緇熸瀬鐐歸厤緗鐞嗚烘潵璁捐$姸鎬佸弽棣堟帶鍒朵負錛
鍙嶉堢嚎鎬у寲鎺у埗閫氳繃瀵硅緭鍑哄彉閲忚繘琛屾潕寰鍒嗭紝寰楀埌鎵闇鐨勭殑鍧愭爣鍙樻崲鍜岄潪綰挎х郴緇熺姸鎬佸弽棣堬紝瀹炵幇浜嗘案紓佸悓姝ョ數鏈洪潪綰挎х郴緇熺殑瑙h︼紝閫氳繃綰挎х悊璁烘潵璁捐℃帶鍒跺櫒錛岃捐″弬鏁扮畝鍗曪紝鍏鋒湁涓瀹氱殑閫熷害璺熻釜鎬ц兘銆傚悓鏃訛紝浠庝笂闈㈡帹瀵肩湅鍑猴紝鍙嶉堢嚎鎬у寲鏄涓縐嶅熀浜庣簿紜鏁板︽ā鍨嬬殑鍙嶉堢嚎鎬у寲錛屽綋緋葷粺鍙傛暟鍙戠敓鍙樺寲鎴栬礋杞戒笉紜瀹氭椂錛岀郴緇熺殑闈炵嚎鎬у洜緔犱笉鑳藉畬鍏ㄥ彇娑堬紝鍙鑳戒細鍥犳ゅ紩璧瘋宸銆傛枃鐚甗8]閽堝硅礋杞界殑涓嶇『瀹氭э紝鎻愬嚭璐熻澆瑙傛祴鍣ㄦ潵緇撳悎鍙嶉堢嚎鎬у寲鎺у埗鏉ヨˉ鍋胯礋杞藉彉鍖栧圭郴緇熺殑褰卞搷銆備笅涓鑺傜粨鍚堢伆鑹查勬祴鏉ュ湪綰塊勬祴姘哥佸悓姝ョ數鏈虹殑瀹氬瓙鐢甸樆銆佺矘婊炵(鎿︾郴鏁般佽礋杞藉彉鍖栫瓑涓嶇『瀹氬洜緔狅紝璋冩暣鍙嶉堢嚎鎬у寲鎺у埗娉曞垯錛屾彁楂樼郴緇熸帶鍒剁殑綺懼害銆
2銆佺伆鑹查勬祴妯″瀷
2.1GM寤烘ā鏂規硶
鐏拌壊妯″瀷寤烘ā鐞嗚猴紝瀹冧笉鍚屼簬甯歌勭殑寤烘ā鏂規硶錛屽畠涓嶆槸閫氳繃闅忔満榪囩▼浜х敓鐨勬暟鎹搴忓垪鎸夌粺璁¤勫緥鎴栧厛楠岃勫緥鏉ュ勭悊錛岃屾槸灝嗗叾瑙嗕綔鍦ㄤ竴瀹氬箙鍊艱寖鍥淬佷竴瀹氭椂鍖哄彉鍖栫殑鐏拌壊閲忋傞氳繃瀵瑰師濮嬫暟鎹鐨勬暣鐞嗭紙鍙堢О鏁扮殑鐢熸垚錛夋潵瀵繪壘鏁扮殑瑙勫緥銆傚洜姝ょ伆鑹叉ā鍨嬶紙GM錛夊疄闄呬笂鏄閽堝圭敓鎴愭暟鍒楃殑寤烘ā銆侴M寤烘ā鐨勬ラ
錛岄噰鐢ㄤ竴闃躲佸崟鍙橀噺鐨凣M錛1錛1錛夋ā鍨嬩綔涓洪勬祴妯″瀷錛屽叾鐧藉寲鏂圭▼涓猴細
鍏朵腑a涓烘ā鍨嬬殑鍙戝睍緋繪暟錛屼負鐏拌壊杈撳叆錛屼負杈╄瘑鍙傛暟銆傚叾鍩烘湰鎬濊礬錛氶栧厛瀵歸噰闆嗙殑鍘熷嬫暟鍒楄繘琛岀瘡鍔狅紙AGO錛夛紝寰楀埌涓鏈夎勫緥瀛樻寚鏁伴掑炵殑鐢熸垚鏁板垪錛屽埄鐢ㄧ敓鎴愮殑鏁板垪錛屼嬌鐢ㄦ渶灝忎簩涔樻硶鏉ヨ京璇嗗弬鏁癮,u錛屽苟鍙浠ュ緱鍒扮敓鎴愭暟鍒楃殑棰勬祴鍊礆紝榪欐牱錛屽氨鍙浠ヨ繘琛岄嗙瘡鍔狅紙IAGO錛夛紝寰楀埌鍘熷嬫暟鍒楃殑棰勬祴鍊箋傚叾棰勬祴綆楁硶涓猴細
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2.2絳夌淮鏂版伅婊氬姩妯″瀷
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3.1PMSM鐏拌壊棰勬祴鍙嶉堢嚎鎬у寲綆楁硶
