controller gain造句
例句與造句
- The linear model of the proposed system is derived to analyze the system stability and to select the controller gains
本論文還推導(dǎo)出控制系統(tǒng)的線性化模型用來對系統(tǒng)進行穩(wěn)定性分析和控制器參數(shù)的選擇。 - For all admissible uncertainties and possible controller gain variations , the desgined controller can guaranteed that the closed - loop system is asymptotically stable and the upper bound of cost function value is not more than a constant
所設(shè)計的控制器對于所允許的不確定性和控制器增益的可能變化，能保證閉環(huán)系是統(tǒng)漸近穩(wěn)定的且性能指標(biāo)上界不超過某個常數(shù)。 - By using lmi toolbox in matlab , it is easily to obtain controllers gain matrices . 2 ) based on lyapunov stability theory , the results on robust control for time - delay systems with markovian jumping parameters are extended to neutral
2 )基于lyapunov穩(wěn)定性理論,將含有markov跳躍參數(shù)的線性不確定時滯系統(tǒng)的魯棒控制結(jié)果推廣到含有markov跳躍參數(shù)的中立型系統(tǒng)中。 - The model - free pid control method with neuron tuning gain and the neuro - fuzzy control method for a constant cutting force metal turning process system are proposed . the former method keeps the cutting force to be constant by using the neuron to change the pid controller gain on - line . the latter method construct the fuzzy neuron controller by combing the fuzzy controller and the neuron controller
針對具有非線性和不確定性的機械加工切削過程，提出了神經(jīng)元增益自整定的pid控制方法和模糊神經(jīng)元非模型控制方法，前者采用神經(jīng)元來在線調(diào)整pid控制器的增益，后者將模糊控制器和神經(jīng)元控制器相結(jié)合構(gòu)成模糊神經(jīng)元控制器，這樣當(dāng)對象特性隨切削深度的變化而變化時，所設(shè)計的控制器能保持切削力恒定，使系統(tǒng)穩(wěn)定并具有滿意的動態(tài)品質(zhì)。 - The neuron control method with self - tuning gain is proposed for a ph neutralization process . in this control system , the fuzzy t - s model is used to predict the control signal . the neuron controller gain is calculated according to the parameter estimation and experience formulas
針對具有嚴(yán)重非線性特性的ph中和過程，提出了一種模糊增益自整定神經(jīng)元控制方法，這種方法采用t - s模糊推理估計下一時刻的控制量，并通過參數(shù)估計和經(jīng)驗公式來計算出神經(jīng)元控制器的增益。 - It's difficult to find controller gain in a sentence. 用controller gain造句挺難的
- Simulation results show that both of them have satisfactory performance and strong robustness . 2 . to ph processes , which are nonlinear and time varying , the neural network model is structured and the learning algorithm is presented , based on which the model - free controller is designed , while the controller gain is scheduled by a fuzzy method
針對具有嚴(yán)重非線性和不確定性的ph中和過程，給出一種神經(jīng)網(wǎng)絡(luò)模型，提出了一種神經(jīng)非模型控制方法，該方法利用模糊算法在線調(diào)整神經(jīng)網(wǎng)絡(luò)控制器的增益，仿真實驗表明這種基于神經(jīng)網(wǎng)絡(luò)的非模型控制方法能有效控制ph過程，具有優(yōu)良的控制品質(zhì)和強魯棒性。 - To the level control problem of a spherical tank , two model - free control methods are proposed . in the former method , the takagi - sugeno fuzzy model is used to tune the neuron controller gain . in the latter method , the model - free control method using the neural network model proposed for nonlinear plants is presented
針對具有非線性特性的球形容器液位受控對象，從增益自調(diào)整和非線性補償兩個角度，分別提出了兩種非模型控制方法，前者采用t - s模糊模型對神經(jīng)元控制器的增益進行在線整定，后者使用本文建立的非線性神經(jīng)元網(wǎng)絡(luò)對球形容器進行非模型控制。 - On account of the uncertainty existing in the nonlinear ship responded model , we design a dynamic adaptive ship steering controller by using adaptive backstepping . after deducing the update law of the unknown constant , we choose the controller gains to guarantee the closed loop system and the control signal global boundedness
由于船舶非線性響應(yīng)模型中含有未知常參數(shù)的不確定項，因此采用自適應(yīng)backstepping的方法，選擇參數(shù)自適應(yīng)調(diào)節(jié)律，設(shè)計動態(tài)的船舶航向控制器，實現(xiàn)在線控制。 - After getting online results , combustion expert controller gained relevant ratiocinate output results by integrating technical parameters of online inspected system , combustion computed results and diagnostic and manipulated countermeasure of knowledge base , then which could propose operator for choosing a suitable control method using concentrated corresponding rules
在得到在線計算結(jié)果以后，燃燒專家控制器綜合在線檢測系統(tǒng)提供的工藝參數(shù)，以及燃燒計算的結(jié)果，使用存放在知識庫中的診斷和操作對策，得到相應(yīng)的推理輸出結(jié)果；利用規(guī)則集的相應(yīng)規(guī)則，提示操作工選用合適的控制方法。