Adaptive Fuzzy CMAC Research Articles

An adaptive supervisory sliding fuzzy cerebellar model articulation controller for sensorless vector-controlled induction motor drive systems.

This paper presents the implementation of an adaptive supervisory sliding fuzzy cerebellar model articulation controller (FCMAC) in the speed sensorless vector control of an induction motor (IM) drive system. The proposed adaptive supervisory sliding FCMAC comprised a supervisory controller, integral sliding surface, and an adaptive FCMAC. The integral sliding surface was employed to eliminate steady-state errors and enhance the responsiveness of the system. The adaptive FCMAC incorporated an FCMAC with a compensating controller to perform a desired control action. The proposed controller was derived using the Lyapunov approach, which guarantees learning-error convergence. The implementation of three intelligent control schemes—the adaptive supervisory sliding FCMAC, adaptive sliding FCMAC, and adaptive sliding CMAC—were experimentally investigated under various conditions in a realistic sensorless vector-controlled IM drive system. The root mean square error (RMSE) was used as a performance index to evaluate the experimental results of each control scheme. The analysis results indicated that the proposed adaptive supervisory sliding FCMAC substantially improved the system performance compared with the other control schemes.

Speed controller design of direct torque control system for induction motors by using adaptive supervisory Fuzzy-CMAC

A model-free approach was used to develop an adaptive supervisory Fuzzy-cerebellar model articulation controller (ASFCMAC) for a direct torque control system for an induction motor without shaft encoder. The two parts of the ASFCMAC are a supervisory controller for limiting tracking error to a bounded range and a Fuzzy-cerebellar model articulation controller subsystem for learning and approximating system dynamics. The ASFCMAC parameters are tuned according to adaptive rules derived from Lyapunov stability theory. Simulations and experimental comparisons with adaptive Fuzzy-cerebellar model articulation controller, adaptive cerebellar model articulation controller, fuzzy logic control, and proportional–integral control show that the proposed ASFCMAC has a superior root mean square error in operation over a wide range of speeds.

Adaptive Control for Free-floating Space Flexible Robot Based on Fuzzy CMAC

The tracking control problems of free-oating exible space robot are studied by the paper, an adaptive Fuzzy CMAC PID control method is proposed by this paper. Dynamics equation of free-oating exible space robot is established base on Lagrange principle, Controller based on Fuzzy CMAC is designed to used to adaptive learn and compensate inverse-model of exible space robot, learning rules of network parameters are designed to adaptive adjust on line. Error function is provided by PID controller. The controller can improved the control accuracy and the asymptotic convergence of tracking error. The simulation results show that the control method had important engineering value.

Design and Implementation of Adaptive Fuzzy Cerebellar Model Articulation Controller for Direct Torque Control System

This study implemented fuzzy control theory with a cerebellar controller to design a fuzzy cerebellar model articulation controller (FCMAC), ensuring system stability by deriving an adaptive FCMAC weight update rule from the Lyapunov theory. The speed estimator design in this study is based on the structure of an adaptive stator flux observer (ASFO) to achieve rapid sensorless control. According to results from experiments, installing the adaptive FCMAC speed controller in the induction motor’s direct torque control system demonstrates excellent speed dynamic response.

Hybrid control of a parallel platform based on pneumatic artificial muscles combining sliding mode controller and adaptive fuzzy CMAC

A three degree of freedom parallel platform based on pneumatic artificial muscle (TDOFPPM) with obvious features of nonlinearity, uncertainty and coupling is introduced in this paper. It would be a challenge to control a nonlinear system with strong uncertainties. The complexity of air flow and the hysteresis of pneumatic artificial muscle are the main uncertainties in the TDOFPPM system. A hybrid controller combining sliding mode controller and adaptive fuzzy CMAC (AFCMAC) uncertainty compensator has been proposed for TDOFPPM. In order to guarantee the approximation ability for those main uncertainties, the input space and parallel structure are carefully designed for AFCMAC compensator. Finally a TDOFPPM experiment facility is introduced and the posture tracking control of TDOFPPM under two reference input signals are presented. The experiment results indicate the performance of hybrid controller is favorable.

Adaptive Fuzzy Cerebellar Model Articulation Controller for Two-Wheeled Robot

In this paper, an adaptive Fuzzy Cerebellar Model Articulation Controller (FCMAC) is proposed to control two-wheeled robot at upright position. The dynamical model of the robot, LEGO Mindstorms NXT, is derived from Lagrange of kinetic and potential energies. Based on the developed model, an adaptive FCMAC is then designed. Adaptation laws are derived from the Lyapunov stability analysis. According to the stability analysis, the developed FCMAC guarantees that the system tracking performance and the error convergence can be assured in the closed-loop system. Finally, to compare the system performances we apply the proposed FCMAC and the PID (Proportional Integral Derivative) controller to balance the two-wheeled robot.

Adaptive fuzzy cerebellar model articulation control for switched reluctance motor drive

This work presents a novel adaptive fuzzy cerebellar model articulation controller (AFCMAC) to regulate the speed of a switched reluctance motor (SRM). The proposed controller comprises two parts – a fuzzy cerebellar model articulation controller (CMAC) and a compensating controller. The fuzzy CMAC learns and approximates system dynamics; the compensating controller compensates the approximation error of the fuzzy CMAC. The parameters of the AFCMAC are adjusted online according to adaptive rules, which are derived from Lyapunov stability theory, so that both the stability of the control system and error convergence can be guaranteed. The effectiveness and robustness of the proposed AFCMAC are investigated by numerical simulation and experimental studies. Three control strategies, AFCMAC, ACMAC and proportional–integral (PI) control, are experimentally investigated and the performance index, root mean square error (RMSE) of each scheme is evaluated. The experimental results indicate that AFCMAC provides a much better system performance than the other compared schemes. The proposed AFCMAC performs well in tracking ability, parameter variation capacity and load disturbance rejection capability. The effectiveness and practicability of the proposed control scheme in a practical SRM drive are experimentally verified.

Adaptive fuzzy CMAC control for a class of nonlinear systems with smooth compensation

Adaptive fuzzy cerebellar model articulation controller (CMAC) schemes are proposed to solve the tracking problem for a class of nonlinear systems. The proposed method provides a simple control architecture that merges CMAC and fuzzy logic, so that the complicated structure and the input space dimension in CMAC can be simplified. Adaptive laws are developed to tune all of the control gains online, thereby accommodating the uncertainty of nonlinear systems without any learning phase. In particular, smooth compensation is adopted to overcome the chattering problem associated with conventional switching compensation. By Lyapunov stability analysis, it is guaranteed that all of the closed-loop signals are bounded and the tracking errors converge exponentially to a residual set whose size can be adjusted by changing the design parameters. Simulation results for its applications to three examples are presented to demonstrate the performance of the proposed methodology.