補償控制棒 的英文怎麼說

中文拼音 [bǔchángkòngzhìbàng]
補償控制棒 英文
shim control rod
  • 補 : Ⅰ動詞1 （修理; 修補） repair; mend; patch 2 （補充; 補足;填補） fill up; add supplement; supply; ...
  • 償 : 動詞1. （歸還; 抵補） repay; compensate for 2. （滿足） meet; fulfil
  • 控 : 動詞1 （告發;控告） accuse; charge 2 （控制） control; dominate 3 （使容器口兒朝下 讓裏面的液體慢...
  • 制 : Ⅰ動詞1 （製造） make; manufacture 2 （擬訂; 規定） draw up; establish 3 （用強力約束; 限定; 管束...
  • 棒 : Ⅰ名詞（棍子） stick; staff; club; cudgel Ⅱ形容詞[口語] （強; 高; 好） strong; excellent; good
  • 補償 : compensate; make up; make up for; make it up to sb. ; make sth. good; compensation; offset
  • 控制 : control; dominate; regulate; govern; manage; check; cybernate; manipulate; encraty; rule; rein; c...
  1. Robust nonlinear friction compensation of mechanical servo system with time variable parameters

    參數不確定機械伺服系統的魯棒非線性摩擦補償控制
  2. After that, a fuzzy neural network control design with mixed performance was proposed aiming at different requirement to uncertainty, which separately compensate parameter uncertainties and external disturbances of robot system. this method can ensure the robust stability that under a prescribed attenuation level for the external disturbance, and also strengthens compensation to parameter uncertainties. the theory of hybrid force / position control is clear, however, it is difficult to implement

    針對實際應用中對不確定的不同要求，又提出了一種具有混合/性能指標的神經網路控制方法，對機器人不確定性（包括參數不確定性和外部擾動）分別進行補償，保證了系統對外界干擾在給定的干擾衰減度下具有魯棒穩定性的同時，還增強了系統對參數不確定性的補償。
  3. The feedback control is introduced to compensate effects of both errors of identification and iterative learning, so the control system can get better robustness and control precision

    在此引入反饋控制是為了補償辨識和迭代學習的誤差，以增強被控系統的魯棒性，提高控制精度。
  4. Firstly, based on backstepping and the supervisory control strategy, a robust adaptive fuzzy controller is designed for a class of nonlinear systems. the first type fuzzy logic system is used to approximate the unknown part of the process. the adaptive compensation term of the optimal approximation error is adopted

    本文首先針對一類不確定非線性系統，基於backstepping方法，利用監督控制，引入最優逼近誤差的自適應補償項，並利用型模糊邏輯系統逼近系統的未知部分，提出了一種魯棒自適應模糊控制器設計方案，運用李亞普諾夫第二方法，先證明了閉環模糊控制系統全狀態有界，再證明了跟蹤誤差收斂到零。
  5. Abstract : a new variable structure model reference adaptive control ( vs - mrac ) scheme is proposed in this paper. by transforming a relative degree one system into a first - order - like differential equation, it is shown that the spr ( strictly positive real ) assumption for reference model or its modified form ( if plant relative degree n * > 1 ) can be removed and the tracking error can converge to zero in finite time or a small residual set

    文摘:提出了一種可具有非嚴格正實參考模型的變結構魯棒自適應控制方案，可去除變結構模型參考自適應控制中普遍要求參考模型為嚴格正實或當相對階大於1時參考模型經補償后為嚴格正實的假設.在此基礎上提出的變結構控制律可保證跟蹤誤差在相對階為1時于有限時間內收斂到零，而當相對階大於1時則可使跟蹤誤差收斂到一個殘差集內
  6. Then an impedance control strategy with robust performance is presented aiming at uncertainties of robot, fnn is used to learning the uncertainties in order to eliminate disturbance, which have good robust and high value in practice. finally, an adaptive method is presented

    為此，本文又在機器人阻抗控制的基礎上，針對機器人和環境的不確定性，提出一種具有魯棒性的阻抗控制結構，使用模糊神經網路作為補償控制器消除力控制中的所有不確定性，具有較強的魯棒性和較好的實用價值。
  7. This dissertation presented two new methods of robust adaptive track control for a class of mimo strong nonlinear system with external disturbance. one method makes use of taylor approximation principle to linearize the mimo strong nonlinear system at the ideal equilibrium point, meanwhile external disturbance is considered, and then designs two on - line neural network controller respectively, which can dynamically compensate the high order items of taylor series and the control signals at ideal equilibrium point under the drive of state error between linear and nonlinear system. a linear feedback controller obtained by pole assignment and two on - line neural network act on the practical mimo high nonlinear system together, guaranteeing the whole system robust stable and tracking the specified signal ; the other method designs three on - line neural networks for this class of system

