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Showing papers on "Fuzzy control system published in 2006"


Journal ArticleDOI
TL;DR: A survey on recent developments (or state of the art) of analysis and design of model based fuzzy control systems based on the so-called Takagi-Sugeno fuzzy models or fuzzy dynamic models.
Abstract: Fuzzy logic control was originally introduced and developed as a model free control design approach. However, it unfortunately suffers from criticism of lacking of systematic stability analysis and controller design though it has a great success in industry applications. In the past ten years or so, prevailing research efforts on fuzzy logic control have been devoted to model-based fuzzy control systems that guarantee not only stability but also performance of closed-loop fuzzy control systems. This paper presents a survey on recent developments (or state of the art) of analysis and design of model based fuzzy control systems. Attention will be focused on stability analysis and controller design based on the so-called Takagi-Sugeno fuzzy models or fuzzy dynamic models. Perspectives of model based fuzzy control in future are also discussed

1,575 citations


Journal ArticleDOI
TL;DR: A method of image compression and reconstruction on the basis of the F-transform, which is a fuzzy partition of a universe into fuzzy subsets (factors, clusters, granules etc.), is presented.

548 citations


Journal ArticleDOI
TL;DR: The condition is represented in the form of linear matrix inequalities (LMIs) and is shown to be less conservative than some relaxed quadratic stabilization conditions published recently in the literature and to include previous results as special cases.
Abstract: This paper proposes a new quadratic stabilization condition for Takagi-Sugeno (T-S) fuzzy control systems. The condition is represented in the form of linear matrix inequalities (LMIs) and is shown to be less conservative than some relaxed quadratic stabilization conditions published recently in the literature. A rigorous theoretic proof is given to show that the proposed condition can include previous results as special cases. In comparison with conventional conditions, the proposed condition is not only suitable for designing fuzzy state feedback controllers but also convenient for fuzzy static output feedback controller design. The latter design work is quite hard for T-S fuzzy control systems. Based on the LMI-based conditions derived, one can easily synthesize controllers for stabilizing T-S fuzzy control systems. Since only a set of LMIs is involved, the controller design is quite simple and numerically tractable. Finally, the validity and applicability of the proposed approach are successfully demonstrated in the control of a continuous-time nonlinear system.

467 citations


Journal ArticleDOI
TL;DR: In SAFIS, the concept of ''Influence'' of a fuzzy rule is introduced and using this the fuzzy rules are added or removed based on the input data received so far.

340 citations


Journal ArticleDOI
TL;DR: This paper presents the correct centroid formulae for fuzzy numbers and justify them from the viewpoint of analytical geometry and a numerical example demonstrates that Cheng's formULae can significantly alter the result of the ranking procedure.

336 citations


Journal ArticleDOI
TL;DR: Simulation results show that the hybridmultiagent system provides significant improvement in traffic conditions when evaluated against an existing traffic signal control algorithm as well as the SPSA-NN-based multiagent system as the complexity of the simulation scenario increases.
Abstract: Real-time traffic signal control is an integral part of the urban traffic control system, and providing effective real-time traffic signal control for a large complex traffic network is an extremely challenging distributed control problem. This paper adopts the multiagent system approach to develop distributed unsupervised traffic responsive signal control models, where each agent in the system is a local traffic signal controller for one intersection in the traffic network. The first multiagent system is developed using hybrid computational intelligent techniques. Each agent employs a multistage online learning process to update and adapt its knowledge base and decision-making mechanism. The second multiagent system is developed by integrating the simultaneous perturbation stochastic approximation theorem in fuzzy neural networks (NN). The problem of real-time traffic signal control is especially challenging if the agents are used for an infinite horizon problem, where online learning has to take place continuously once the agent-based traffic signal controllers are implemented into the traffic network. A comprehensive simulation model of a section of the Central Business District of Singapore has been developed using PARAMICS microscopic simulation program. Simulation results show that the hybrid multiagent system provides significant improvement in traffic conditions when evaluated against an existing traffic signal control algorithm as well as the SPSA-NN-based multiagent system as the complexity of the simulation scenario increases. Using the hybrid NN-based multiagent system, the mean delay of each vehicle was reduced by 78% and the mean stoppage time, by 85% compared to the existing traffic signal control algorithm. The promising results demonstrate the efficacy of the hybrid NN-based multiagent system in solving large-scale traffic signal control problems in a distributed manner