鑰冭檻緋葷粺鐨勪笉紜瀹氬洜緔狅紝閲嶅啓鏂圭▼錛1錛
榪欐槸鍙︿竴綃
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鍏抽敭璇嶏細鐏拌壊鐞嗚 棰勬祴 鍙嶉堢嚎鎬у寲 姘哥佸悓姝ョ數鍔ㄦ満
Nonlinear Speed Control of PMSM based on Grey Prediction
LIU Dong-liang1,2錛孼hao Guang-zhou1錛孻an Wei-can2錛1.College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China2 .Wolong holding group co.,LTD,Shangyu 312300,China錛
Abstract: A direct feedback linearization control with regard to PMSM nonlinear dynamic mathematical model is introced in the paper. And a closed loop input-output system is builted. A controller is designed according to linearization model. The design methods mentioned above are simple and applicable. But they requir the model must be accurate, so that the grey uncertainty predictor is bringed forward. It can adjust the lumped uncertainty existed in PMSM into a feedback linearization control law on line and improve the system鈥檚 dynamic performance. The simulated result indicates the control scheme has the advantage of good tracking performance and robustness to uncertainty.
Key words: Grey Theroy錛 Prediction錛孎eedback Linearization錛孭MSM
姘哥佸悓姝ョ數鍔ㄦ満錛圥MSM錛変互鍏朵紭鑹鐨勬ц兘鍦ㄤ己鏈嶆帶鍒剁郴緇熻幏寰椾簡騫挎硾鐨勫簲鐢ㄣ傚湪姘哥佸悓姝ョ數鍔ㄦ満鐨勬帶鍒朵腑錛岀敱浜庤漿瀛愯漿閫熷拰瀹氬瓙鐢墊祦鐨勯潪綰挎ц﹀悎浣垮緱緋葷粺鍏鋒湁寰堝己鐨勯潪綰挎э紝鐗瑰埆鍦ㄧ郴緇熷瓨鍦ㄤ笉紜瀹氭ф椂錛岃繖縐嶉潪綰挎т嬌寰楃郴緇熼毦浜庤揪鍒伴珮綺懼害浼烘湇銆傚湪姘哥佸悓姝ョ數鍔ㄦ満榪愯岃繃紼嬩腑錛岀數鏈虹殑瀹氬瓙鐢甸樆銆佺矘婊炴懇鎿︾郴鏁板拰璐熻澆杞鐭╅兘鍙鑳藉彂鐢熷緢澶х殑鍙樺寲錛岃繖浜涘弬鏁扮殑鍙樺寲蹇呯劧褰卞搷鍒扮郴緇熺殑浼烘湇綺懼害銆備負浜嗚В鍐蟲案紓佸悓姝ョ數鍔ㄦ満綺劇『浼烘湇鎺у埗闂棰橈紝褰撳墠閲囩敤鐨勯潪綰挎ф帶鍒舵柟娉曚富瑕佹湁鍙樼粨鏋勬帶鍒躲佸井鍒嗗嚑浣曞拰鏃犳簮鎬х悊璁虹瓑銆
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璁劇郴緇焗鏃跺埢鐨勯噰鏍峰間負錛屽苟涓庢ゅ墠鐨刴-1涓閲囨牱鏁版嵁褰㈡垚搴忓垪,鐢辨m涓鏁版嵁緇忕敱鐏拌壊棰勬祴妯″瀷寰楀埌瓚呭墠涓姝ラ勬祴寮忥細
k1姝ラ勬祴涓猴細
鍒:
涓婂紡鍗充負絳夌淮鏂版伅婊氬姩棰勬祴綆楁硶錛屽紡涓環涓洪噰鏍鋒椂鍒伙紝m涓哄緩妯$淮鏁幫紝a,u涓篽鏃跺埢杈ㄨ瘑鎵寰楃殑鍙傛暟錛宬1涓洪勬祴姝ユ暟銆備竴鑸鏉ヨ達紝寤烘ā緇存暟閫夊彇m=5銆
3銆佺伆鑹查勬祴鍙嶉堢嚎鎬у寲鎺у埗
3.1 PMSM鐏拌壊棰勬祴鍙嶉堢嚎鎬у寲綆楁硶
鑰冭檻緋葷粺鐨勪笉紜瀹氬洜緔狅紝閲嶅啓鏂圭▼錛1錛
鍏朵腑錛
寮忎腑鏄姝e父鏉′歡鏃剁殑鍙傛暟錛屽畾涔変笉紜瀹氬洜緔狅細
鍚屾牱錛岄夋嫨蠅錛宨d涓虹郴緇熺殑杈撳嚭錛屽垯
鐢辯洿鎺ュ弽棣堢嚎鎬у寲鎺у埗娉曞垯寰楀疄闄呮帶鍒墮噺錛
寮忎腑,瀹冩槸涓嶇『瀹氬洜緔犲潡錛岀敱寮忥紙12錛夈侊紙13錛夊彲浠ョ湅鍑哄傛灉鑳介勬祴涓嶇『瀹氬洜緔犲潡鐨勫礆紝瀹炴椂璋冩暣鍙嶉堢嚎鎬у寲鎺у埗娉曞垯錛屽氨鑳戒嬌闈炵嚎鎬х郴緇熷畬鍏ㄨ漿鎹涓虹嚎鎬х郴緇燂紝瀹炵幇緋葷粺鐨勮В鑰︺
鐢卞紡錛10錛夈侊紙11錛夎繘琛岀繪暎鍖栵紝寰楋細
寮忥紙14銆15錛変腑棰勬祴搴忓垪鍙鐢辯伆鑹茬瓑緇存柊鎮婊氬姩妯″瀷錛9錛夊緱鍒幫細
