A constrained generalized predictive control （GPC） algorithm based on the T-S fuzzy model is presented for the nonlinear system. First, a Takagi-Sugeno （T-S） fuzzy model based on the fuzzy cluster algorithm and th...A constrained generalized predictive control （GPC） algorithm based on the T-S fuzzy model is presented for the nonlinear system. First, a Takagi-Sugeno （T-S） fuzzy model based on the fuzzy cluster algorithm and the orthogonalleast square method is constructed to approach the nonlinear system. Since its consequence is linear, it can divide the nonlinear system into a number of linear or nearly linear subsystems. For this T-S fuzzy model, a GPC algorithm with input constraints is presented. This strategy takes into account all the constraints of the control signal and its increment, and does not require the calculation of the Diophantine equations. So it needs only a small computer memory and the computational speed is high. The simulation results show a good performance for the nonlinear systems.展开更多
Based on the T-S fuzzy model,this paper presents a new model of non-linear network control system with stochastic transfer delay.Sufficient criterion is proposed to guarantee globally asymptotically stability of this ...Based on the T-S fuzzy model,this paper presents a new model of non-linear network control system with stochastic transfer delay.Sufficient criterion is proposed to guarantee globally asymptotically stability of this two-levels T-S fuzzy model.Also a T-S fuzzy observer of NCS is designed base on this two-levels T-S fuzzy model.All these results present a new approach for networked control system analysis and design.展开更多
A design method is presented for Takagi-Sugeno （T-S） fuzzy model based nonlinear sliding model controller. First, the closed-loop fuzzy system is divided into a set of dominant local linear systems according to oper...A design method is presented for Takagi-Sugeno （T-S） fuzzy model based nonlinear sliding model controller. First, the closed-loop fuzzy system is divided into a set of dominant local linear systems according to operating sub-regions. In each sub-region the fuzzy system consists of nominal linear system and a group of interacting systems. Then the controller composed two parts is designed. One part is designed to control the nominal system, the other is designed to control the interacting systems with sliding mode theory. The proposed controller can improve the robusmess and gnarantee tracking performance of the fuzzy system. Stability is guaranteed without finding a common positive definite matrix.展开更多
The problem of global robust asymptotical stability for a class of Takagi-Sugeno fuzzy neural networks（TSFNN） with discontinuous activation functions and time delays is investigated by using Lyapunov stability theor...The problem of global robust asymptotical stability for a class of Takagi-Sugeno fuzzy neural networks（TSFNN） with discontinuous activation functions and time delays is investigated by using Lyapunov stability theory.Based on linear matrix inequalities（LMIs）,we originally propose robust fuzzy control to guarantee the global robust asymptotical stability of TSFNNs.Compared with the existing literature,this paper removes the assumptions on the neuron activations such as Lipschitz conditions,bounded,monotonic increasing property or the right-limit value is bigger than the left one at the discontinuous point.Thus,the results are more general and wider.Finally,two numerical examples are given to show the effectiveness of the proposed stability results.展开更多
针对目前ON-OFF控制策略在PLZT驱动器光致应变位移的闭环伺服控制系统中的缺点,提出了一种基于T-S模糊模型的PLZT驱动器应变位移的动态模型及预测控制方法。首先,建立了PLZT驱动器光致应变位移的T-S模糊模型,该模型利用基于减法聚类的模...针对目前ON-OFF控制策略在PLZT驱动器光致应变位移的闭环伺服控制系统中的缺点,提出了一种基于T-S模糊模型的PLZT驱动器应变位移的动态模型及预测控制方法。首先,建立了PLZT驱动器光致应变位移的T-S模糊模型,该模型利用基于减法聚类的模糊C均值聚类算法进行前件辨识,并利用奇异值分解(singular value decomposition, SVD)算法进行后件辨识,所建立模型的有效性通过拟合度仿真加以验证。随后,在所建立的T-S模糊模型的基础上结合预测控制方法对PLZT驱动器的光致应变位移进行闭环控制,并对该算法进行仿真验证。仿真结果显示,在PLZT驱动器微位移的控制中,该文控制算法减小了基于ON-OFF控制策略下的抖振,且具有更好的控制效果。展开更多
基金This Project was supported by the National Natural Science Foundation of China (60374037 and 60574036)the Opening Project Foundation of National Lab of Industrial Control Technology (0708008).
