Showing papers on "Fuzzy control system published in 2002"

DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction

Abstract: This paper introduces a new type of fuzzy inference systems, denoted as dynamic evolving neural-fuzzy inference system (DENFIS), for adaptive online and offline learning, and their application for dynamic time series prediction. DENFIS evolve through incremental, hybrid (supervised/unsupervised), learning, and accommodate new input data, including new features, new classes, etc., through local element tuning. New fuzzy rules are created and updated during the operation of the system. At each time moment, the output of DENFIS is calculated through a fuzzy inference system based on m-most activated fuzzy rules which are dynamically chosen from a fuzzy rule set. Two approaches are proposed: (1) dynamic creation of a first-order Takagi-Sugeno-type fuzzy rule set for a DENFIS online model; and (2) creation of a first-order Takagi-Sugeno-type fuzzy rule set, or an expanded high-order one, for a DENFIS offline model. A set of fuzzy rules can be inserted into DENFIS before or during its learning process. Fuzzy rules can also be extracted during or after the learning process. An evolving clustering method (ECM), which is employed in both online and offline DENFIS models, is also introduced. It is demonstrated that DENFIS can effectively learn complex temporal sequences in an adaptive way and outperform some well-known, existing models.

Genetic Fuzzy Systems: Evolutionary Tuning And Learning Of Fuzzy Knowledge Bases

Abstract: Fuzzy Rule-Based Systems Evolutionary Computation Introduction to Genetic Fuzzy Systems Genetic Tuning Processes Learning with Genetic Algorithms Genetic Fuzzy Rule-Based Systems Based on the Michigan Approach Genetic Fuzzy Rule-Based Systems Based on the Pittsburgh Approach Genetic Fuzzy Rule-Based Systems Based on the lterative Rule Learning Approach Other Genetic Fuzzy Rule-Based System Other Kinds of Evolutionary Fuzzy Systems Applications.

Fuzzy Relational Systems: Foundations and Principles

Abstract: From the Publisher: Fuzzy Relational Systems: Foundations and Principles presents a general theory of fuzzy relational systems and concentrates on selected general issues of fuzzy relational modeling in the framework of the developed theory. The text discusses phenomena hidden in the ordinary bivalent case, as well as new topics in fuzzy relational systems, such as object-attribute fuzzy relations and fuzzy concept latices, similarity, and fuzzy closure operators. Both mathematicians and engineers will find Fuzzy Relational Systems to be an invaluable teaching and reference resource in modeling and fuzzy logic.

Fuzzy logic control for parallel hybrid vehicles

Abstract: In this paper, a fuzzy logic controller is developed for hybrid vehicles with parallel configuration. Using the driver command, the state of charge of the energy storage, and the motor/generator speed, a set of rules have been developed, in a fuzzy controller, to effectively determine the split between the two powerplants: electric motor and internal combustion engine. The underlying theme of the fuzzy rules is to optimize the operational efficiency of all components, considered as one system. Simulation results were used to assess the performance of the controller. A forward-looking hybrid vehicle model was used for implementation and simulation of the controller. Potential fuel economy improvement is shown by using fuzzy logic, relative to other controllers, which maximize only the efficiency of the engine.

Uncertainty bounds and their use in the design of interval type-2 fuzzy logic systems

Abstract: We derive inner- and outer-bound sets for the type-reduced set of an interval type-2 fuzzy logic system (FLS), based on a new mathematical interpretation of the Karnik-Mendel iterative procedure for computing the type-reduced set. The bound sets can not only provide estimates about the uncertainty contained in the output of an interval type-2 FLS, but can also be used to design an interval type-2 FLS. We demonstrate, by means of a simulation experiment, that the resulting system can operate without type-reduction and can achieve similar performance to one that uses type-reduction. Therefore, our new design method, based on the bound sets, can relieve the computation burden of an interval type-2 FLS during its operation, which makes an interval type-2 FLS useful for real-time applications.

