Sentence examples for model articulation control from inspiring English sources

The advantage of using cerebellar model articulation control (CMAC) network has been well documented in many applications.

The adaptive output recurrent cerebellar model articulation control (AORCMAC) is an adaptive system with simple computation, good generalization capability and fast learning property.

In this paper, we propose a sensorless wind energy conversion system (WECS) maximum wind power point tracking using Takagi Sugeno fuzzy cerebellar model articulation control (T-S CMAC).

A direct adaptive output feedback CMAC (cerebellar model articulation controller) control based on only output measurements is proposed with unknown control gain function for a class of affine nonlinear systems.

An adaptive fuzzy cerebellar model articulation controller-based (CMAC) nonlinear control with the advantage of architecture learning is proposed.

This paper attempts to propose a hybrid adaptive cerebellar model articulation controller (CMAC) sliding mode control (SMC; called HAC-SMC) with a supervisory controller for a class of nonlinear system, in which the HAC composed of a direct adaptive CMAC and an indirect adaptive CMAC control is performed as the SMC.

This paper proposes a hybrid control architecture and an algorithm which integrates cerebellar model articulation controller (CMAC) or fuzzified CMAC with fuzzy logic control and bang bang control under an intelligent supervisor.

In the proposed control system, an adaptive recurrent cerebellar model articulation controller (RCMAC) is used to mimic an ideal backstepping control and a robust controller is designed to attenuate the effects caused by unmodeled dynamics, disturbances and approximate errors.

In the proposed control system, an adaptive backstepping cerebellar model articulation controller (ABCMAC) is used to mimic an ideal backstepping control (IBC), and a robust H∞ controller is designed to attenuate the effect of the residual approximation errors and external disturbances with desired attenuation level.

This paper presents an intelligent control scheme that uses a cerebellar model articulation controller (CMAC) and genetic algorithms (GA) in aircraft automatic landing control and to make automatic landing systems (ALS) more intelligent.

In the proposed control system, an adaptive output recurrent cerebellar model articulation controller (ORCMAC) is used to mimic an ideal backstepping control and a robust controller is designed to attenuate the effects caused by lumped uncertainty term (such as unmodeled dynamics, external disturbances and approximate errors), so that the H∞ tracking performance can be achieved.