334 citations


Journal ArticleDOI
TL;DR: A control structure that makes possible the integration of a kinematic controller and an adaptive fuzzy controller for trajectory tracking is developed for nonholonomic mobile robots using a fuzzy logic system (FLS).
Abstract: In this paper, a control structure that makes possible the integration of a kinematic controller and an adaptive fuzzy controller for trajectory tracking is developed for nonholonomic mobile robots. The system uncertainty, which includes mobile robot parameter variation and unknown nonlinearities, is estimated by a fuzzy logic system (FLS). The proposed adaptive controller structure represents an amalgamation of nonlinear processing elements and the theory of function approximation using FLS. The real-time control of mobile robots is achieved through the online tuning of FLS parameters. The system stability and the convergence of tracking errors are proved using the Lyapunov stability theory. Computer simulations are presented which confirm the effectiveness of the proposed tracking control law. The efficacy of the proposed control law is tested experimentally by a differentially driven mobile robot. Both simulation and results are described in detail.

330 citations


Journal ArticleDOI
TL;DR: In this article, a maximum power point tracker using fuzzy set theory is presented to improve energy conversion efficiency of photovoltaic (PV) generation, by using a fuzzy cognitive network, which is in close cooperation with the presented fuzzy controller.
Abstract: The studies on the photovoltaic (PV) generation are extensively increasing, since it is considered as an essentially inexhaustible and broadly available energy resource. However, the output power induced in the photovoltaic modules depends on solar radiation and temperature of the solar cells. Therefore, to maximize the efficiency of the renewable energy system, it is necessary to track the maximum power point of the PV array. In this paper, a maximum power point tracker using fuzzy set theory is presented to improve energy conversion efficiency. A new method is proposed, by using a fuzzy cognitive network, which is in close cooperation with the presented fuzzy controller. The new method gives a very good maximum power operation of any PV array under different conditions such as changing insolation and temperature. The simulation studies show the effectiveness of the proposed algorithm

318 citations


Book
05 Dec 2006
TL;DR: Multilayer Control Structure, Model-based Fuzzy Control, model-based Predictive Control, and set-point Optimization are presented.
Abstract: Multilayer Control Structure.- Model-based Fuzzy Control.- Model-based Predictive Control.- Set-point Optimization.

291 citations


Journal ArticleDOI
TL;DR: A new method is provided by introducing some free-weighting matrices and employing the lower bound of time-varying delay and based on the Lyapunov-Krasovskii functional method, sufficient condition for the asymptotical stability of the system is obtained.

265 citations


Journal ArticleDOI
TL;DR: Experimental results show that better control can be achieved using a type-2 FLC with fewer fuzzy sets/rules so one benefit of type-1 FLC is a lower trade-off between modeling accuracy and interpretability.

Proceedings ArticleDOI
07 Sep 2006
TL;DR: An approach to real-time generation of fuzzy rule-base systems of extended Takagi-Sugeno (xTS) type from data streams is proposed in the paper, which leads to a very powerful construct - evolving xTS (exTS).
Abstract: An approach to real-time generation of fuzzy rule-base systems of extended Takagi-Sugeno (xTS) type from data streams is proposed in the paper. The xTS fuzzy system combines both zero and first order Takagi-Sugeno (TS) type systems. The fuzzy rule-base (system structure) evolves starting 'from scratch' based on the data distribution in the joint input/output data space. An incremental clustering procedure that takes into account the non-stationary nature of the data pattern and generates clusters that are used to form fuzzy rule based systems antecedent part in on-line mode is used as a first stage of the non-iterative learning process. This structure proved to be computationally efficient and powerful to represent in a transparent way complex non-linear relationships. The decoupling of the learning task into a non-iterative, recursive (thus computationally very efficient and applicable in real-time) clustering with a modified version of the well known recursive parameter estimation technique leads to a very powerful construct - evolving xTS (exTS). It is transparent and linguistically interpretable. The contributions of this paper are: i) introduction of an adaptive recursively updated radius of the clusters (zone of influence of the fuzzy rules) that learns the data distribution/variance/scatter in each cluster; ii) a new condition to replace clusters that excludes contradictory rules; iii) an extended formulation that includes both zero order TS and simplified Mamdani multi-input-multi-output (MIMO) systems; iv) new improved formulation of the membership functions, which closer resembles the normal Gaussian distribution; v) introduction of measures of clusters quality that are used to form the antecedent parts of respective fuzzy rules, namely their age and support; vi) experimental results with a well known benchmark problem as well as with real experimental data of concentration of exhaust gases (NOx) in on-line modeling of car engine test rigs

Journal ArticleDOI
TL;DR: This paper presents the correct normalization methods for interval and fuzzy weights and offers relevant theorems in support of them and numerical examples are examined to show the correctness of the proposednormalization methods.

