| 1. | The linear model of the proposed system is derived to analyze the system stability and to select the controller gains
本论文还推导出控制系统的线性化模型用来对系统进行稳定性分析和控制器参数的选择。 |
| 2. | 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
所设计的控制器对于所允许的不确定性和控制器增益的可能变化，能保证闭环系是统渐近稳定的且性能指标上界不超过某个常数。 |
| 3. | 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稳定性理论,将含有markov跳跃参数的线性不确定时滞系统的鲁棒控制结果推广到含有markov跳跃参数的中立型系统中。 |
| 4. | 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
针对具有非线性和不确定性的机械加工切削过程，提出了神经元增益自整定的pid控制方法和模糊神经元非模型控制方法，前者采用神经元来在线调整pid控制器的增益，后者将模糊控制器和神经元控制器相结合构成模糊神经元控制器，这样当对象特性随切削深度的变化而变化时，所设计的控制器能保持切削力恒定，使系统稳定并具有满意的动态品质。 |
| 5. | 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
针对具有严重非线性特性的ph中和过程，提出了一种模糊增益自整定神经元控制方法，这种方法采用t - s模糊推理估计下一时刻的控制量，并通过参数估计和经验公式来计算出神经元控制器的增益。 |
| 6. | 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
针对具有严重非线性和不确定性的ph中和过程，给出一种神经网络模型，提出了一种神经非模型控制方法，该方法利用模糊算法在线调整神经网络控制器的增益，仿真实验表明这种基于神经网络的非模型控制方法能有效控制ph过程，具有优良的控制品质和强鲁棒性。 |
| 7. | 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
针对具有非线性特性的球形容器液位受控对象，从增益自调整和非线性补偿两个角度，分别提出了两种非模型控制方法，前者采用t - s模糊模型对神经元控制器的增益进行在线整定，后者使用本文建立的非线性神经元网络对球形容器进行非模型控制。 |
| 8. | 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
由于船舶非线性响应模型中含有未知常参数的不确定项，因此采用自适应backstepping的方法，选择参数自适应调节律，设计动态的船舶航向控制器，实现在线控制。 |
| 9. | 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
在得到在线计算结果以后，燃烧专家控制器综合在线检测系统提供的工艺参数，以及燃烧计算的结果，使用存放在知识库中的诊断和操作对策，得到相应的推理输出结果；利用规则集的相应规则，提示操作工选用合适的控制方法。 |
