Fault detection for T-S fuzzy time-delay systems: delta operator and input-output methods.

This paper focuses on the problem of fault detection for Takagi-Sugeno fuzzy systems with time-varying delays via delta operator approach. By designing a filter to generate a residual signal, the fault detection problem addressed in this paper can be converted into a filtering problem. The time-varying delay is approximated by the two-term approximation method. Fuzzy augmented fault detection system is constructed in δ -domain, and a threshold function is given. By applying the scaled small gain theorem and choosing a Lyapunov-Krasovskii functional in δ -domain, a sufficient condition of asymptotic stability with a prescribed H∞ disturbance attenuation level is derived for the proposed fault detection system. Then, a solvability condition for the designed fault detection filter is established, with which the desired filter can be obtained by solving a convex optimization problem. Finally, an example is given to demonstrate the feasibility and effectiveness of the proposed method.

Stabilization of nonlinear systems using sampled-data output-feedback fuzzy controller based on polynomial-fuzzy-model-based control approach.

This paper investigates the stability of sampled-data output-feedback (SDOF) polynomial-fuzzy-model-based control systems. Representing the nonlinear plant using a polynomial fuzzy model, an SDOF fuzzy controller is proposed to perform the control process using the system output information. As only the system output is available for feedback compensation, it is more challenging for the controller design and system analysis compared to the full-state-feedback case. Furthermore, because of the sampling activity, the control signal is kept constant by the zero-order hold during the sampling period, which complicates the system dynamics and makes the stability analysis more difficult. In this paper, two cases of SDOF fuzzy controllers, which either share the same number of fuzzy rules or not, are considered. The system stability is investigated based on the Lyapunov stability theory using the sum-of-squares (SOS) approach. SOS-based stability conditions are obtained to guarantee the system stability and synthesize the SDOF fuzzy controller. Simulation examples are given to demonstrate the merits of the proposed SDOF fuzzy control approach.

LMI-based stability analysis of fuzzy-model-based control systems using approximated polynomial membership functions.

Relaxed linear-matrix-inequality-based stability conditions for fuzzy-model-based control systems with imperfect premise matching are proposed. First, the derivative of the Lyapunov function, containing the product terms of the fuzzy model and fuzzy controller membership functions, is derived. Then, in the partitioned operating domain of the membership functions, the relations between the state variables and the mentioned product terms are represented by approximated polynomials in each subregion. Next, the stability conditions containing the information of all subsystems and the approximated polynomials are derived. In addition, the concept of the S-procedure is utilized to release the conservativeness caused by considering the whole operating region for approximated polynomials. It is shown that the well-known stability conditions can be special cases of the proposed stability conditions. Simulation examples are given to illustrate the validity of the proposed approach.

Stability analysis of interval type-2 fuzzy-model-based control systems.

This paper presents the stability analysis of interval type-2 fuzzy-model-based (FMB) control systems. To investigate the system stability, an interval type-2 Takagi-Sugeno (T-S) fuzzy model, which can be regarded as a collection of a number of type-1 T-S fuzzy models, is proposed to represent the nonlinear plant subject to parameter uncertainties. With the lower and upper membership functions, the parameter uncertainties can be effectively captured. Based on the interval type-2 T-S fuzzy model, an interval type-2 fuzzy controller is proposed to close the feedback loop. To facilitate the stability analysis, the information of the footprint of uncertainty is used to develop some membership function conditions, which allow the introduction of slack matrices to handle the parameter uncertainties in the stability analysis. Stability conditions in terms of linear matrix inequalities are derived using a Lyapunov-based approach. Simulation examples are given to illustrate the effectiveness of the proposed interval type-2 FMB control approach.

Hybrid particle swarm optimization with wavelet mutation and its industrial applications.

A new hybrid particle swarm optimization (PSO) that incorporates a wavelet-theory-based mutation operation is proposed. It applies the wavelet theory to enhance the PSO in exploring the solution space more effectively for a better solution. A suite of benchmark test functions and three industrial applications (solving the load flow problems, modeling the development of fluid dispensing for electronic packaging, and designing a neural-network-based controller) are employed to evaluate the performance and the applicability of the proposed method. Experimental results empirically show that the proposed method significantly outperforms the existing methods in terms of convergence speed, solution quality, and solution stability.

LMI-based stability and performance conditions for continuous-time nonlinear systems in Takagi-Sugeno's form.