    本文對於一類含有外部擾動的多輸入多輸出（ mimo ）強非線性系統，提出了兩種新的魯棒自適應跟蹤控制方法，第一種利用了taylor近似的原理，在考慮了外部擾動的情況下，將mimo強非線性系統在理想平衡點處線性化，分別設計了兩個在線神經網路控制器，在線性和非線性系統之間的狀態誤差驅動下動態補償系統的taylor近似高階項及理想平衡點處的控制信號，滿足極點配置方法的線性反饋控制器和兩個在線神經網路聯合作用於實際的被控mimo強非線性系統，在保證整個系統魯棒穩定性的情況下，能夠跟蹤給定的指令信號；另一種方法是針對這類系統設計了3個在線神經網路，分別實時抵消這類非線性系統中的非線性部分、與控制量耦合的非線性項以及外部擾動，使得受控系統的輸出可以完全跟蹤給定輸入參考信號。
  8. Problems, which are the robust stabilization, the normal h control, the minimum sensitivity and mix sensitivity control, are discussed for the dead - time compensated upldt

    討論了時滯補償后系統的魯棒穩定化、標準h _控制、最小靈敏度和混合靈敏度控制等問題。
  9. A new robust adaptive scheme which are used for tracking of this robot with parametric and bounded external uncertainties is proposed in this thesis. the controller is consist of a controller which is proposed by slotine1 ' 1 and nonlinear continued feedback compensation part. by estimating the unknown physical parameters of robot on - line, it can eliminate the effects result by parameters and external disturbances and guarantee gas and uniform boundedness of parametric estimation. the only information required in setting up the strategy is the output states of jionts, while the inversion of the inertia matrix or estimation the bound of the inertia matrix and measure the jionts accelerations are not needed. it is shown by simulations that the proposed control scheme has quicker convergence velocity and better control precision than paper [ 1 ] and control schemes at present

    針對該模型具有參數及有界外部擾動不確定性時提出了一種新的魯棒自適應控制策略，控制器由基於slotine的控制器和非線性連續反饋補償控制器構成。通過在線估計機器人的未知物理參數，有效的消除了由參數及外部擾動所引起的不確定性影響，保證系統達到漸近穩定和參數估計一致有界。與現存的許多控制方法相比，該控制策略不需求解慣性矩陣的逆或估計慣性矩陣的界，不需測量關節加速度，而唯一需要了解的只是系統輸出的位置及速度狀態。
  10. ( 2 ). similar to mark w. spong [ 2 ] " s control structure, we propose a new control strategy for above model with only parametric uncertainty. the proposed scheme consists of two parts. the first part is the nonlinear compensating standard controller

    （ 2 ） .採用類似於markw . spon夢]的控制結構，針對該模型只具有參數不確定性時進行魯棒性設計，控制器由基於標稱模型設計的非線性補償控制器lz ]和基於勿opunov理論設計的魯棒補償控制器。
  11. A design method of robust tracking control for rigid - link robots with uncertainties was proposed, its controller was composed by the computed - torque controller based on nominal model and a robust compensating controller based on uncertain parameters. the availability of this control method was shown by theory and simulation

    針對不確定剛體連桿機器人的跟蹤控制問題，提出一種軌跡跟蹤魯棒控制方案，其控制器由基於標稱模型設計的計算力矩控制器和基於不確定性因素設計的魯棒跟蹤補償控制器組成，使系統達到理想的控制性能。
  12. According to the demand of the technology of gas nitriding, the author firstly put forward total demand analysis and systematic model - making for gas - nitriding furnace ' s temperature control system with uml in order to definite the temperature control system ' s functional requirement and the interaction of each object in this system on the basis of this idea. it ' s thought that the key step for realizing the temperature control system is the analysis and the design of the pid controller. considering that the gas - nitriding furnace has significant temperature lag, the author initially pointed out the opinion of realizing the system of temperature control by pid controller with pure lag compensation and then designed the parameter of pid controller with pure lag compensation and ordinary pid controller by matlab6. 5