文摘A constrained generalized predictive control （GPC） algorithm based on the T-S fuzzy model is presented for the nonlinear system. First, a Takagi-Sugeno （T-S） fuzzy model based on the fuzzy cluster algorithm and the orthogonalleast square method is constructed to approach the nonlinear system. Since its consequence is linear, it can divide the nonlinear system into a number of linear or nearly linear subsystems. For this T-S fuzzy model, a GPC algorithm with input constraints is presented. This strategy takes into account all the constraints of the control signal and its increment, and does not require the calculation of the Diophantine equations. So it needs only a small computer memory and the computational speed is high. The simulation results show a good performance for the nonlinear systems.
基金National Natural Science Foundation of china(60274014,60574088)
文摘Based on the T-S fuzzy model,this paper presents a new model of non-linear network control system with stochastic transfer delay.Sufficient criterion is proposed to guarantee globally asymptotically stability of this two-levels T-S fuzzy model.Also a T-S fuzzy observer of NCS is designed base on this two-levels T-S fuzzy model.All these results present a new approach for networked control system analysis and design.
文摘A design method is presented for Takagi-Sugeno （T-S） fuzzy model based nonlinear sliding model controller. First, the closed-loop fuzzy system is divided into a set of dominant local linear systems according to operating sub-regions. In each sub-region the fuzzy system consists of nominal linear system and a group of interacting systems. Then the controller composed two parts is designed. One part is designed to control the nominal system, the other is designed to control the interacting systems with sliding mode theory. The proposed controller can improve the robusmess and gnarantee tracking performance of the fuzzy system. Stability is guaranteed without finding a common positive definite matrix.
基金supported by the National Natural Science Foundation of China(6077504760835004)+2 种基金the National High Technology Research and Development Program of China(863 Program)(2007AA04Z244 2008AA04Z214)the Graduate Innovation Fundation of Hunan Province(CX2010B132)
文摘The problem of global robust asymptotical stability for a class of Takagi-Sugeno fuzzy neural networks（TSFNN） with discontinuous activation functions and time delays is investigated by using Lyapunov stability theory.Based on linear matrix inequalities（LMIs）,we originally propose robust fuzzy control to guarantee the global robust asymptotical stability of TSFNNs.Compared with the existing literature,this paper removes the assumptions on the neuron activations such as Lipschitz conditions,bounded,monotonic increasing property or the right-limit value is bigger than the left one at the discontinuous point.Thus,the results are more general and wider.Finally,two numerical examples are given to show the effectiveness of the proposed stability results.
文摘针对目前ON-OFF控制策略在PLZT驱动器光致应变位移的闭环伺服控制系统中的缺点,提出了一种基于T-S模糊模型的PLZT驱动器应变位移的动态模型及预测控制方法。首先,建立了PLZT驱动器光致应变位移的T-S模糊模型,该模型利用基于减法聚类的模糊C均值聚类算法进行前件辨识,并利用奇异值分解(singular value decomposition, SVD)算法进行后件辨识,所建立模型的有效性通过拟合度仿真加以验证。随后,在所建立的T-S模糊模型的基础上结合预测控制方法对PLZT驱动器的光致应变位移进行闭环控制,并对该算法进行仿真验证。仿真结果显示,在PLZT驱动器微位移的控制中,该文控制算法减小了基于ON-OFF控制策略下的抖振,且具有更好的控制效果。
文摘传统Takagi-Sugeno(T-S)模糊系统模型因模糊规则使用样本全部特征,导致模型的可解释性较差,冗余特征的存在还会导致模型的过拟合,降低模型的泛化性能。针对该问题,提出了一种模糊系统联合稀疏建模新方法L2-CFS-FIS(L2-common feature selection fuzzy inference systems),从而提高模型的泛化性能和可解释性。该方法充分考虑存在于模糊规则间的公共特征信息,同时引入模型过拟合处理机制,将模糊系统建模问题转化为一个基于双正则的联合优化问题,并使用交替方向乘子(alternating direction method of multipliers,ADMM)算法来进行求解。实验结果表明,该方法所构造的模糊系统不仅能够获得较为满意的泛化性能,而且通过有效地挖掘规则间重要的公共特征,可以确保模型具有较高的可解释性。
基金Supported by National Natural Science Foundation of China （61034005, 60974071）, Program for New Century Excellent Talents in University （NCET-08-0101）, and Fundamental Research Funds for the Central Universities （N100104102, Nl10604007）