This correspondence presents the stability analysis and performance design of the continuous-time fuzzy-model-based control systems. The idea of the nonparallel-distributed-compensation (non-PDC) control laws is extended to the continuous-time fuzzy-model-based control systems. A nonlinear controller with non-PDC control laws is proposed to stabilize the continuous-time nonlinear systems in Takagi-Sugeno's form. To produce the stability-analysis result, a parameter-dependent Lyapunov function (PDLF) is employed. However, two difficulties are usually encountered: 1) the time-derivative terms produced by the PDLF will complicate the stability analysis and 2) the stability conditions are not in the form of linear-matrix inequalities (LMIs) that aid the design of feedback gains. To tackle the first difficulty, the time-derivative terms are represented by some weighted-sum terms in some existing approaches, which will increase the number of stability conditions significantly. In view of the second difficulty, some positive-definitive terms are added in order to cast the stability conditions into LMIs. In this correspondence, the favorable properties of the membership functions and nonlinear control laws, which allow the introduction of some free matrices, are employed to alleviate the two difficulties while retaining the favorable properties of PDLF-based approach. LMI-based stability conditions are derived to ensure the system stability. Furthermore, based on a common scalar performance index, LMI-based performance conditions are derived to guarantee the system performance. Simulation examples are given to illustrate the effectiveness of the proposed approach.

BMI-based stability and performance design for fuzzy-model-based control systems subject to parameter uncertainties.

This paper presents the stability and performance design of a fuzzy-model-based control system subject to parameter uncertainties. A nonlinear controller with a favorable characteristic to relax the stability conditions is proposed to drive the system states of the nonlinear plant to follow those of a stable reference model. Stability and performance conditions in terms of bilinear matrix inequalities (BMIs) will be derived based on a Lyapunov-based approach. A combined genetic algorithm and convex programming technique process will be developed to solve the solution to the BMIs. An application example will be given to illustrate the merits of the proposed approach.

Sampled-data fuzzy controller for time-delay nonlinear systems: fuzzy-model-based LMI approach.

This paper presents the stability analysis and performance design for a sampled-data fuzzy control system with time delay, which is formed by a nonlinear plant with time delay and a sampled-data fuzzy controller connected in a closed loop. As the sampled-data fuzzy controller can be implemented by a microcontroller or a digital computer, the implementation time and cost can be reduced. However, the sampling activity and time delay, which are potential causes of system instability, will complicate the system dynamics and make the stability analysis much more difficult than that for a pure continuous-time fuzzy control system. In this paper, a sampled-data fuzzy controller with enhanced nonlinearity compensation ability is proposed. Based on the fuzzy-model-based control approach, linear matrix inequality (LMI)-based stability conditions are derived to guarantee the system stability. By using a descriptor representation, the complexity of the sampled-data fuzzy control system with time delay can be reduced to ease the stability analysis, which effectively leads to a smaller number of LMI-stability conditions. Information of the membership functions of both the fuzzy plant model and fuzzy controller are considered, which allows arbitrary matrices to be introduced, to ease the satisfaction of the stability conditions. An application example will be given to show the merits and design procedure of the proposed approach. Furthermore, LMI-based performance conditions are derived to aid the design of a well-performed sampled-data fuzzy controller.

Design and stabilization of sampled-data neural-network-based control systems.

This paper presents the design and stability analysis of a sampled-data neural-network-based control system. A continuous-time nonlinear plant and a sampled-data three-layer fully connected feedforward neural-network-based controller are connected in a closed loop to perform the control task. Stability conditions will be derived to guarantee the closed-loop system stability. Linear-matrix-inequality- and genetic-algorithm-based approaches will be employed to obtain the largest sampling period and the connection weights of the neural network subject to the considerations of the system stability and performance. An application example will be given to illustrate the design procedure and effectiveness of the proposed approach.

Stability analysis of fuzzy control systems subject to uncertain grades of membership.

This paper presents relaxed stability conditions for fuzzy control systems subject to parameter uncertainties. As the parameter uncertainties introduce uncertain grades of membership to the fuzzy control systems, the favorable property offered by sharing the same premises in the fuzzy plant models and fuzzy controllers cannot be employed to enhance the stabilization ability of the fuzzy control systems. To widen the applicability of the fuzzy control approach, fuzzy control systems subject to uncertain grades of membership will be investigated. New relaxed stability conditions will be derived to guarantee the stability of this class of fuzzy control systems. A numerical example will be given to show the effectiveness of the proposed approach.

An improved genetic algorithm based fuzzy-tuned neural network.