A TSK-type recurrent fuzzy network for dynamic systems processing by neural network and genetic algorithms

Abstract: In this paper, a TSK-type recurrent fuzzy network (TRFN) structure is proposed. The proposal calls for the design of TRFN by either neural network or genetic algorithms depending on the learning environment. A recurrent fuzzy network is described which develops from a series of recurrent fuzzy if-then rules with TSK-type consequent parts. The recurrent property comes from feeding the internal variables, derived from fuzzy firing strengths, back to both the network input and output layers. In this configuration, each internal variable is responsible for memorizing the temporal history of its corresponding fuzzy rule. The internal variable is also combined with external input variables in each rule's consequence, which shows an increase in network learning ability. TRFN design under different learning environments is next advanced. For problems where supervised training data is directly available, TRFN with supervised learning (TRFN-S) is proposed, and a neural network (NN) learning approach is adopted for TRFN-S design. An online learning algorithm with concurrent structure and parameter learning is proposed. With flexibility of partition in the precondition part, and outcome of TSK-type, the TRFN-S displays both small network size and high learning accuracy. For problems where gradient information for NN learning is costly to obtain or unavailable, like reinforcement learning, TRFN with Genetic learning (TRFN-G) is put forward. The precondition parts of TRFN-G are also partitioned in a flexible way, and all free parameters are designed concurrently by genetic algorithm. Owing to the well-designed network structure of TRFN, TRFN-G, like TRFN-S, is characterized by high learning accuracy. To demonstrate the superior properties of TRFN, TRFN-S is applied to dynamic system identification and TRFN-G to dynamic system control. By comparing the results to other types of recurrent networks and design configurations, the efficiency of TRFN is verified.

Systems and control

Abstract: 1. Dynamical Systems and Modeling 2. Analysis of Modeling Equations 3. Linear Systems 4. Stability 5. Optimal Control 6. Sliding Modes 7. Vector Field Methods 8. Fuzzy Systems 9. Neural Networks 10. Genetic and Evolutionary Algorithms 11. Chaotic Systems and Fractals Index

Fuzzy logic controlled shunt active power filter for power quality improvement

Abstract: The simulation and experimental study of a fuzzy logic controlled, three-phase shunt active power filter to improve power quality by compensating harmonics and reactive power required by a nonlinear load is presented. The advantage of fuzzy control is that it is based on a linguistic description and does not require a mathematical model of the system. The fuzzy control scheme is realised on an inexpensive dedicated micro-controller (INTEL 8031) based system. The compensation process is based on sensing line currents only, an approach different from conventional methods, which require harmonics or reactive volt-ampere requirement of the load. The performance of the fuzzy logic controller is compared with a conventional PI controller. The dynamic behavior of the fuzzy controller is found to be better than the conventional PI controller. PWM pattern generation is based on carrierless hysteresis based current control to obtain the switching signals. Various simulation and experimental results are presented under steady state and transient conditions.

Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models

Abstract: The construction of interpretable Takagi-Sugeno (TS) fuzzy models by means of clustering is addressed. First, it is shown how the antecedent fuzzy sets and the corresponding consequent parameters of the TS model can be derived from clusters obtained by the Gath-Geva (GG) algorithm. To preserve the partitioning of the antecedent space, linearly transformed input variables can be used in the model. This may, however, complicate the interpretation of the rules. To form an easily interpretable model that does not use the transformed input variables, a new clustering algorithm is proposed, based on the expectation-maximization (EM) identification of Gaussian mixture models. This new technique is applied to two well-known benchmark problems: the MPG (miles per gallon) prediction and a simulated second-order nonlinear process. The obtained results are compared with results from the literature.

