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In this study, we develop a fuzzy back-propagation (BP) neural network controller for active vibration control of a centrifugal pendulum vibration absorber (CPVA). The fuzzy BP neural network controller systems can be viewed as a conventional fuzzy algorithm for coarse tuning. The BP algorithm can also be applied for fine tuning, in this case to regulate the anti-resonance frequency in an active pendulum vibration absorber (APVA), by suppressing vibration of the carrier. The dynamic model of the APVA was developed and simulated using MATLAB. In the simulation results, when the frequency of the disturbance changes, the outputs of the fuzzy BP neural network controller are used to determine an appropriate value for the torque of the active pendulum such that the vibration amplitude of the carrier is minimized. A comparison of the carrier vibration results for the CPVA, the fuzzy algorithm and the fuzzy BP algorithm is performed. The simulation results demonstrate the effectiveness of the proposed fuzzy BP neural network APVA for reducing the carrier vibrations.

The lithium ion battery is considered as the experimental object, and its discharge characteristics are studied. A model of the battery in different charge-states is established by a tool of neural network while battery’s rebound voltage, temperature and load are set as input parameters. The validity of the model is tested based on the experimental data. The accuracy, adaptability and stability of the SOC in this model is validated in a variety of the working conditions, and the accuracy of the model is demonstrated to be higher than 5%.

This paper describes an image processing system that calculates real time information from the combustion process itself. The applications of the system on burner and supplemental fuel adjustment, and ignition trend monitoring are also discussed. Finally, new combustion control based on the image information and quantitative model of the furnace is discussed.

A mobile ad hoc network is a collection of mobile hosts forming a temporary network on the fly, without using any fixed infrastructure. Characteristics of mobile ad hoc networks such as lack of central coordination, mobility of hosts, dynamically varying network topology, and limited availability of resources make QoS provisioning very challenging in such networks.

In this paper, we introduce a fuzzy QoS traffic conditioner for mobile ad hoc networks. The proposed traffic conditioner consists of fuzzy admission control (FAC), fuzzy traffic rate controller (FTRC), and fuzzy scheduler (FS).

The proposed FAC monitors the delay and available bandwidth and decides whether to accept or reject the request. The FTRC uses the additive increase multiplicative decrease (AIMD) rate control algorithm as a base, in which a node increments its transmission rate when the observed delay is below the predefined threshold, with an increment rate of c Kbps and decreases its transmission rate by r% when the delay passes the threshold. FTRC accepts the packet delay and the "delay-threshold d" as inputs and calculates c and r by using a set of fuzzy rules. The third part of the proposed traffic conditioner is FS, which is based on the traditional weighted round robin (WRR) mechanism. FS monitors the packet drop and delay of each queue and adjusts the queue weights by using a fuzzy inference engine.

This paper presents a scalable architecture suitable for the implementation of high-speed fuzzy inference systems on reconfigurable hardware. The main features of the proposed architecture, based on the Takagi–Sugeno inference model, are scalability, high performance, and flexibility. A scalable fuzzy inference system (FIS) must be efficient and practical when applied to complex situations, such as multidimensional problems with a large number of membership functions and a large rule base. Several current application areas of fuzzy computation require such enhanced capabilities to deal with real-time problems (e.g., robotics, automotive control, etc.). Scalability and high performance of the proposed solution have been achieved by exploiting the inherent parallelism of the inference model, while flexibility has been obtained by applying hardware/software codesign techniques to reconfigurable hardware. Last generation reconfigurable technologies, particularly field programmable gate arrays (FPGAs), make it possible to implement the whole embedded FIS (e.g., processor core, memory blocks, peripherals, and specific hardware for fuzzy inference) on a single chip with the consequent savings in size, cost, and power consumption. As a prototyping example, we implemented a complex fuzzy controller for a vehicle semi-active suspension system composed of four three-input FIS on a single FPGA of the Xilinx's Virtex 5 device family.

Light-weight network gateways often employ a cost-effective embedded network processor and have received a strong demand for empowering content filtering services. In this regard, we were motivated to propose a specialized cache, fuzzy-updated cache automata matching (FCAM) circuit for accelerating the embedded network processors. Although automata matching algorithms are robust with deterministic matching time, there is still plenty of room for improving its average-case performance. The proposed FCAM employs cache to accelerate the root state and nonroot state with the multiple characters matching, and applies the fuzzy decision to improve the cache performance. In our experiment, the FPGA implementation of FCAM can perform at the rate of 10.5 Giga bits per second with the patterns of 25,642 bytes. This performance is superior to previous matching hardware in terms of throughput and pattern set.

