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This article tackles the path-tracking problem of wheeled mobile robots (WMRs) used in the Micro Robot Soccer Tournament (MicroSot). An heuristic fuzzy logic controller (FLC) has been designed based on a model-free approach. Hardware experimental results have been presented to verify that the FLC can control a WMR. The performance of a fine-tuned P-controller has been compared with that of the proposed fuzzy logic controller. The response time of the fuzzy-logic-controlled WMR is two times faster. Good tracking control performance was also obtained from the proposed controller.

This paper presents a novel fuzzy neural network which consists of an antecedent network and a consequent network. The antecedent network matches the premises of the fuzzy rules and the consequent network implements the consequences of the rules. In the network learning and training phase, a concise and effective algorithm based on the fuzzy hierarchy error approach is proposed to update the parameters of the network. This algorithm is simple to implement and it does not require as many calculations as some other classic neural network learning algorithms. A model reference adaptive control structure incorporating the proposed fuzzy neural network is studied. Simulation results of a cart-pole balancing system demonstrate the effectiveness of the proposed method.

Loop-shaping H∞ controllers are designed for both stable and integrating processes. H∞ controllers for typical industrial processes have a PID structure interpretation. Further, if the precompensator is carefully chosen, the design indicator will be independent of plant constants. This fact leads to the derivation of a direct relation between the controller parameters and the plant constants, and the results can be viewed as new PID tuning rules for stable and integrating processes. The main contribution of the tuning rules is that the PID parameters can be obtained using one design parameter which reflects the trade-off between stability robustness and time domain performance of the closed-loop system, thus making it convenient to choose PID parameters dependent on the uncertainties of the processes.

In this paper, the method of elliptic Fourier descriptors using arc length parameterization is applied to tackle the problem of detection and recovery of symmetry under parallel projection. A simple and fast iteration algorithm together with the invariants of symmetry provides sufficient information for the detection and recovery of symmetry under parallel projection. The proposed method has been extensively tested using symmetric figures under different parallel projections. Simulation results of the algorithm are presented. The extension of this method for planar object recognition under parallel projection is also addressed. >

This paper proposes an environment model acquisition technique for robot navigation in an unknown terrain, which requires no prior information about the environment, and which can handle concave Curved/polygonal obstacles as well as convex ones . In this method, features extracted from the coefficients of X/sup 2/ terms which approximate sets of 4 consecutive range sensor data with second-order polynomials by the Least-squares method. Then a "local environment map" representing the robot's line of sight environment can be built. This map is helpful for a local planner to devise safe paths in an unknown environment.

The determination of reflectional symmetric planar shapes is important for pattern recognition, shape analysis and quantitative shape recovery. In this paper, forms of shape invariants for reflectional symmetry using elli]?tic Fourier descriptors are derived. They are found Ito be independent of shape rotation, translation and orl:hographic parallel projection. By theoretical analysis, these invariants provide sufficient information for (i) detection of reflectional symmetry and skewed symmetry, (ii) the axes of symmetry and skewed-symmetry, and the skewed-transverse axis, (iii) the root locus of the gradient vector (p,q) that can interpret the vkewed symmetry as a real symmetry.

In this paper, a fuzzy neural networks based on hierarchical approach reasoning is proposed. The construction combining model is described by the fuzzy logic technology. The output of the antecedent part of the fuzzy logic is expressed as the input of the consequent part. The consequent part is a simple linear equation of the variables corresponding to the rule strength of the antecedent network and the output variables of the consequent network. So, the physical meaning of the proposed fuzzy neural network is clearer and its structure is simpler. We present a learning algorithm based on hierarchy error approach which utilizes a fuzzy logic function to aggregate the weight coefficients of the neural network, so the output can rapidly converge to the desired tolerable error range. Simulation results show the fuzzy neural network based on fuzzy hierarchy error approach have very good approach ability of for the complex functions through learning and training of the rule weight.

To reduce the difficulties in choosing an appropriate fuzzy knowledge base, ie., scaling factors, membership functions and rules etc., of a fuzzy controller, a hierarchical fuzzy controller (HFC) is proposed. This hierarchical fuzzy controller is divided into several hierarchical level subcontrollers, which can be designed separately. Each subcontroller can be designed easily and can perform well at a certain level and running state. According to the hierarchical structure, when all the subcontrollers perform well, the hierarchical fuzzy controller can gain good performance.

A model reference adaptive speed control structure for an induction motor drive with the fuzzy neural network is proposed. The fuzzy neural network is composed of an antecedent network and a consequent network. A concise and fast-speed algorithm called the fuzzy hierarchy error approach (FHEA) is employed. Simulation results demonstrate that this kind of neural fuzzy controller is effective in tracking as well as the regulation speed control for an induction motor drive.