A recurrent fuzzy-neural model for dynamic system identification

Abstract: This paper presents a fuzzy modeling approach for identification of dynamic systems. In particular, a new fuzzy model, the Dynamic Fuzzy Neural Network (DFNN), consisting of recurrent TSK rules, is developed. The premise and defuzzification parts are static while the consequent parts of the fuzzy rules are recurrent neural networks with internal feedback and time delay synapses. The network is trained by means of a novel learning algorithm, named Dynamic-Fuzzy Neural Constrained Optimization Method (D-FUNCOM), based on the concept of constrained optimization. The proposed algorithm is general since it can be applied to locally as well as fully recurrent networks, regardless of their structures. An adaptation mechanism of the maximum parameter change is presented as well. The proposed dynamic model, equipped with the learning algorithm, is applied to several temporal problems, including modeling of a NARMA process and the noise cancellation problem. Performance comparisons are conducted with a series of static and dynamic systems and some existing recurrent fuzzy models. Simulation results show that DFNN compares favorably with its competing rivals and thus it can be considered for efficient system identification.

Self-adaptive neuro-fuzzy inference systems for classification applications

Abstract: This paper presents a self-adaptive neuro-fuzzy inference system (SANFIS) that is capable of self-adapting and self-organizing its internal structure to acquire a parsimonious rule-base for interpreting the embedded knowledge of a system from the given training data set. A connectionist topology of fuzzy basis functions with their universal approximation capability is served as a fundamental SANFIS architecture that provides an elasticity to be extended to all existing fuzzy models whose consequent could be fuzzy term sets, fuzzy singletons, or functions of linear combination of input variables. Without a priori knowledge of the distribution of the training data set, a novel mapping-constrained agglomerative clustering algorithm is devised to reveal the true cluster configuration in a single pass for an initial SANFIS construction, estimating the location and variance of each cluster. Subsequently, a fast recursive linear/nonlinear least-squares algorithm is performed to further accelerate the learning convergence and improve the system performance. Good generalization capability, fast learning convergence and compact comprehensible knowledge representation summarize the strength of SANFIS. Computer simulations for the Iris, Wisconsin breast cancer, and wine classifications show that SANFIS achieves significant improvements in terms of learning convergence, higher accuracy in recognition, and a parsimonious architecture.

Observers for Takagi-Sugeno fuzzy systems

Abstract: We focus on the analysis and design of two different sliding mode observers for dynamic Takagi-Sugeno (TS) fuzzy systems. A nonlinear system of this class is composed of multiple affine local linear models that are smoothly interpolated by weighting functions resulting from a fuzzy partitioning of the state space of a given nonlinear system subject to observation. The Takagi-Sugeno fuzzy system is then an accurate approximation of the original nonlinear system. Our approach to the analysis and design of observers for Takagi-Sugeno fuzzy systems is based on extending sliding mode observer schemes to the case of interpolated multiple local affine linear models. Thus, our main contribution is nonlinear observer analysis and design methods that can effectively deal with model/plant mismatches. Furthermore, we consider the difficult case when the weighting functions in the Takagi-Sugeno fuzzy system depend on the estimated state.

Direct adaptive fuzzy-neural control with state observer and supervisory controller for unknown nonlinear dynamical systems

Abstract: In this paper, an observer-based direct adaptive fuzzy-neural network (FNN) controller with supervisory mode for a certain class of high order unknown nonlinear dynamical system is presented. The direct adaptive control (DAC) has the advantage of less design effort by not using FNN to model the plant. By using an observer-based output feedback control law and adaptive law, the free parameters of the adaptive FNN controller can be tuned on-line based on the Lyapunov synthesis approach. A supervisory controller is appended into the FNN controller to force the state to be within the constraint set. Therefore, if the FNN controller cannot maintain the stability, the supervisory controller starts working to guarantee stability. On the other hand, if the FNN controller works well, the supervisory controller will be de-activated. The overall adaptive scheme guarantees the global stability of the resulting closed-loop system in the sense that all signals involved are uniformly bounded. Simulation results also show that our initial control effort is much less than those in previous works, while preserving the tracking performance.

