Adaptive neural control for an uncertain fractional-order rotational mechanical system using disturbance observer

• Published in: IET control theory & applications Vol. 10; no. 16; pp. 1972 - 1980
• Main Authors: Shao, Shuyi, Chen, Mou, Chen, Shaodong, Wu, Qingxian
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 31.10.2016
• Subjects: adaptive control; Adaptive control systems; Computer simulation; control system synthesis; Control systems; Disturbances; external unknown disturbances; FODO; FORMS; Mechanical systems; neural network; Neural networks; neurocontrollers; nonlinear control systems; nonlinear fractional‐order disturbance observer; observers; position control; Research Article; robust adaptive neural control; Robust control; tracking control scheme; uncertain fractional‐order rotational mechanical system; uncertain systems; Uncertainty; NN; external unknown disturbances; uncertain systems; control system synthesis; adaptive control; neural network; nonlinear fractional-order disturbance observer; FORMS; neurocontrollers; FODO; nonlinear control systems; robust adaptive neural control; robust control; position control; tracking control scheme; uncertain fractional-order rotational mechanical system; observers
• ISSN: 1751-8644; 1751-8652
• DOI: 10.1049/iet-cta.2015.1054

In this study, a robust adaptive neural control is proposed for a fractional-order rotational mechanical system (FORMS) in the presence of system uncertainties and external unknown disturbances. System uncertainties of the FORMS are handled by the neural network (NN). To tackle unknown disturbances, a non-linear fractional-order disturbance observer (FODO) is explored for the FORMS. A robust adaptive control scheme is then developed by combining the NN with the designed FODO. Finally, numerical simulation results further demonstrate the effectiveness of the proposed tracking control scheme for the uncertain FORMS subject to external unknown disturbances.