Journal ArticleDOI
TL;DR: This paper presents an adaptive nonsingular terminal sliding mode (NTSM) tracking control design for robotic systems using fuzzy wavelet networks, which requires no prior knowledge about the dynamics of the robot and no off-line learning phase.
Abstract: This paper presents an adaptive nonsingular terminal sliding mode (NTSM) tracking control design for robotic systems using fuzzy wavelet networks. Compared with linear hyperplane-based sliding control, terminal sliding mode controller can provide faster convergence and higher precision control. Therefore, a terminal sliding controller combined with the fuzzy wavelet network, which can accurately approximate unknown dynamics of robotic systems by using an adaptive learning algorithm, is an attractive control approach for robots. In addition, the proposed learning algorithm can on-line tune parameters of dilation and translation of fuzzy wavelet basis functions and hidden-to-output weights. Therefore, a robust control law is used to eliminate uncertainties including the inevitable approximation errors resulted from the finite number of fuzzy wavelet basis functions. The proposed controller requires no prior knowledge about the dynamics of the robot and no off-line learning phase. Moreover, both tracking performance and stability of the closed-loop robotic system can be guaranteed by Lyapunov theory. Finally, the effectiveness of the fuzzy wavelet network-based control approach is illustrated through comparative simulations on a six-link robot manipulator

Journal ArticleDOI
TL;DR: An algorithm is developed to repair an inconsistent fuzzy preference relation and to make it become one with weak transitivity, via a synthesis matrix which reflects the relationship between the fuzzy preference relationship with additive consistency and the initial one given by a decision maker.

Journal ArticleDOI
TL;DR: Three methods are presented to perform the center of gravity (COG) defuzzification method in the context of linguistic fuzzy models with t-norm-based inference: one well-known method, the discretisation method, and two new methods, the slope-based method and the modified transformation function method.

Journal ArticleDOI
TL;DR: A guided rules reduction system (GRRS) is proposed to regulate the number of rules required during the fuzzy RPN modeling process and the effectiveness of the proposed GRRS is investigated using three real‐world case studies in a semiconductor manufacturing process.
Abstract: Purpose – To propose a generic method to simplify the fuzzy logic‐based failure mode and effect analysis (FMEA) methodology by reducing the number of rules that needs to be provided by FMEA users for the fuzzy risk priority number (RPN) modeling process.Design/methodology/approach – The fuzzy RPN approach typically requires a large number of rules, and it is a tedious task to obtain a full set of rules. The larger the number of rules provided by the users, the better the prediction accuracy of the fuzzy RPN model. As the number of rules required increases, ease of use of the model decreases since the users have to provide a lot of information/rules for the modeling process. A guided rules reduction system (GRRS) is thus proposed to regulate the number of rules required during the fuzzy RPN modeling process. The effectiveness of the proposed GRRS is investigated using three real‐world case studies in a semiconductor manufacturing process.Findings – In this paper, we argued that not all the rules are actual...

Journal ArticleDOI
01 Aug 2006
TL;DR: A Takagi-Sugeno (T-S) model is employed to represent a networked control system (NCS) with different network-induced delays, and a parity-equation and fuzzy-observer-based approach for fault detection of an NCS were developed.
Abstract: A Takagi-Sugeno (T-S) model is employed to represent a networked control system (NCS) with different network-induced delays. Comparing with existing NCS modeling methods, this approach does not require the knowledge of exact values of network-induced delays. Instead, it addresses situations involving all possible network-induced delays. Moreover, this approach also handles data-packet loss. As an application of the T-S-based modeling method, a parity-equation approach and a fuzzy-observer-based approach for fault detection of an NCS were developed. An example of a two-link inverted pendulum is used to illustrate the utility and viability of the proposed approaches