A First Course in Fuzzy and Neural Control

Abstract: A PRELUDE TO CONTROL THEORY An Ancient Control System Examples of Control Problems Open-Loop Control Systems Closed-Loop Control Systems Stable and Unstable Systems A Look at Controller Design Exercises and Projects MATHEMATICAL MODELS IN CONTROL Introductory Examples: Pendulum Problems State Variables and Linear Systems Controllability and Observability Stability Controller Design State Variable Feedback Control Second-Order Systems Higher-Order Systems Proportional-Integral-Derivative Control Nonlinear Control Systems Linearization Exercises and Projects FUZZY LOGIC FOR CONTROL Fuzziness and Linguistic Rules Fuzzy Sets in Control Combining Fuzzy Sets Sensitivity of Functions Combining Fuzzy Rules Truth Tables for Fuzzy Logic Fuzzy Partitions Fuzzy Relations Defuzzification Level Curves and Alpha-Cuts Universal Approximation Exercises and Projects FUZZY CONTROL A Fuzzy Controller for an Inverted Pendulum Main Approaches to Fuzzy Control Stability of Fuzzy Control Systems Fuzzy Controller Design Exercises and Projects NEURAL NETWORKS FOR CONTROL What is a Neural Network? . Implementing Neural Networks Learning Capability The Delta Rule The Back Propagation Algorithm Example: Training a Neural Network Practical Issues in Training Exercises and Projects NEURAL CONTROL Why Neural Networks in Control Inverse Dynamics Neural Networks in Direct Neural Control Example: Temperature Control Neural Networks in Indirect Neural Control Exercises and Projects FUZZY-NEURAL AND NEURAL-FUZZY CONTROL Fuzzy Concepts in Neural Networks Basic Principles of Fuzzy-Neural Systems Basic Principles of Neural-Fuzzy Systems Generating Fuzzy Rules and Membership Functions Exercises and Projects APPLICATIONS A Survey of Industrial Applications Cooling Scheme for Laser Materials Color Quality Processing Identification of Trash in Cotton Integrated Pest Management Systems Comments Bibliography Index

Hybrid adaptive fuzzy identification and control of nonlinear systems

Abstract: We present a combined direct and indirect adaptive control scheme for adjusting an adaptive fuzzy controller, and adaptive fuzzy identification model parameters First, using adaptive fuzzy building blocks, with a common set of parameters, we design and study an adaptive controller and an adaptive identification model that have been proposed for a general class of uncertain structure nonlinear dynamic systems We then propose a hybrid adaptive (HA) law for adjusting the parameters The HA law utilizes two types of errors in the adaptive system, the tracking error and the modeling error Performance analysis using a Lyapunov synthesis approach proves the superiority of the HA law over the direct adaptive (DA) method in terms of faster and improved tracking and parameter convergence Furthermore, this is achieved at negligible increased implementation cost or computational complexity We prove a theorem that shows the properties of this hybrid adaptive fuzzy control system, ie, bounds for the integral of the squared errors, and the conditions under which these errors converge asymptotically to zero are obtained Finally, we apply the hybrid adaptive fuzzy controller to control a chaotic system, and the inverted pendulum system

Stability of fuzzy control systems with bounded uncertain delays

Abstract: Global exponential stability of fuzzy control systems with delays is studied. These delays in the fuzzy control systems are assumed to be any uncertain bounded continuous functions. Stability of systems with uncertain delays is interesting since in practical applications it is not easy to know the delays exactly. Conditions for global exponential stability of free fuzzy systems with uncertain delays are derived. Criteria for design of nonlinear fuzzy controllers to feedback control the stability of global nonlinear fuzzy systems are given. Theorems are proved via the method of functional differential inequalities analysis.

A fuzzy disturbance observer and its application to control

Abstract: In this paper, a fuzzy disturbance observer (FDO) is developed and its application to the control of a nonlinear system under the internal and external disturbances is presented. To construct the FDO, two parameter tuning methods are proposed and shown to be useful in adjusting the parameters of the FDO. The first tuning method employs the disturbance observation error to guarantee that the FDO monitors the unknown disturbance. The next one enlarges the concept of error and introduces augmented error to guarantee that the FDO monitors the disturbance and the control objective is achieved. In addition, the relationships between the suggested FDO-based control and the conventional adaptive fuzzy controls reported in the previous literatures are discussed and it is shown in a rigorous manner that the disturbance observation error or the augmented error converges to a region of which size can be kept arbitrarily small. Finally, some examples and computer simulation results are presented to illustrate the effectiveness and the applicability of the FDO.