This paper proposes a linear sampled-data controller for the stabilization of chaotic system. The system stabilization and performance issues will be investigated. Stability conditions will be derived based on the Lyapunov approach. The findings of the maximum sampling period and the feedback gain of controller, and the optimization of system performance will be formulated as a generalized eigenvalue minimization problem. Based on the analysis result, a stable linear sampled-data controller can be realized systematically to stabilize a chaotic system. An example of stabilizing a Lorenz system will be given to illustrate the design procedure and effectiveness of the proposed approach.

This paper deals with the problem of stability analysis and stabilization via Takagi-Sugeno (T-S) fuzzy models for nonlinear time-delay systems. First, Takagi-Sugeno (T-S) fuzzy models and some stability results are recalled. To design fuzzy controllers, nonlinear time-delay systems are represented by Takagi-Sugeno fuzzy models. The concept of parallel-distributed compensation (PDC) is employed to determine structures of fuzzy controllers from the T-S fuzzy models. LMI-based design problems are defined and employed to find feedback gains of fuzzy controller and common positive definite matrices P satisfying stability a delay-dependent stability criterion derived in terms of Lyapunov direct method. Based on the control scheme and this criterion, a fuzzy controller is then designed via the technique of PDC to stabilize the nonlinear time-delay system and the H^∞ control performance is achieved in the mean time. Finally, the proposed controller design method is demonstrated through numerical simulations on the chaotic and resonant systems.

This paper integrates wavelet sound wave analysis with a fuzzy control method to develop a stage phobia analysis system for vocal performers in order to enhance the psychological efficiency of vocal performers and reduce the effect of stage phobia on vocal performance. To achieve howling signal filtering, the frequency sub-band with howling is reversed and then superimposed with the original signal in audio processing. Furthermore, this paper incorporates the actual requirements for processing the vocal audio spectrum and builds the corresponding practical modules. Furthermore, this paper integrates the research needs of vocal performers’ stage phobia to create system function modules, and investigates the psychological activities of vocal performers using the fuzzy control system, discovers the factors that influence stage performances, and improves the psychological output of vocal performers. Finally, this paper proposes experiments to test and evaluate the system’s results. The research findings indicate that the system described in this paper has a significant impact.

A robustness design of fuzzy control via model-based approach is proposed in this paper to overcome the effect of approximation error between nonlinear system and Takagi-Sugeno (T-S) fuzzy model. T-S fuzz model is used to model the resonant and chaotic systems and the parallel distributed compensation (PDC) is employed to determine structures of fuzzy controllers. Linear matrix inequality (LMI) based design problems are utilized to find common definite matrices P and feedback gains K satisfying stability conditions derived in terms of Lyapunov direct method. Finally, the effectiveness and the feasibility of the proposed controller design method is demonstrated through numerical simulations on the chaotic and resonant systems.

Inference problems are one of the main research topics in the artificial intellect field. So far there have been various inference systems, some of them have been applied in fuzzy control according to their feature. In 1989, the concept of truth-valued flow inference was introduced by Wang¹, and its mathematical theory of truth-valued flow inference was set up by Wang² in 1995. In this paper, aimed at the real meaning of the truth-valued in the truth-valued flow inference, we introduce the concepts of the inner product truth-valued and inner product truth-valued flow inference, and analyze some inference algorithms of fuzzy control at present in detail. We reveal that the fuzzy inference algorithms in fuzzy control at present are all regarded as inner product truth-valued flow inference algorithm. Finally, inner product truth-valued flow inference approach is generalized in the multiple inputs and single output.

An interval-valued fuzzy logic controller (I-V FLC) is presented to control a class of nonlinear distributed parameter systems. The proposed FLC is inspired by human operators' knowledge or expert experience to control a distributed parameter process from the point of view of overall space domain. Based on spatial fuzzy set, the I-V FLC employs a centralized rule base over the space domain. Using spatial membership degree fusion operation, the I-V FLC can compress spatial input information into interval-valued fuzzy sets and then execute an interval-valued rule inference mechanism; thereby the I-V FLC has the capability to process spatial information over the space domain. Compared with traditional FLCs, the I-V FLC can improve its control performance due to its increased ability to express and process spatial information. The I-V FLC is successfully applied to a catalytic packed-bed reactor and compared with the traditional FLCs. The results demonstrate its effectiveness to control the unknown nonlinear distributed parameter process.