Journal ArticleDOI
TL;DR: A fuzzy neural network combined with a chaos-search genetic algorithm (CGA) and simulated annealing (SA) applied to short-term power-system load forecasting as a sample test demonstrates an encouraging degree of accuracy superior to other commonly used forecasting methods available.
Abstract: A fuzzy neural network combined with a chaos-search genetic algorithm (CGA) and simulated annealing (SA), hereafter called the FCS method, or simply the FCS, applied to short-term power-system load forecasting as a sample test is proposed in this paper. A fuzzy hyperrectangular composite neural network (FHCNN) is adopted for the initial load forecasting. An integrated CGA and fuzzy system (CGF) and SA is then used to find the optimal FHCNN parameters instead of the ones with the back propagation method. The CGF method will generate a set of parameters for a feasible solution. The CGF method holds good global search capability but poor local search ability. On the contrary, the SA method possesses a good local optimal search capability. We hence propose in this paper to combine the two methods to exploit their advantages and, furthermore, to eliminate the known downside of the traditional artificial neural network. The proposed FCS is next applied to power-system load forecasting as a sample test, which demonstrates an encouraging degree of accuracy superior to other commonly used forecasting methods available. The forecasting results are tabulated and partially converted into bar charts for evaluation and clear comparisons.

Journal ArticleDOI
TL;DR: The results from real experiments show that the unmanned vehicles behave very similarly to human-driven cars and are very adaptive to any kind of situation at a broad range of speeds, thus raising the safety of the driving and allowing cooperation with manually driven cars.
Abstract: Research on adaptive cruise control (ACC) with Stop&Go maneuvers is presently one of the most important topics in the field of intelligent transportation systems. The main feature of such controllers is that there is adaptation to a user-preset speed and, if necessary, speed reduction to keep a safe distance from the vehicle ahead in the same lane of the road, whatever the speed. The extreme case is the stop and go operation in which the lead car stops and the vehicle at the rear must also do so. This paper presents the development of an ACC system and related experiments. The system input information is acquired by a real-time kinematic phase differential global positioning system (GPS) (i.e., centimetric GPS) and wireless local area network links. The outputs are the variables that control the pressure on the throttle and brake pedals, which is calculated by an onboard computer. In addition, the car control is based on fuzzy logic. The system has been installed in two mass-produced Citroe/spl uml/n Berlingo electric vans, in which all the actuators have been automated to achieve humanlike driving. The results from real experiments show that the unmanned vehicles behave very similarly to human-driven cars and are very adaptive to any kind of situation at a broad range of speeds, thus raising the safety of the driving and allowing cooperation with manually driven cars.

Journal ArticleDOI
TL;DR: This paper studies the stability and stabilization problems for time-delay nonlinear systems through Takagi-Sugeno (T-S) fuzzy model approach and presents results in terms of LMIs.

Journal ArticleDOI
TL;DR: A robust fuzzy neural network sliding-mode control based on computed torque control design for a two-axis motion control system is proposed and the motion tracking performance is significantly improved, and robustness to parameter variations, external disturbances, cross-coupled interference, and friction force can be obtained as well.
Abstract: A robust fuzzy neural network (RFNN) sliding-mode control based on computed torque control design for a two-axis motion control system is proposed in this paper. The two-axis motion control system is an x-y table composed of two permanent-magnet linear synchronous motors. First, a single-axis motion dynamics with the introduction of a lumped uncertainty including cross-coupled interference between the two-axis mechanism is derived. Then, to improve the control performance in reference contours tracking, the RFNN sliding-mode control system is proposed to effectively approximate the equivalent control of the sliding-mode control method. Moreover, the motions at x-axis and y-axis are controlled separately. Using the proposed control, the motion tracking performance is significantly improved, and robustness to parameter variations, external disturbances, cross-coupled interference, and friction force can be obtained as well. Furthermore, the proposed control algorithms are implemented in a TMS320C32 DSP-based control computer. From the simulated and experimental results due to circle and four leaves reference contours, the dynamic behaviors of the proposed control systems are robust with regard to uncertainties

Journal ArticleDOI
TL;DR: A class of fuzzy time-delay descriptor systems in the extended Takagi-Sugeno (T-S) fuzzy model are studied for the stability and stabilization in terms of linear matrix inequalities (LMIs).
Abstract: This paper studies a class of fuzzy time-delay descriptor systems in the extended Takagi-Sugeno (T-S) fuzzy model. Sufficient conditions are derived for the stability and stabilization in terms of linear matrix inequalities (LMIs). Illustrative examples are given to show the effectiveness and the advantages of the present results

Journal ArticleDOI
TL;DR: It is observed that the network losses are reduced when the voltage stability is enhanced by the network reconfiguration, and the fuzzy genetic algorithm uses a suitable coding and decoding scheme for maintaining the radial nature of the network at every stage of genetic evolution.