According to the basic optimization principle of artificial neural networks, a novel kind of neural network model for solving the quadratic programming problem is presented. The methodology is based on the Lagrange multiplier theory in optimization and seeks to provide solutions satisfying the necessary conditions of optimality. The equilibrium point of the network satisfies the Kuhn-Tucker condition for the problem. The stability and convergency of the neural network is investigated and the strategy of the neural optimization is discussed. The feasibility of the neural network method is verified with computation examples. Results of the simulation of the neural network to solve optimum problems are presented to illustrate the computational power of the neural network method.

This paper considers an optimum nonuniform FIR transmultiplexer design subject to specifications in the frequency domain. Our objective is to minimize the sum of the ripple energy for all the individual filters, subject to the specifications on amplitude and aliasing distortions, and to the passband and stopband specifications for the individual filters. This optimum nonuniform transmultiplexer design problem can be formulated as a quadratic semi-infinite programming problem. The dual parametrization algorithm is extended to the design of this nonuniform transmultiplexer problem. If the lengths of the filters are sufficiently long and the set of decimation integers is compatible, then our algorithm guarantees that the solution obtained will give rise to the global minimum, and the required specifications are satisfied.

Wavelet denoising by singularity detection was proposed as an algorithm that combines Mallat and Donoho's denoising approaches. With wavelet transform modulus sum, we can avoid the error and ambiguities of tracing the modulus maxima across scales and the complicated and computationally demanding reconstruction process. We can also avoid the visual artifacts produced by shrinkage. In this paper, we investigate a multiwavelet denoising algorithm based on a modified singularity detection approach. Improved signal denoising results are obtained in comparison to the single wavelet case.

A direct composite H/sub /spl infin// controller design procedure is applied to a quarter-car active suspension model by utilizing its two-time-scale property. Unlike the usual approach, the procedure does not need to decompose the full plant into slow and fast subsystems, thus avoiding some numerical difficulties. It is shown that our reduced-order H/sub /spl infin// controller achieves better performance than controllers designed by other approaches.

This paper proposes a fast, robust Hough transform based algorithm for estimating the location (position and orientation) of a robot in a two-dimensional terrain by using range sensor data. In our approach, the overall workspace is divided into M/spl times/N grids to form a "global" map. From the acquired range sensor data, a "local" map which represents the robot's line of sight environment is constructed. Then the error of the location of the robot can be determined by matching the found "local" map with the "global" map. In this paper, the matching process is performed by a Hough transform technique which is in many aspects an improvement over the simple template matching. Our proposed technique has the advantages of large noise tolerance and it is amenable to parallel implementation on a suitable network of rather simple processing elements. >

Presents the stability and robustness analysis of digital fuzzy control systems subject to parameter uncertainties. To carry out the analysis, we first describe exactly a digital nonlinear system with parameter uncertainties with a fuzzy plant model. Second, a digital fuzzy controller is proposed to close the feedback loop. Three design approaches are introduced. Based on the resultant fuzzy control system, a stability condition is derived for each design approach. A numerical example is given to show the merits.

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.

In this paper, the problem of edge preserving smoothing in image processing is tackled using a fuzzy framework. The gray value differences between neighbour pixels are modelled as fuzzy variables and a design rule based on human experience and the nature of the filtering process gives rise to an effective filtering algorithm. Experimental results demonstrate the validity of the recursive fuzzy edge-preserving smoothing filter for both synthetic and natural images.

A new PID tuning rule is derived for integrating processes. We base our method on the loop-shaping H/sub /spl infin// design with the observation that the H/sub /spl infin// controllers can be interpreted as PID controllers and that for some choice of pre-compensator the 'design indicator' is independent of plant constants. Simulation results show that our PID settings work well compared with other settings.

In this paper, the task of integrating conflicting experts' opinions is achieved through a training of local receptive gating network using backpropagation. The front layers of the gating network consist of local-receptive fields which form feature maps of the input that enable modulations to experts' output. The resulting network achieves accurate modelling of the solution mapping through the efficient combination of existing experts. Experimental results on a histogram thresholding problem show the superior performance of the modular network over classical algorithms.

This paper presents the stability and robustness analyses, and design of fuzzy observer-controllers for uncertain multivariable nonlinear systems of which the system states are inaccessible. To proceed with the analysis, the uncertain system is represented by a fuzzy model with uncertain parameters, Based on this model, under a single-grid point (SGP) approach, a fuzzy observer-controller is designed to estimate the system states and to close the feedback loop. A simple stability criterion is derived by estimating some norms of the system parameters and the matrix measure, and a robust area in the uncertain parameter space is defined. The design methodology of a multiple-grid-point (MGP) fuzzy observer-controller for nonlinear systems subjected to large parameter uncertainties is developed based on the analysis results of the SGP approach.

