Sampled-data fuzzy controller for continuous nonlinear systems

The sampled-data fuzzy control of nonlinear systems is presented. The consequents of the fuzzy controller rules are linear sampled-data sub-controllers. As a result, the fuzzy controller is a weighted sum of some linear sampled-data sub-controllers that can be implemented by a microcontroller or a digital computer to lower the implementation cost. Consequently, a hybrid fuzzy controller consisting of continuous-time grades of memberships and discrete-time sub-controller is obtained. The system stability of the fuzzy control system is investigated on the basis of Lyapunov-based approach. The sampling activity introduces discontinuity to complicate the system dynamics and make the stability analysis difficult. The proposed fuzzy controller exhibits a favourable property to alleviate the conservativeness of the stability analysis. Furthermore, linear matrix inequality-based performance conditions are derived to guarantee the system performance of the fuzzy control system. An application example is given to illustrate the merits of the proposed approac

Design and Implementation of Takagi-Sugeno Fuzzy Tracking Control for a DC-DC Buck Converter

This paper presents the design and implementation of a Takagi-Sugeno (T-S) fuzzy controller for a DC-DC buck converter using Arduino board. The proposed fuzzy controller is able to pilot the states of the buck converter to track a reference model. The T-S fuzzy model is employed, firstly, to represent exactly the dynamics of the nonlinear buck converter system, and then the considered controller is designed on the basis of a concept called Virtual Desired Variables (VDVs). In this case, a two-stage design procedure is developed: i) determine the reference model according to the desired output voltage, ii) determine the fuzzy controller gains by solving a set of Linear Matrix Inequalities (LMIs). A digital implementation of the proposed T-S fuzzy controller is carried out using the ATmega328P-based Microcontroller of the Arduino Uno board. Simulations and experimental results demonstrate the validity and effectiveness of the proposed control scheme

A new approach to adaptive fuzzy control: the controller output error method

The controller output error method (COEM) is introduced and applied to the design of adaptive fuzzy control systems. The method employs a gradient descent algorithm to minimize a cost function which is based on the error at the controller output. This contrasts with more conventional methods which use the error at the plant output. The cost function is minimized by adapting some or all of the parameters of the fuzzy controller. The proposed adaptive fuzzy controller is applied to the adaptive control of a nonlinear plant and is shown to be capable of providing good overall system performance

Realization of linear defuzzified output via mixed fuzzy logics

The realization of a linear defuzzified output of a fuzzy controller by appropriate choice of each component of the fuzzy controller is discussed. The fuzzy controller with a simplified fuzzy reasoning method is described. The linear defuzzified output of the fuzzy controller is discussed, where arbitrary numbers of triangular fuzzy numbers are employed to fuzzify the linguistic variables in fuzzy control rules. Using three mixed fuzzy logics, the author shows that the defuzzified output can be precisely equivalent to a linear function of all the inputs to the fuzzy controller[[abstract]]The realization of a linear defuzzified output of a fuzzy controller by appropriate choice of each component of the fuzzy controller is discussed. The fuzzy controller with a simplified fuzzy reasoning method is described. The linear defuzzified output of the fuzzy controller is discussed, where arbitrary numbers of triangular fuzzy numbers are employed to fuzzify the linguistic variables in fuzzy control rules. Using three mixed fuzzy logics, the author shows that the defuzzified output can be precisely equivalent to a linear function of all the inputs to the fuzzy controller[[conferencetype]]國際[[conferencedate]]19930328~19930401[[booktype]]紙本[[conferencelocation]]San Francisco, United State

Sistem Kendali Fuzzy Bertipe-2 Interval dengan Struktur Adaptif Beracuan Model

Adaptive fuzzy controller is a fuzzy controller that has ability to change its parameters when the plant's operating conditions vary. In this paper, design and implementation of model reference adaptive fuzzy control are presented. Interval type-2 fuzzy logic controller with PD-like action is employed and its performance is studied. The fuzzy controller structure is applied to control an inverted pendulum. Simulation and experimental study shows that by using similar membership function, fuzzy rules and scaling, interval type-2 adaptive fuzzy logic controller provides better control system performance compared to type-1 fuzzy controller

A modular CMOS analog fuzzy controller

The low/medium precision required for many fuzzy applications makes analog circuits natural candidates to design fuzzy chips with optimum speed/power figures. This paper presents a sixteen rules-two inputs analog fuzzy controller in a CMOS 1 /spl mu/m single-poly technology based on building blocks implementations previously proposed by the authors (1995). However, such building blocks are rearranged here to get a highly modular architecture organized from two high level blocks: the label block and the rule block. In addition, sharing of membership function circuits allows a compact design with low area and power consumption and its highly modular architecture will permit to increase the number of inputs and rules in future chips with hardly design effort. The paper includes measurements from a silicon prototype of the controller

Fuzzy Predictive Controller for Mobile Robot Path Tracking

IFAC Intelligent Components and Instruments for Control Applications, Annecy, France 1997This paper presents a way of implementing a Model Based Predictive Controller (MBPC) for mobile robot path-tracking. The method uses a non-linear model of mobile robot dynamics and thus allows an accurate prediction of the future trajectories. Constraints on the maximum attainable angular velocity is also considered by the algorithm. A fuzzy approach is used to implement the MBPC. The fuzzy controller has been trained using a lookup-table scheme, where the database of fuzzy-rules has been obtained automatically from a set of input-output training patterns, computed with the predictive controller. Experimental results obtained when applying the fuzzy controller to a TRC labmate mobile platform are given in the paper.Ministerio de Ciencia y Tecnología TAP95-0307Ministerio de Ciencia y Tecnología TAP96-884C

Аналіз використання методу субтрактивної кластерізації при створені нечітких регуляторів електрогідравлічних слідкуючих приводів автомобілів

У статті розглянута задача створення нечіткого регулятора для електрогідравлічних слідкуючих приводів автомобілів з використанням методу субтрактивної кластерізації. Проведено дослідження перехідних процесів замкненої системи електрогідравлічного слідкуючого привода з нечітким регулятором, а також дослідження впливу методу субтрактивної кластерізації на якість таких нечітких регуляторівThe problem of creating a fuzzy controller for electrohydraulic servo drive vehicles using the subtractive clustering is considered in the article. The study of transient processes of closed systeme lectrohydraulic servo drive with fuzzy controller,and a study of the influence of the subtractive clustering method on the quality of the fuzzy controller are realiz