Presents the analyses of the stability and robustness of multivariable continuous-time nonlinear systems subject to parameter uncertainties and with digital fuzzy controllers. To proceed with the analysis, first, an uncertain multivariable nonlinear plant is represented by a fuzzy plant model with parameter uncertainties. Second, a digital fuzzy controller is designed to close the feedback loop. Third, the stability criteria, the robust area and the largest sampling period are derived in terms of the matrix measures of the system parameters and the norms of the parameter uncertainties. An application example on stabilizing an uncertain nonlinear mass-spring-damper system is given to show the stabilizability and robustness properties of the proposed digital fuzzy controller.
This paper presents the stability analysis and design of fuzzy control systems. An improved and simple stability condition will be derived based on the Lyapunov's stability theory. The derived stability condition involves a smaller number of Lyapunov's conditions than that given by Wang et al. (1996). A design of the membership functions of the fuzzy controller will be given. In our approach, the number of rules of the fuzzy controller can be different from that of the fuzzy plant model. Our approach can be applied to those fuzzy controllers with both positive and negative grades of membership, An application example on stabilizing a mass-spring-damper system will be given to show the stabilizability of the fuzzy controller.
This paper presents the design of a fuzzy controller for PWM (pulse width modulation) DC-DC switching converter based on the Takagi-Sugeno (TS) fuzzy modelling approach. Stability and robustness conditions are derived for the fuzzy control system to help the design of the fuzzy controlled DC-DC converter. Simulation and experimental results on regulating a boost DC-DC converter subject to large load changes by using the proposed fuzzy controller are given. The transient response is compared to that controlled by a traditional PI controller.
A fuzzy controller, which is a fuzzy combination of linear state-feedback and switching controllers, is proposed for nonlinear systems subject to parameter uncertainties. By proper design of the proposed fuzzy controller, the chattering effect near the origin can be eliminated. The global system stability is also guaranteed.
This paper presents the interpretation of digits and commands using a modified neural network and the genetic algorithm. The modified neural network exhibits a node-to-node relationship which enhances its learning and generalization abilities. A digit-and-command interpreter constructed by the modified neural networks is proposed to recognize handwritten digits and commands. A genetic algorithm is employed to train the parameters of the modified neural networks of the digit-and-command interpreter. The proposed digit-and-command interpreter is successfully realized in an electronic book. Simulation and experimental results will be presented to show the applicability and merits of the proposed approach.
Fisher Linear Discriminant Analysis (LDA) is a widely-used projection technique. Its application includes face recognition and speaker recognition. The kernel version of LDA (KDA) has also been developed, which generalizes LDA by introducing a kernel. LDA and KDA consists of a within-class scatter matrix and a between-class scatter matrix. The original formulations of LDA and KDA involve the inversion of the within-class scatter matrix, which may have singularity problem. A simple way to prevent singularity is adding a regularization term to the within-class scatter matrix. The resulting LDA and KDA are called Regularized LDA (RLDA) and Regularized KDA (RKDA). In this paper, we experimentally investigate how this regularization term will influence the performance of LDA and KDA. In addition, we introduce an extra regularization term to the between-class scatter matrix, and the resulting LDA and KDA are then called Doubly Regularized LDA (D-RLDA) and Doubly Regularized KDA (D-RKDA). We then apply LDA, KDA, RLDA, RKDA, D-RLDA and D-RKDA as a feature projection technique to two audio signal classification tasks. Gaussian Supervector (GSV) is used as the feature vector and linear Support Vector Machine (SVM) is used as the classifier. Experimental results show that, RLDA, D-RLDA, RKDA and D- RKDA are more effective than the conventional LDA and KDA. Besides, D-RLDA and D-RKDA are more robust than RLDA and RKDA.
This paper presents the stability analysis of fuzzy model based nonlinear control systems, and the design of nonlinear gains and feedback gains of the nonlinear controller using genetic algorithm with arithmetic crossover and nonuniform mutation. A stability condition is derived based on the Lyapunov stability theory with a smaller number of Lyapunov conditions. The solution of the stability conditions are also determined using GA. An application example of stabilizing a cart-pole type inverted pendulum system is given to show the stabilizability of the nonlinear controller.
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