Mohamed Chemachema

This paper presents a finite-time fault-tolerant neural-network adaptive controller designed for strict-feedback fractional-order nonlinear systems subject to actuator failures, input saturation, immeasurable states, and external perturbations. Unlike most existing adaptive-control methods for fractional-order systems, which heavily rely on complex backstepping schemes, our proposed algorithm transforms the original system into an affine Brunovsky form. This transformation enables a direct and straightforward controller design, voiding the intricacies of recursive backstepping. The algorithm addresses four types of unknown actuator faults: bias, drift, and loss of accuracy (as additive faults), as well as loss of effectiveness (as a multiplicative fault). An observer estimates the unmeasured virtual states, while a pair of neural networks approximates the uncertain nonlinearities and compensates for actuator saturation. The proposed approach ensures robust tracking performance and bounded signals in the closed-loop system, even in the presence of actuator faults. Stability analysis shows that the tracking error converges to a bounded set near the origin in finite time. Finally, simulation results validate the effectiveness and accuracy of the proposed technique.

This paper introduces a fault-tolerant fuzzy adaptive controller for a specific class of strict-feedback fractional-order nonlinear systems. Systems are susceptible to actuator and sensor failures, input saturation, and external disturbances. The proposed control scheme effectively addresses four types of faults that affect sensors and actuators, including additive faults such as bias, drift, and loss of accuracy, as well as the multiplicative fault of loss of effectiveness. In contrast to the complexity associated with the back-stepping technique, the proposed approach relies on state transformation. Fuzzy logic System (FLS) are incorporated to approximate unknown smooth nonlinear functions. A tracking-error observer is introduced to estimate unknown new states, and the properties of the reciprocal function and the strictly positive real function (SPR) are leveraged to overcome the unavailability of original state variables. Stability analysis of the closed-loop system is conducted using the Lyapunov theory. The results of numerical simulation are presented to validate the feasibility and effectiveness of the proposed controller.

Summary: This paper presents neural networks (NNs) adaptive controller for an uncertain fractional‐order nonlinear system in strict‐feedback form, subject to input saturation, unavailable states for measurement, and external disturbances. The fractional‐order adaptive laws are derived based only on the output tracking error thanks to the implementation of the strictly positive real (SPR) property, differently from the existing results in the literature of fractional‐order strict‐feedback systems where all system states are used in the adaptive laws. The proposed design approach addresses the nonaffine nature of the control input due to saturation nonlinearity by using the mean value theorem and follows a nonrecursive design by using a state transformation where the advantage is twofold. First, it eliminates the explosion complexity found in back‐stepping control‐based approaches design, and second, it reduces the approximators units and parameters in controller implementation. Furthermore, an observer is introduced to estimate the unavailable newly defined states, and then an output adaptive feedback control design is ensured. An NN is used to approximate the unknown ideal control law, and an auxiliary control term is appended to deal with saturation effect, unknown disturbance, and approximation errors. The tracking error is proved to converge asymptotically to a bounded set using the Lyapunov theory. Simulation results on three examples show the effectiveness of the proposed approach.

Summary: For a class of multiple‐input multiple‐output large‐scale nonlinear systems in strict‐feedback form with input saturation, external disturbances and immeasurable states, an adaptive decentralized neural network (NN) control strategy on the basis of event triggered mechanism is investigated in this article. In contrast to the literature, the proposed method is centered on the control‐error as a replacement to the tracking‐error that leads to a simplified derivation approach of adaptive laws. Furthermore, the control gains for this class of systems are considered unknown nonlinear functions and not assumed as simple unity or known gains as always done in the literature. Moreover, the challenge of losing controllability that typically arises in state transformation‐based methods, as reported in the literature, is entirely resolved in our approach. Last and not least, all restrictions imposed on unmatched interconnections are eliminated along with avoiding the complexity explosion caused by recursive back‐stepping designs. For this end, the unknown ideal control laws are approximated using NNs, while additional control terms are added to handle saturation effects, unknown interactions, and approximation errors. Additionally, fuzzy inference systems are employed to estimate unknown control errors. Due to the strictly positive real property, the tracking errors are proved to belong to a small compact set using Lyapunov theory. Simulation results demonstrate the effectiveness of the proposed approach.

This article discusses the problem of stabilizing the attitude control of a quadrotor system, which is subject to uncertainty, external disturbances, and sensor and actuator faults. To address these challenges, the control stage employs universal approximators, such as fuzzy systems, which estimate the system’s uncertainties and eliminate nonaffine nonlinear actuator faults. In addition, the particle swarm optimization technique is used to adjust the adaptive parameters and fuzzy initial values. The robust control term is carefully designed to handle approximation errors, time-varying sensors, and external disturbances. To solve the issue of the unavoidable algebraic loop during the actuator approximation phase, a Butterworth low-pass filter is integrated. This approach automatically deals with external disturbances, and no further approximation is necessary. Furthermore, the controller can be reconfigured online to enable fast-fault compensation without requiring a fault detection or isolation unit. To prove the global stability and boundedness of all signals in the closed-loop system, Lyapunov theory is used.