Fuzzy logic controllers (FLCs) have been widely used in many applications, However, they are usually implemented digitally by expensive digital signal processors (DSPs), and the performance is restricted by the sampling period. In this paper, an analog FLC implemented by a low-cost device is proposed. Through analog circuitry, the features of an FLC such as membership function shaping, rule inference and defuzzification are realized. The controller is successfully applied to regulate a PWM boost power converter with satisfactory performance.

This paper describes the application of genetic algorithms (GAs) to a GA-optimized fuzzy-PID controller. GAs are utilized to automatically optimize the parameters of multimode controllers for cargo ships and industrial process controlled plants. Simulation results demonstrate that this GA-based approach is beneficially complementary to some classic methods.

Regarding switching converters as highly nonlinear plants with inherent parameter uncertainties, the deleterious effects due to large disturbances in line voltage and load resistance are tackled by a grid-point control approach which owns merits in both performance and cost-effectiveness. Design and experimental results based on a boost converter are given.

This paper presents the design of a fuzzy scheduler which is robust enough to (1) stabilize an uncertain nonlinear system subject to large parameter uncertainties, and (2), give an acceptable closed-loop performance. Based on a fuzzy model with parameter uncertainty information, a stability criterion which involves solving an algebraic Riccati equation (ARE) is derived, and fuzzy controllers corresponding to the vertices of the operation parameter space (vertex fuzzy controllers) are designed. It is shown that the actual control signal can be interpolated by a proposed fuzzy scheduler from the control signals of vertex fuzzy controllers.

This paper presents the stability and robustness analyses of uncertain nonlinear control systems. To proceed with the analysis, the uncertain nonlinear system is represented by a fuzzy model with uncertainties. Based on this model, under a single-grid-point (SGP) approach, a fuzzy controller is designed to close the feedback loop. A simple stability criterion is derived by estimating the norm of the system parameters and the matrix measure, and a robust area in the uncertain parameter space is defined. The design methodology of a multiple-grid-point (MGP) fuzzy controller for nonlinear systems subjected to large parameter uncertainties is developed based on the analysis results of the SGP approach.

This paper presents the analysis of stability and robustness of an uncertain nonlinear fuzzy system. To proceed with the analysis, the uncertain nonlinear plant is represented by a fuzzy model that includes parameter uncertainty information. Then, three design approaches will be introduced to close the feedback loop. By using the Lyapunov's stability theory, the closed loop uncertain fuzzy control system is shown to be stable if there exists a positive definite solution for an algebraic Riccati equation (ARE) derived. An example on stabilizing an uncertain nonlinear mass-spring-damper system is given to illustrate the stabilizability and robustness properties of the proposed fuzzy controller.

This paper presents an optimal scheme for the design of a fuzzy neural network as a controller through simulating the process of the controlled system. The structure and the parameters of the fuzzy neural network are first optimized by a three-stage strategy applying genetic algorithms off-line. The defuzzification part of the fuzzy neural network is then reconstructed and is optimized to refine the control rules online. The simulation results demonstrate that the responses are more favorable than that of conventional fuzzy controller and conventional fuzzy neural network controller trained by expert data.

In this paper, the problem of edge preserving smoothing in image processing is tackled by combining a noise corruption model and a region and edge image model. The derivation of the probability model for the first order difference in the gray levels of the region pixels and edge pixels leads to a non-linear filter with coefficients as functions of the estimated noise variance and edge intensity. Such a model-based approach allows the design of improved filters for noise filtering and image compression. Experimental results demonstrate the improved performance of the filter for both synthetic and natural images.

A new simplified model of fuzzy neural network is presented based on the functional equivalence relation between radial basis function (RBF) network and fuzzy inference system. The proposed network model has a lower number of the centre values of the network and is especially suitable for multivariable systems. An adaptive constructing method and some learning algorithms of the simplified model are proposed. The simulation results of a function mapping show that the simplified model of the fuzzy neural network has a satisfactory approximation ability to a nonlinear multivariable function.

An approach for designing Takagi-Sugeno (TS) model based fuzzy systems (1985) using output feedback with guaranteed closed-loop stability is proposed in this paper. The complex process on finding a common Lyapunov function to guarantee the system stability can be omitted. This can significantly simplify the design procedure. Moreover the overall closed-loop system behaves like a linear system and the system responses can be designed by properly choosing the coefficients of the closed-loop transfer function. An illustrative example will be given to demonstrate the ability of the proposed approach.

Systems with parameter uncertainties are difficult to control. We propose a combined controller approach to tackle this problem. This approach involves designs of different controllers for different operation points. Then a fuzzy logic controller (FLC) is used to schedule these controllers according to the plant parameters. This FLC is different from the common FLCs in that plant parameters are used as the input variables in the premises of the fuzzy rules. Hence this FLC can tackle a large parameter change during operation. The resulting FLC will not only display good robustness but also lead to a stable closed-loop system. The stability will be proved by applying a new stability analysis method.