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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.

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.

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.

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.

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.

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.