Composite learning adaptive sliding mode control of fractional-order nonlinear systems with actuator faults

• Published in: Journal of the Franklin Institute Vol. 356; no. 16; pp. 9580 - 9599
• Main Authors: Liu, Heng, Wang, Hongxing, Cao, Jinde, Alsaedi, Ahmed, Hayat, Tasawar
• Format: Journal Article
• Language: English
• Published: Elmsford Elsevier Ltd 01.11.2019; Elsevier Science Ltd
• Subjects: Actuators; Adaptive control; Controllers; Error correction & detection; Excitation; Faults; Learning; Nonlinear control; Nonlinear systems; Sliding mode control; Tracking control; Tracking control systems; Tracking errors; Uncertainty
• ISSN: 0016-0032; 1879-2693; 0016-0032
• DOI: 10.1016/j.jfranklin.2019.02.042

•Composite learning sliding mode control method is used to control fractional-order nonlinear systems with actuator faults.•A fractional integral sliding surface is introduced.•The proposed method can guarantee the convergence of tracking error as well as the parameter estimation error by interval-excitation condition which is weaker that the persistent excitation condition. This paper considers the tracking control of fractional-order nonlinear systems (FONSs) in triangular form with actuator faults by means of sliding mode control (SMC) and composite learning SMC (CLSMC). In SMC design, a fractional sliding surface is introduced, and an adaptation law is designed to update the estimation of the mismatched parametric uncertainty in the actuator faults. The proposed SMC can guarantee the convergence of the tracking error where a persistent excitation (PE) condition should be satisfied. To overcome this limitation, by using the online recorded data and the instantaneous data, a prediction error of the parametric uncertainty is defined. Both the tracking error and the prediction error are utilized to generate a composite learning law. A composite learning law is designed by using the prediction error and the tracking error. The proposed CLSMC can guarantee not only the stability of system but also the accurate estimation of the parametric uncertainties in the actuator faults. In CLSMC, only an interval-excitation (IE) condition that is weaker than the PE one should be satisfied. Finally, simulation example is presented to show the control performance of the proposed methods.