Journal ArticleDOI
TL;DR: The proposed i-FMOLP method aims to simultaneously minimize the total distribution costs and the total delivery time with reference to fuzzy available supply and total budget at each source, and fuzzy forecast demand and maximum warehouse space at each destination.

Journal ArticleDOI
TL;DR: Computer simulation results show that the dynamic behavior of TS fuzzy controller is better than the conventional Pl controller and is found to be more robust to changes in load and other system parameters when implemented for PWM switching signal generation.
Abstract: This paper describes the application of Takagi-Sugeno (TS)-type fuzzy logic controller to a three-phase shunt active power filter for the power-quality improvement and reactive power compensation required by a nonlinear load. The advantage of fuzzy logic control is that it does not require a mathematical model of the system. The application of the Mamdani-type fuzzy logic controller to a three-phase shunt active power filter was investigated earlier but it has the limitation of a larger number of fuzzy sets and rules. Therefore, it needs to optimize a large number of coefficients, which increases the complexity of the controller. On the other hand, TS fuzzy controllers are quite general in that they use arbitrary input fuzzy sets, any type of fuzzy logic, and the general defuzzifier. Moreover, the TS fuzzy controller could be designed by using a lower number of rules and classes. Further, in this paper, the hysteresis current control mode of operation is implemented for pulsewidth-modulation switching signal generation. Computer simulation results show that the dynamic behavior of the TS fuzzy controller is better than the conventional proportional-integral (PI) controller and is found to be more robust to changes in load and other system parameters compared to the conventional PI controller.

Journal ArticleDOI
TL;DR: A fuzzy logic controller (FLC) for the path tracking of a wheeled mobile robot based on controlling the robot at a higher level is presented and automatically follows a sequence of discrete waypoints, and no interpolation of the waypoints is needed to generate a continuous reference trajectory.

Journal ArticleDOI
TL;DR: Simulation and experiments on the newly proposed 8/6-pole DSPM machine have shown that the proposed new self-tuning fuzzy PI controller offers better adaptability than the normal linear PI control and that the developed motor drive offers better steady-state and dynamic performances.
Abstract: In a doubly salient permanent-magnet (DSPM) motor drive, it is difficult to get satisfied control characteristics by using a normal linear proportional plus integral (PI) controller due to the high nonlinearity between speed and current or torque. Hence, a new self-tuning fuzzy PI controller with conditional integral, which is performed by a single-chip N87C196KD, is proposed. The initial parameters of the controller are optimized by using genetic arithmetic. Simulation and experiments on the newly proposed 8/6-pole DSPM machine have shown that the proposed new self-tuning fuzzy PI controller offers better adaptability than the normal linear PI control and that the developed motor drive offers better steady-state and dynamic performances.

Journal ArticleDOI
TL;DR: This paper provided the stability conditions and controller design for a class of structural and mechanical systems represented by Takagi-Sugeno (T-S) fuzzy models and this control problem can be reduced to linear matrix inequalities (LMI) problems by the Schur complements and efficient interior-point algorithms.

Journal ArticleDOI
TL;DR: Experimental results indicate that the proposed simplified type- 2 FLC is as robust as a conventional type-2 FLC, while lowering the computational cost.
Abstract: Increasingly, genetic algorithms (GAs) are used to optimize the parameters of fuzzy logic controllers (FLCs). Although GAs provide a systematic design approach, the optimization process is generally performed off-line using a plant model. Differences between the model and physical plant may result in unsatisfactory control performance when the FLCs are deployed in practice. Type-2 FLCs are an attractive alternative because they can better cope with modeling uncertainties. Unfortunately, type-2 FLCs are computationally intensive. This paper presents a simplified type-2 FLC that is suitable for real-time applications. The key idea is to only replace some critical type-1 fuzzy sets by type-2 sets. Experimental results indicate that the proposed simplified type-2 FLC is as robust as a conventional type-2 FLC, while lowering the computational cost.