This paper presents a fuzzy-tuned neural network, which is trained by an improved genetic algorithm (GA). The fuzzy-tuned neural network consists of a neural-fuzzy network and a modified neural network. In the modified neural network, a neuron model with two activation functions is used so that the degree of freedom of the network function can be increased. The neural-fuzzy network governs some of the parameters of the neuron model. It will be shown that the performance of the proposed fuzzy-tuned neural network is better than that of the traditional neural network with a similar number of parameters. An improved GA is proposed to train the parameters of the proposed network. Sets of improved genetic operations are presented. The performance of the improved GA will be shown to be better than that of the traditional GA. Some application examples are given to illustrate the merits of the proposed neural network and the improved GA.

Fuzzy combination of fuzzy and switching state-feedback controllers for nonlinear systems subject to parameter uncertainties.

This paper presents a fuzzy controller, which involves a fuzzy combination of local fuzzy and global switching state-feedback controllers, for nonlinear systems subject to parameter uncertainties with known bounds. The nonlinear system is represented by a fuzzy combined Takagi-Sugeno-Kang model, which is a fuzzy combination of the global and local fuzzy plant models. By combining the local fuzzy and global switching state-feedback controllers using fuzzy logic techniques, the advantages of both controllers can be retained and the undesirable chattering effect introduced by the global switching state-feedback controller can be eliminated. The steady-state error introduced by the global switching state-feedback controller when a saturation function is used can also be removed. Stability conditions, which are related to the system matrices of the local and global closed-loop systems, are derived to guarantee the closed-loop system stability. An application example will be given to demonstrate the merits of the proposed approach.

Digit and command interpretation for electronic book using neural network and genetic algorithm.

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.

Design of a switching controller for nonlinear systems with unknown parameters based on a fuzzy logic approach.

This paper deals with nonlinear plants subject to unknown parameters. A fuzzy model is first used to represent the plant. An equivalent switching plant model is then derived, which supports the design of a switching controller. It will be shown that the closed-loop system formed by the plant and the switching controller is a linear system. Hence, the system performance of the closed-loop system can be designed. An application example on controlling a two-inverted pendulum system on a cart will be given to illustrate the design procedure of the proposed switching controller.

Tuning of the structure and parameters of a neural network using an improved genetic algorithm.

This paper presents the tuning of the structure and parameters of a neural network using an improved genetic algorithm (GA). It is also shown that the improved GA performs better than the standard GA based on some benchmark test functions. A neural network with switches introduced to its links is proposed. By doing this, the proposed neural network can learn both the input-output relationships of an application and the network structure using the improved GA. The number of hidden nodes is chosen manually by increasing it from a small number until the learning performance in terms of fitness value is good enough. Application examples on sunspot forecasting and associative memory are given to show the merits of the improved GA and the proposed neural network.

Design and stability analysis of fuzzy model-based nonlinear controller for nonlinear systems using genetic algorithm.

This paper presents stability analysis of fuzzy model-based nonlinear control systems, and the design of nonlinear gains and feedback gains of the nonlinear controller using a genetic algorithm (GA) with arithmetic crossover and nonuniform mutation. A stability condition is derived based on Lyapunov's stability theory with a smaller number of Lyapunov conditions. A solution of the stability conditions is also determined using GA. An application example of stabilizing a cart-pole typed inverted pendulum system is given to show the stabilizability of the nonlinear controller.

Establishment and characterization of a human cell line from paediatric cerebellar glioblastoma multiforme.

Permanent glioma cell lines are invaluable tools in understanding the biology of glioblastomas. The present study reports the establishment of a clonal human cell line, GBM6840, derived from a biopsy of paediatric cerebellar glioblastoma multiforme. GBM6840 had a doubling time of 32 h and grew as a monolayer of large round cells that retained immunopositivity for glial fibrillary acidic protein and vimentin. Karyotypic analysis revealed a modal chromosome number of 68 and polysomies of chromosomes 3, 5 and 20, as well as the presence of 3-4 marker chromosomes. GBM6840 also showed anchorage-independent growth in soft agar and tumour formation in nude mice. The p16(CDKN2A) gene was transcriptionally silenced by hypermethylation, consistent with the lack of protein expression observed in the original tumour and cultured cells. Western blot analysis revealed normal protein expression of pRb and CDK4. It appears that p16 is the major component altered in the cell cycle pathway and may confer these cells unrestrained proliferation potential. Neither EGFR gene amplification nor over-expression of the protein was detected in the cultured cells. Over-expression of the p53 protein was observed in the majority of cells, despite undetectable mutation (exons 5-8) in the gene. One allele of the PTEN gene was found to be mutated during in vitro cultivation. Telomerase activity was demonstrated in the cultured cells but not in the original tumour, supporting the hypothesis that telomerase is required for the in vitro immortalization process.