Modelling of a Flexible Manoeuvring System Using ANFIS Techniques

The increased utilization of flexible structure systems, such as flexible manipulators and flexible aircraft in various applications, has been motivated by the requirements of industrial automation in recent years. Robust optimal control of flexible structures with active feedback techniques requires accurate models of the base structure, and knowledge of uncertainties of these models. Such information may not be easy to acquire for certain systems. An adaptive Neuro-Fuzzy inference Systems (ANFIS) use the learning ability of neural networks to adjust the membership function parameters in a fuzzy inference system. Hence, modelling using ANFIS is preferred in such applications. This paper discusses modelling of a nonlinear flexible system namely a twin rotor multi-input multi-output system using ANFIS techniques. Pitch and yaw motions are modelled and tested by model validation techniques. The obtained results indicate that ANFIS modelling is powerful to facilitate modelling of complex systems associated with nonlinearity and uncertainty

The Adaptive Control of FES-assisted Indoor Rowing Exercise

This paper describes the development of an adaptive control mechanism for FES-assisted indoor rowing exercise (FES-rowing). The FES-rowing is intro-duced as a total body exercise for rehabilitation of function of lower body through the application of functional elec-trical stimulation (FES). A model of the rowing ergometer with humanoid is developed using the visual Nastran soft-ware environment (vN4D). A fuzzy logic control (FLC) scheme is designed in Matlab/Simulink and adapted online by pre-training artificial neural network (ANN) to regulate the muscle stimulation pulse width required to drive FES-rowing. The ANN is used as an adaptation to the system that is required to account for muscle fatigue. The results signify that the adaptive control scheme is able to achieve and maintain better tracking performance. This study indicates that the adaptive control developed may provide an effective mechanism for automatically regulat-ing the stimulation pulse width for FES-rowing to over-come muscle fatigue

Modelling and Control of FES-Assisted Indoor Rowing Exercise.” UKACC

Abstract -This paper describes the development of an adaptive control mechanism for FES-assisted indoor rowing exercise (FES-rowing). The FES-rowing is introduced as a total body exercise for rehabilitation of function of lower body through the application of functional electrical stimulation (FES). A model of the rowing ergometer with humanoid is developed using the visual Nastran software environment (vN4D). A fuzzy logic control (FLC) scheme is designed in Matlab/Simulink and adapted online by pre-training artificial neural network (ANN) to regulate the muscle stimulation pulse width required to drive FES-rowing. The ANN is used as an adaptation to the system that is required to account for muscle fatigue. The results signify that the adaptive control scheme is able to achieve and maintain better tracking performance. This study indicates that the adaptive control developed may provide an effective mechanism for automatically regulating the stimulation pulse width for FES-rowing to overcome muscle fatigue