Approximation‐based adaptive fault compensation backstepping control of fractional‐order nonlinear systems: An output‐feedback scheme

Abstract

Summary An observer‐based adaptive fuzzy backstepping approach is proposed for nonlinear systems with respect to fractional‐order differential equations, unmatched uncertainties, unmeasured states, and actuator faults. The approximation capability of fuzzy logic system and minimal learning parameter approaches are applied to identify uncertain functions in a simultaneous manner. For estimating the unavailable conditions, a fuzzy fractional‐order state‐observer is extended. Applying fault‐tolerant approach in a backstepping design methodology would provide a new fault‐tolerant adaptive fuzzy output‐feedback approach for fractional‐order strict‐feedback systems. This control structure would assure the considered system stability through selection of the appropriate Lyapunov candidate function. Two numerical simulations are run to exhibit the validity herein.

Event‐triggered finite‐time control for a class of switched nonlinear systems

“…However, the stability analysis of controlled systems becomes more difficult because of the switching signals. In recent years, the control design problem of switched nonlinear systems has attracted tremendous numbers of attention based on backstepping techniques, such as References 1‐8. Reference 9 solved the output feedback control problem for switched nonlinear systems with contains unmeasured states based on average dwell time technique.…”

“…The authors of Reference 10 developed the distributed controller for nonlinear multiagent systems with unknown and nonidentical control directions via event‐triggered communication. The developed control methods 1‐10 can guarantee the closed‐loop stability without imposing matching conditions on the controlled systems. In fact, the control methods in References 1‐10 can only guarantee the stability of the closed‐loop system when the time goes to infinity, which means that they cannot solve the problem of finite‐time control.…”

“…The developed control methods 1‐10 can guarantee the closed‐loop stability without imposing matching conditions on the controlled systems. In fact, the control methods in References 1‐10 can only guarantee the stability of the closed‐loop system when the time goes to infinity, which means that they cannot solve the problem of finite‐time control.…”

Observer‐based adaptive neural networks control for Markovian jump nonlinear systems with partial mode information and input saturation

“…During the literature mentioned above, one of crucial issues is how to stabilize unstable or chaotic systems. In order to do this, state feedback controllers were always designed, 19–22 but, for MJSs, main limitations of this kind of controllers are that mode information of Markov chain and state variables are involved in controllers, that is, they are mode‐dependent and/or state‐dependent 2,4,13,16,23–25 . Actually, the assumption of Markov mode and system states availability may not be practical, which is because the measuring of mode information and state variables in most of practical systems is too expensive or they even cannot be measured directly in some cases.…”

Stabilization control of a flexible marine riser with failed and bounded actuator and time‐varying boundary constraints

“…While qualified to tackle asymmetric input saturation, the above‐mentioned methods may be invalid when the considered system is PDE‐governed and the actuator fails simultaneously. A parameter adaptive law was designed to compensate the unknown yet invariable actuator efficient factor for a strict‐feedback uncertain nonlinear fractional‐order system 21 and successfully extended to a flexible Euler–Bernoulli (E‐B) beam in the three‐dimensional space 22 . An adaptive neural compensation scheme was reported to ensure the reliability for a class of large‐scale time delay nonlinear systems under possible additive fault 23 .…”