鍏朵腑錛歛,u涓簁鏃跺埢杞閫熻鯨璇嗘墍寰楃殑鍙傛暟錛沘a,uu涓簁鏃跺埢鐢墊祦杈ㄨ瘑鎵寰楃殑鍙傛暟錛
3.2 緋葷粺浠跨湡緇撴灉
姘哥佸悓姝ョ數鍔ㄦ満鐏拌壊棰勬祴鍙嶉堢嚎鎬у寲鎺у埗妗嗗浘錛屽傚浘1鎵紺恆傞氳繃璋冩暣鍙傛暟k1,k2,k3浣跨郴緇熻揪鍒版弧鎰忕殑閰嶇疆鐐廣傛案紓佸悓姝ョ數鏈哄弬鏁頒負瀹氬瓙鐢甸樆R=0.56惟錛屽畾瀛愮數鎰烲=0.0153H錛屾案紓佺侀毼f=0.82Wb錛屾瀬瀵規暟P=3銆
鍥1 緋葷粺鎺у埗妗嗗浘
鍦ㄤ豢鐪熸椂鐢ㄧ洿鎺ュ弽棣堢嚎鎬у寲錛堝嵆W=0錛夋潵浣滀負瀵規瘮銆
錛1錛夊湪t=5s鏃訛紝璐熻澆騫叉壈錛
錛涘傚浘2鎵紺猴紝鍥劇殑涓婃柟琛ㄧず閫熷害璺熻釜緇欏畾鏂規嘗杞閫焠錛屽浘鐨勪笅鏂硅〃紺洪熷害璺熻釜璇宸瓻銆
鍥2 璐熻澆鍙樺寲鐨勫弽棣堢嚎鎬у寲璺熻釜鍝嶅簲鍙婅宸鏇茬嚎
錛2錛夊湪t=5s鏃訛紝鍙傛暟鍙樺寲錛
錛涘傚浘3鎵紺恆
鍥3 鐢墊満鍙傛暟鍙樺寲鐨勫弽棣堢嚎鎬у寲璺熻釜鍝嶅簲鍙婅宸鏇茬嚎
浠庡浘2錛屽浘3鍙浠ョ湅鍑猴紝褰撶郴緇熷瓨鍦ㄨ礋杞藉彉鍖栨垨鐢墊満鍙傛暟鍙樺寲絳変笉紜瀹氬洜緔犲獎鍝嶆椂錛岀郴緇熺殑璺熻釜鎬ц兘鍙樺樊銆傜幇鍦錛屽湪浠ヤ笂鐩稿悓鏉′歡涓嬬敤鐏拌壊鍙嶉堢嚎鎬у寲鎺у埗鏂規硶鏉ユ帶鍒惰漿閫熴備豢鐪熺粨鏋滃傚浘4錛5鎵紺恆傚叾涓鍥4涓鴻礋杞藉彉鍖栨椂鍒╃敤鐏拌壊鍙嶉堢嚎鎬у寲鎺у埗鏂規硶瀹炵幇鐨勯熷害鍝嶅簲鍜岃窡韙璇宸鏇茬嚎錛屼粠鍥句腑錛屽彲浠ョ湅鍑哄湪t=5s錛岀數鏈洪熷害鏈夎交寰鐨勬嘗鍔錛屼絾寰堝揩鐢墊満鍙堣兘璺熻釜閫熷害緇欏畾銆傚浘錛5錛変負鐢墊満鍙傛暟鍙樺寲鏃跺埄鐢ㄧ伆鑹插弽棣堢嚎鎬у寲鎺у埗鏂規硶瀹炵幇鐨勯熷害鍝嶅簲鍜岃窡韙璇宸鏇茬嚎錛屼粠鍥句腑鍚屾牱鍙浠ョ湅鍑哄湪浣跨敤鐏拌壊鍙嶉堢嚎鎬у寲鏂規硶鍑忓皬浜嗚窡韙璇宸銆傚洜姝ょ伆鑹插弽棣堢嚎鎬у寲鎺у埗鏂規硶鍏鋒湁瀵圭郴緇熷弬鏁幫紝璐熻澆絳変笉紜瀹氬洜緔犵殑椴佹掓ц兘銆
鍥4 璐熻澆鍙樺寲鐨勭伆鑹插弽棣堢嚎鎬у寲璺熻釜鍝嶅簲鍙婅宸鏇茬嚎
鍥5 鐢墊満鍙傛暟鍙樺寲鐨勭伆鑹插弽棣堢嚎鎬у寲璺熻釜鍝嶅簲鍙婅宸鏇茬嚎
4銆佺粨璁
鏈鏂囨彁鍑虹殑鐏拌壊棰勬祴鍙嶉堢嚎鎬у寲鎺у埗綆楁硶錛屽畠鍏鋒湁涓瀹氱殑椴佹掓у強蹇閫熺殑璺熻釜鑳藉姏錛屽苟鍑忓皯浜嗙畻娉曠殑澶嶆潅鎬с傚彟澶栵紝鐏拌壊鐞嗚鴻繕鑳戒笌妯$硦鎺у埗銆佺炵粡緗戠粶鎺у埗絳夌畻娉曠浉緇撳悎錛屾敼鍠勭郴緇熸ц兘錛屾彁楂樻帶鍒剁簿搴︺