Fuzzy decision making in modeling and control

Abstract: Fuzzy Decision Making Fuzzy Decision Functions Fuzzy Aggregated Membership Control Modeling and Identification Fuzzy Decision Making for Modeling Fuzzy Model-Based Control Performance Criteria Model-Based Control with Fuzzy Decision Functions Derivative-Free Optimization Advanced Optimization Issues Application Example Future Developments Appendices: Model-Based Predictive Control Nonlinear Internal Model Control.

H/sub /spl infin// tracking-based sliding mode control for uncertain nonlinear systems via an adaptive fuzzy-neural approach

Abstract: A novel adaptive fuzzy-neural sliding-mode controller with H/sub /spl infin// tracking performance for uncertain nonlinear systems is proposed to attenuate the effects caused by unmodeled dynamics, disturbances and approximate errors. Because of the advantages of fuzzy-neural systems, which can uniformly approximate nonlinear continuous functions to arbitrary accuracy, adaptive fuzzy-neural control theory is then employed to derive the update laws for approximating the uncertain nonlinear functions of the dynamical system. Furthermore, the H/sub /spl infin// tracking design technique and the sliding-mode control method are incorporated into the adaptive fuzzy-neural control scheme so that the derived controller is robust with respect to unmodeled dynamics, disturbances and approximate errors. Compared with conventional methods, the proposed approach not only assures closed-loop stability, but also guarantees an H/sub /spl infin// tracking performance for the overall system based on a much relaxed assumption without prior knowledge on the upper bound of the lumped uncertainties. Simulation results have demonstrated that the effect of the lumped uncertainties on tracking error is efficiently attenuated, and chattering of the control input is significantly reduced by using the proposed approach.

Self-adaptive recurrent neuro-fuzzy control for an autonomous underwater vehicle

Abstract: This paper presents the utilization of a self-adaptive recurrent neuro-fuzzy control as a feedforward controller and a proportional-plus-derivative (PD) control as a feedback controller for controlling an autonomous underwater vehicle (AUV) in an unstructured environment. Without a priori knowledge, the recurrent neuro-fuzzy system is first trained to model the inverse dynamics of the AUV and then utilized as a feedforward controller to compute the nominal torque of the AUV along a desired trajectory. The PD feedback controller computes the error torque to minimize the system error along the desired trajectory. This error torque also provides an error signal for online updating the parameters in the recurrent neuro-fuzzy control to adapt in a changing environment. A systematic self-adaptive learning algorithm, consisting of a mapping-constrained agglomerative clustering algorithm for the structure learning and a recursive recurrent learning algorithm for the parameter learning, has been developed to construct the recurrent neuro-fuzzy system to model the inverse dynamics of an AUV with fast learning convergence. Computer simulations of the proposed recurrent neuro-fuzzy control scheme and its performance comparison with some existing controllers have been conducted to validate the effectiveness of the proposed approach.

Sliding mode neural network inference fuzzy logic control for active suspension systems

Abstract: In the automotive industry, suspension systems are designed to provide desirable vehicle ride and handling properties. This paper presents the development of a robust intelligent nonlinear controller for active suspension systems based on a comprehensive and realistic nonlinear model. The inherent complex nonlinear system model's structure, and the presence of parameter uncertainties, have increased the difficulties of applying conventional linear and nonlinear control techniques. Recently, the combination of sliding mode, fuzzy logic, and neural network methodologies has emerged as a promising technique for dealing with complex uncertain systems. In this paper, a sliding mode neural network inference fuzzy logic controller is designed for automotive suspension systems in order to enhance the ride and comfort. Extensive simulations are performed on a quarter-car model, and the results show that the proposed controller outperforms existing conventional controllers with regard to body acceleration, suspension deflection, and tire deflection.