In this paper, the problems of stability and control for a class of uncertain nonlinear systems with unknown state time-delay are studied by using the fuzzy logic systems. Because the dynamic surface control technique is introduced to deal with the uncertain time-delay systems, the designed adaptive fuzzy controller can avoid the issue of "explosion of complexity", which comes from the traditional backstepping design procedure. Compared with the existing results in the literature, the robustness to the fuzzy approximation errors is improved by adjusting the estimations of the unknown bounds for the approximation errors. It is shown that the resulting closed-loop system is stable in the sense that all the signals are bounded and the system output track the reference signal in a small neighborhood of the origin by choosing design parameters appropriately. Three simulation examples are given to demonstrate the effectiveness of the proposed techniques.

This paper discusses the social learning of robot partners through interaction with a person. We use a robot music player; Miuro, and we focus on the music selection for providing the comfortable sound field for the person. First, we propose the control architecture of Miuro based on autonomous behavior mode, interactive behavior mode, and human control mode. Next, we propose a learning method of the relationship between human interaction and its corresponding reaction based on Boltzmann selection, adaptive reward function, and temperature control. The experimental results show that the proposed method can learn the relationship between human interaction and its corresponding behavior, even if the human intention is changed in the learning. Furthermore, the experimental results show that the proposed method can provide the person the preferable song as the comfortable sound field.

The spacecraft attitude control systems are becoming more and more sophisticated with the increasing complex system configurations. This paper investigates the problem of three-axis rigid spacecraft maneuver control. The rigid spacecraft model consisting of the dynamic and kinematics equation is firstly provided. This nonlinear model is converted into a Takagi-Sugeno fuzzy model. Then, based on the parallel distributed compensation scheme, a fuzzy state feedback controller is designed for the obtained T-S fuzzy model with considering the decay rate and input constraints. Next, sufficient conditions for the existence of such a controller are derived in terms of linear matrix inequalities and the controller design is cast into a convex optimization problem subject to linear matrix inequalities constraints, which can be readily solved via Matlab LMI toolbox. At last, a design example shows that the time of spacecraft attitude maneuver is shortened and the input constraint is realized. The simulation results show the effectiveness of the proposed methods.

This paper is concerned with the problem of observer-based fuzzy control design for discrete-time T-S fuzzy bilinear systems. Based on the piecewise quadratic Lyapunov function (PQLF), the piecewise fuzzy observer-based controllers are designed for T-S fuzzy bilinear systems. It is shown that the stability for discrete T-S fuzzy bilinear system can be established if there exists a PQLF can be constructed and the fuzzy observer-based controller can be obtained by solving a set of nonlinear minimization problem involving linear matrix inequalities(LMIs) constraints. An iterative algorithm making use of sequential linear programming matrix method (SLPMM) to derive a single-step LMI condition for fuzzy observer-based control design. Finally, an illustrative example is provided to demonstrate the effectiveness of the results proposed in this paper.

The paper considers the usefulness of a control strategy based on a fuzzy relational model of the controller to counteract uncertainties caused by measurement noise and unmeasured disturbances. The fuzzy relational model is identified using a combination of feedback error learning and fuzzy identification. An important feature of the resulting fuzzy relational model is that it will generate a fuzzy output in the presence of uncertainties. Two causes of uncertainty are considered separately, the first cause of uncertainty is due to the noise on the sensor measuring the controlled variable and the second one is an unmeasured input disturbance. Results are presented that show that the fuzzy control signal is representative of the uncertainties and that conditional defuzzification can then be used to improve the control performance by reducing the control activity.

This paper introduces sensors employing a fuzzy numeric to symbolic interface. The fundamental design considerations for this kind of fuzzy symbolic sensor, or fuzzy sensor, are formally presented. Then, the use of these components for fuzzy control is discussed and illustrated.

In this paper, a class of fuzzy controllers is considered. The controllers are constructed by applying product-max-COA(Center Of Area) inference method. The membership functions of the antecedence and the consequence are triangular and singelton in shape, respectively. The class of fuzzy controllers can be expressed by an explicit form, i.e. the sum of a linear function and some nonlinear terms. The explicit form of the class of controllers is generalized for multiple inputs. Therefore, by the use of the explicit form, the analysis of the fuzzy control system can be performed with the use of nonlinear control theory.

This paper presents a new adaptive fuzzy control scheme that is formulated and constructed directly in the control objective space. The idea of the objective-centered for-mulism on the basis of decomposition of closed-loop response profile is clarified first followed by a detailed description of the scheme. Unlike the existing adaptive fuzzy control methods, the rules and the membership functions of the fuzzy controller in the new scheme are fixed and the adaptation is done on the input and output weighting factors of the fuzzy controller. A simulation analysis is conducted to evaluate the controller performance in regulating a structure-varying process, and to illustrate the advantage of the scheme in controlling plants that can not be easily handled by other control approaches.