    這篇論文以作者本人在原工作單位的技術改造實踐中（即滲氮爐的技術改造）所遺留下來的技術問題為背景，根據氣體滲氮工藝的具體需要，首先提出了用uml對滲氮爐溫控系統進行總體的需求分析和系統實物建模以明確溫控系統的功能需求及各對象間的交互，在此基礎上分析得出溫控系統實現的關鍵所在即pid控制器的分析設計和實現，由氣體滲氮爐存在較大的溫度滯后這一特性，初步提出用帶純滯后補償的pid控制器來實現溫控系統的溫度控制，隨后藉助于matlab6 . 5平臺對帶純滯后補償的pid控制器和常規的pid控制器進行參數的設計與整定，進而在matlab6 . 5的simulink模擬環境中對基於兩種不同pid控制器的滲氮爐的兩種溫控系統的設計方案進行了模擬實現及模擬結果比較，結果表明，採用帶純滯后補償的pid控制系統的魯棒性較常規的pid控制系統好。
  13. First, n - degree of freedom robotic manipulator control problem is studied. on the basis of computed torque ( ct ), the uncertainty is learned by the neural network, and the output of the neural network is used as compensator. this approach overcome the deterioration of control performance due to the uncertainties including working circumstance and load change

    針對計算力矩法對模型誤差敏感，魯棒性差的特點，文中將神經網路和計算力矩方法結合，首先根據標稱模型設計計算力矩控制器，然後採用神經網路來學習系統中的不確定性，神經網路的輸出作為補償控制器：有效的克服了機械手由於工作環境和負載變化等不確定性引起的控製品質的破壞。
  14. The application of control law developed above to the problem of reconflgurable flight control is described. based on the simulation results, the effects of learning rate ( or adaptation gain ) and slide model terms on the control system are discussed. comparing with adaptive control, the control law is helpful to improve system ' s transition response and reduce undesirable oscillations, provides nearly perfect tracking in the case of undetected failure

    4 、探討了所設計的控制律在重構飛行控制中的應用，討論了自適應調整率對自適應控制性能的影響、滑模魯棒控制補償項對控制性能的影響；對幾種舵面損傷的模擬結果表明，本文的控制律較自適應控制具有響應改善速度快、響應振蕩小的優點，具有較好的重構功能。
  15. But direct pd control has better robustness against modeling error and uncertain disturbance compared with pd control with feedforward compensation

    與pd加前饋補償控制相比，直接pd控制對機器人手臂動態模型誤差及外界不確定性干擾具有更強的魯棒性。
  16. Simulations show that this method can quickly and accurately recognize the object in input image and has a few strong robustness against noise in the input image. secondly, the simulations for two kinds of control algorithms based on pd, namely, direct pd control and pd control with feedforward compensation, have been completed in this thesis. simulations show that both control algorithms have good control performance in tracking a known trajectory and the effect of dynamic compensation of pd control with feedforward compensation is obvious

    本文首先根據二值圖像的特點結合距離變換提出了一種基於ga模式匹配的復雜背景下目標的識別方法，該方法能快速準確的匹配圖像中的目標，而且對噪聲有較強的魯棒性；接著對基於pd的2種機器人控制演算法（直接pd控制和pd加前饋補償控制）進行了模擬研究，實驗結果表明這兩種pd控制演算法都具有較好的軌跡跟蹤性能，而且pd加前饋控制的動態補償效果也很明顯。
  17. The load of the dc motor has a great influence on the tracking effect of speed control system, so a kind of load disturbance observer is constructed to compensate the load disturbance, and a new variable structure controller is designed by employing lyapunov speed error function

    針對實際直流電機負載對速度控制系統跟蹤效果有較大影響的情況，提出了一種重構軋制負載擾動的觀測器，進而對負載擾動進行前饋補償控制，設計了一種新的利用lyapunov速度誤差函數構造的變結構魯棒控制器。
  18. The second part is a robust compensating controller based on lyapnuov theory which has simple structure and is used for eliminate the effect caused by parametric uncertainty. exact knowledge for robot dynamics is not needed except that the bound of parametric uncertainty must be known

    魯棒補償控制器用於消除參數誤差帶來的不確定性影響，其結構簡單，只需知道系統的一個參數不確定性上界，並保證在系統存在較大的參數誤差情況下，按照對控制器參數的不同選擇，達到3種不同的穩定性結果（即guub 、 gas和ges ） 。
  19. In the thesis, robust design methods for controlling strategy in the coiler control system are studied in detail

    詳細研究了該步進控制系統控制律的魯棒設計方法，並給出了一種魯棒補償設計方法。
  20. Further the backstepping technique is adopted to construct fuzzy robust reconfigurable control scheme for a class of uncertain nonlinear system, which is an integration of fuzzy - mode - based controller and fuzzy adaptive controller. fuzzy adaptive controller in this scheme is used to cancel the effect caused by the modeling error, disturbance, and unknown actuator failures

    進一步，文中針對一類具有不確定性的非線性系統，通過backstepping技術將基於t ? s模糊模型的模糊增益控制器與自適應模糊控制器相結合，提出了一種模糊魯棒重構控制方法，通過模糊自適應控制器的作用，可以自動補償于系統的建模誤差、不確定因素及執行機構故障等造成的影響。
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