An image enhancement technique combining sharpening and noise reduction

Abstract: A new approach to contrast enhancement of image data is presented. The proposed method is based on a multiple-output system that adopts fuzzy models in order to prevent the noise increase during the sharpening of the image details. Key features of the proposed technique are better performance than available methods in the enhancement of images corrupted by Gaussian noise and no complicated tuning of fuzzy set parameters. In fact, the overall nonlinear behavior of the enhancement system is very easily controlled by one parameter only.

Adaptive hybrid intelligent control for uncertain nonlinear dynamical systems

Abstract: A new hybrid direct/indirect adaptive fuzzy neural network (FNN) controller with a state observer and supervisory controller for a class of uncertain nonlinear dynamic systems is developed in this paper. The hybrid adaptive FNN controller, the free parameters of which can be tuned on-line by an observer-based output feedback control law and adaptive law, is a combination of direct and indirect adaptive FNN controllers. A weighting factor, which can be adjusted by the tradeoff between plant knowledge and control knowledge, is adopted to sum together the control efforts from indirect adaptive FNN controller and direct adaptive FNN controller. Furthermore, a supervisory controller is appended into the FNN controller to force the state to be within the constraint set. Therefore, if the FNN controller cannot maintain the stability, the supervisory controller starts working to guarantee stability. On the other hand, if the FNN controller works well, the supervisory controller will be deactivated. The overall adaptive scheme guarantees the global stability of the resulting closed-loop system in the sense that all signals involved are uniformly bounded. Two nonlinear systems, namely, inverted pendulum system and Chua's (1989) chaotic circuit, are fully illustrated to track sinusoidal signals. The resulting hybrid direct/indirect FNN control systems show better performances, i.e., tracking error and control effort can be made smaller and it is more flexible during the design process.

Speed control of induction motors using a novel fuzzy sliding-mode structure

Abstract: This paper presents a new approach to indirect vector control of induction motors. Two nonlinear controllers, one of sliding mode type and the other PI-fuzzy logic-based, define a new control structure. Both controllers are combined by means of an expert system based on Takagi-Sugeno fuzzy reasoning. The sliding-mode controller acts mainly in a transient state while the PI-like fuzzy controller acts in the steady state. The new structure embodies the advantages that both nonlinear controllers offer: sliding-mode controllers increasing system stability limits, and PI-like fuzzy logic based controllers reducing the chattering in permanent state. The scheme has been implemented and experimentally validated.

Fuzzy-based reactive power and voltage control in a distribution system

Abstract: This paper presents fuzzy-based reactive power and voltage control in a distribution system. The main purpose is to find the combination of main transformer load tap changer (LTC) positions and capacitors on/off switching operations in a day, such that the voltage deviations at the secondary bus of main transformer become as small as possible, while the reactive power flows through the main transformer and the real power losses at feeders become as little as possible. To minimize system repair cost, the total number of switching operations of LTC and capacitors in a day must be kept as few as possible. From the descriptions above, the linguistic expressions such as "as small as possible," "as little as possible," and "as few as possible" are not clear. So in this paper, the reactive power and voltage control problem is first formulated with fuzzy sets then an annealing searching technique is used to find a proper combination of LTC positions and capacitors on/off switching operations in a day. To demonstrate the effectiveness of the proposed method, reactive power and voltage control in a distribution system within the service area of Yunlin District Office of Taiwan Power Company (TPC) are analyzed. It is found that a proper dispatching schedule for LTC positions and capacitors switching operations can be reached by the proposed method.

Fuzzy model reference adaptive control

Abstract: This paper investigates a fuzzy model reference adaptive controller (FMRAC) for continuous-time multiple-input-multiple-output (MIMO) nonlinear systems. The proposed adaptive scheme uses a Takagi-Seguno (TS) fuzzy adaptive system, which allows for the inclusion of a priori information in terms of qualitative knowledge about the plant operating points or analytical regulators (e.g., state feedback) for those operating points. A proportional-integral update law is used to obtain a fast parameters adaptation. Stability and robustness of this adaptive scheme are established using Lyapunov stability tools. The simulation results, for a two-link robot, confirm the performance of the proposed approach.