Disturbance-observer-based adaptive neural event-triggered fault-tolerant control for uncertain nonlinear systems against sensor faults

This article is concerned with the event-triggered fault-tolerant control (FTC) for uncertain nonlinear cyber-physical systems (CPSs) by only exploiting the triggered faulty output. During the control design process, the unknown system dynamics, the time-varying sensor, and the actuator faults are considered simultaneously. Based on the event-triggered mechanism, the first-order filter technique and the nonlinear impulsive dynamics approach, an adaptive neural event-triggered output feedback FTC scheme is established. More specifically, one triggering condition is established for both the measurable output and the state estimations, with the adaptive parameters being triggered at the same instants. Another triggering condition is established for the controller, eliminating the need for real-time monitoring of control information and thereby reducing the computational burden. Then, a neural state observer is designed from triggered faulty output and triggered state estimations. The first-order filter technique is introduced to handle the non-differentiability of virtual controls stemmed from the event-triggered mechanism. The nonlinear impulsive dynamics approach is employed for stability analysis of the discontinuous error dynamics. It is proved that, with the proposed scheme, all the closed-loop signals are bounded, meanwhile the system output converges to the origin asymptotically, and the Zeno behavior is excluded. Finally, simulation results present the feasibility and effectiveness of the seeking schemes.

In this paper, an adaptive event-triggered constrained control strategy is proposed for uncertain nonlinear systems with input constraints by using reinforcement learning technology and disturbance observer. By constructing an Actor-Critic neural network (NN) framework, the unknown uncertainties can be tackled by online learning and more accurate compensation. The Actor-NN is adopted for generating actions (regarded as compensation signals), and the Critic-NN is employed to evaluate the performed actions (regarded as to monitor and assess the Actor-NN performance, including the control performance). Moreover, a self-learning disturbance observer with learning ability is designed to estimate the external disturbance. On the basis of the backstepping control technology, the event-triggered control method and the smooth approximation of input saturation nonlinearity, an improved event-triggered constrained control strategy is presented using reinforcement learning technique, and the rigorous theoretical proofs of the closed-loop system stability and the avoidance of Zeno behavior are presented. The application for the quadrotor unmanned aerial vehicle validates the effectiveness of the developed event-triggered control approach.

Fuzzy adaptive event-triggered finite-time constraint control for output-feedback uncertain nonlinear systems

Switching-based adaptive fault-tolerant control for uncertain nonlinear systems against actuator and sensor faults

Adaptive event-triggered control for uncertain nonlinear systems with multiple intermittent actuator faults of uncertain directions.

Event-triggered adaptive fixed-time fuzzy control for uncertain nonlinear systems with unknown actuator faults

Adaptive event-triggered output-feedback controller for uncertain nonlinear systems

The adaptive event-triggered fault-tolerant control problem for bidirectional consensus of multi-agent systems (MASs) subject to sensor faults and external disturbances is investigated. A hierarchical algorithm is first introduced to eliminate the dependence on Laplacian matrix information, thereby reducing computational complexity. Subsequently, a disturbance observer (DO) and a compensation signal were constructed to accommodate external disturbances, filtering errors, and approximation errors introduced by the radial basis function neural network (RBFNN). Compared with the absence of a disturbance observer, the tracking performance was improved by 15.2%. In addition, a switching event-triggered mechanism is considered, in which the advantages of fixed-time triggering and relative triggering are integrated to balance communication frequency and tracking performance. Finally, the boundedness of all signals under the proposed fault-tolerant control (FTC) scheme is established. It has been clearly demonstrated by the simulation results that the proposed mechanism achieves a 39.8% reduction in triggering frequency relative to the FT scheme, while simultaneously yielding a 5.0% enhancement in tracking performance compared with the RT scheme, thereby highlighting its superior efficiency and effectiveness.

This paper investigates the problem of adaptive fault tolerant tracking control for uncertain nonlinear systems in the event of external disturbances, actuator faults and input saturation. In order to guarantee the transient and steady-state control performance, the prescribed performance function is employed to impose the anticipant performance criterion on the output tracking error. By using the error transformation, the original inequality error constraint is converted to an unconstrained issue. For the conversion systems, the negative effect resulting from the external disturbances is restrained via the adaptive control approach. The radial basis function neural network (RBFNN) is adopted to handle the unknown nonlinear functions. The Nussbaum gain technique is utilised to deal with the nonlinear terms arising from the actuator faults and input saturation. Combining with the backstepping control strategy, an adaptive neural fault tolerant control (FTC) scheme is developed for the uncertain nonlinear systems. It is rigorously proved that the proposed controller is capable of ensuring the boundness of all closed-loop signals as well as the specified tracking performance. Simulation results on two examples are given to illustrate the effectiveness of the presented control approach.

Event-triggered decentralized adaptive fault-tolerant control of uncertain interconnected nonlinear systems with actuator failures

Event-triggered adaptive optimal tracking control for nonlinear stochastic systems with dynamic state constraints

This article focuses on the adaptive control issue for uncertain nonlinear systems with time-varying full-state constraints. First, a novel integral barrier Lyapunov functions (IBLFs)-based neural backstepping control approach is designed, which circumvents the trouble of conversion in the traditional used BLFs. And then, the sliding-mode disturbance observers (SMDOs) are established to deal with the immeasurable disturbances in each order of the state-constrained uncertain nonlinear systems. Besides, the dynamic threshold-based event-sampling mechanism is constructed to deal with the sparsity of resources and system controlling burden. Finally, according to the given design approach, an event-triggered adaptive controller is developed and ensures disturbance observation errors uniformly converge to the origin in finite time, and all the signals in the closed-loop system are semiglobally uniformly ultimately bounded. A developed numerical simulation case verifies the validity of the proposed approach.

Neuro-dynamic programming-based event-triggered fault tolerant control for nonlinear systems with multiple faults.

In this article, the global adaptive fault-tolerant control is considered for a class of uncertain nonlinear systems with sensors, actuator faults, and disturbance. Different from the previous results, there are no a priori bounds of the sensor sensitivity, actuation effectiveness, and disturbance. A switching-type adaptive controller is designed where the controller parameter is tuned online by the proposed switching mechanism. It has also been proven that global stability can be achieved under the designed controller. Finally, a simulation example is provided to illustrate the effectiveness of the proposed method.

Recently, event-triggered control has received increasing attention due to its advantages on saving computation and communication resources. For the controller design of nonlinear systems, backstepping method plays a vital role in most existing results. However, the backstepping control with an event-triggered scheme (ETS) merely ensures the practical stability of the system subject to uncertainties. Additionally, backstepping techniques encounter a significant challenge known as “complex explosion” when dealing with high-dimensional systems. To address such issues, this paper proposes an event-triggered adaptive sliding mode control (SMC) scheme for a class of uncertain strict feedback nonlinear systems (SFNSs) based on fully actuated system (FAS) approach. By model transformation, an FAS model of the original systems is obtained, which makes the solution of control law simple and effective. Then, a high-order sliding mode surface is designed based on FAS model, where a hyperbolic tangent function is introduced such that a nonlinear equivalent control law can be synthesized to compensate the effect of ETS. As a result, a sliding mode dynamic with fewer variables is derived. Furthermore, the stability analysis ensures the asymptotic convergence of control system without requiring global Lipschitz conditions. Finally, the effectiveness of the proposed control strategy is also illustrated by a practical example.