Neural network-based adaptive fault-tolerant control for nonlinear systems with uncertainties.

This paper proposes a novel fault-tolerant control (FTC) scheme for real-time uncertainty estimation in nonlinear systems. It addresses the challenges arising from nonlinear dynamics in system inputs, states, and outputs, along with measurement uncertainties, within an output feedback framework. Our approach leverages two key components: 1) A neural network NN descriptor-based observer: this novel observer concurrently estimates both system states and sensor uncertainties. It is particularly capable of handling unbounded sensor uncertainties in specific situations. It utilizes NNs as universal approximators to capture the system's complex nonlinearities. 2) A robust model reference tracking controller: this controller employs the estimated states from the NN descriptor-based observer to achieve the desired system performance despite the existence of uncertainties. It exhibits robustness, guaranteeing system stability and asymptotic state tracking to a given reference model. The efficacy of the proposed FTC scheme is validated through theoretical analysis and its application to two real-world case studies.

Adaptive leader-follower control for nonlinear uncertain multi-agent systems with an uncertain leader and unknown tracking paths.

With unknown leader parameters and false or immeasurable tracking paths, the leader-follower tracking problem via adaptive control becomes more complex and challenging. Different from the existing work in the literature and in line with real-world applications, this challenging issue is solved in this paper for multi-agent networks with different kinds of unknown uncertainties in the followers such as partially known parameters, nonlinearities, external disturbances, communication weights, and sensor faults. The solution of this challenging issue is obtained in three steps. Firstly, the tracking path is estimated by designing distributed adaptive observers at all agents that have direct communication edges with the leader. Next, by designing local adaptive observers for all followers, the estimates of all followers' states can be obtained. Then, distributed adaptive controllers for all followers are designed using the estimated states of both followers and their neighbors in addition to the estimated tracking path. The tracking performances are provided via sufficient conditions for both the adaptive observers and the adaptive controllers. The proposed methods are evaluated through two examples that are commonly used in the literature. The obtained results prove the efficacy of the recommended adaptive approaches.

Secure control design for nonlinear cyber-physical systems under DoS, replay, and deception cyber-attacks with multiple transmission channels.

This paper introduces the state-/output-feedback control for multi-channel nonlinear cyber-physical systems (CPSs). Many cyber-attacks are considered such as Denial-of-Service (DoS), replay and deception attacks. The deception cyber-attacks can be treated as measurement additive and multiplicative uncertainties. Both time-varying state-dependent and state-independent sensor additive attacks are considered. As DoS attack makes the CPS states unavailable, the standard modeling​ and control methods cannot be applied directly. Alternatively, as attackers in the replay attack re-transmit previous data and prevent the transmission of the more recent data, a delayed model is generated. To deal with these problems, a new observer at the controller side is proposed. It is used to perform two main tasks. The first is to estimate all system states at every time instant. The second is to exclude some unsecured transmitting channels from affecting the system response. Therefore, all attacks in these channels will have no effect on the system response. Using the estimated states, an anti-cyber-attacks state-feedback controller is investigated. Meanwhile, it is verified that the suggested approach certifies the convergence of all the CPSs states under different cyber-attacks. The effectiveness of the proposed secure control approach against different kinds of cyber-attacks is confirmed through two examples with simulation results.

Cooperative control for cyber-physical multi-agent networked control systems with unknown false data-injection and replay cyber-attacks.

The paper discusses the cooperative tracking problem of partially known cyber-physical multi-agent networked systems. In this system, there exist two cascaded chances for cyber-attacks. The local agent is of networked system type that is subjected to unknown false data-injection and replay cyber-attacks that are dissimilar in the sensor-controller and the controller-actuator network parts. The communication between any two agents, if they are connected, is accomplished via a communication network that is subjected to false data-injection cyber-attacks. The problem of the existing two cascaded chances for cyber-attacks is solved in three steps. First, with partially known system parameters and unknown false data-injection and replay cyber-attacks, the state estimates of all the local followers are evaluated by designing local adaptive observers. Second, a new technique is designed to compensate for the unmatched terms that result from the use of local adaptive observers. After that, distributed adaptive leader-follower security controllers are proposed based on the local estimated information in addition to the infected arrived information from the neighbors. Meanwhile, it is verified that the suggested security control method guarantees that all states of the followers under the considered cyber-attacks follow the given leader asymptotically. The efficacy of the developed adaptive leader-follower security controllers is verified via an illustrative example.

A new unmatched-disturbances compensation and fault-tolerant control for partially known nonlinear singular systems.

This study investigates the challenge of designing a fault-tolerant control (FTC) for partially known nonlinear singular systems subjected to unmatched uncertainties in addition to actuator and sensor faults. The suggested technique is dependent on the neural network-based adaptive observer. The unknown system nonlinearities are approximated by making use of an adaptive neural network (NN) approximation technique. The parameters of the NN are unknown. A new methodology is proposed to transform the partially known singular system to a non-singular form with unknown uncertainties. Different from all previous work dealing with singular systems with partially known system parameters, an adaptive observer is proposed with the help of adaptive laws to obtain an estimation of the augmented states of the constructed descriptor system. Based on the estimated states, a new approach for dealing with unmatched disturbances and faults are proposed. Finally, an adaptive controller is designed to account for unmatched disturbances and faults. The asymptotic stability of the overall system is guaranteed via Lyapunov-stability functional. The designed method is applied efficiently to a satellite control system as a practical example in addition to another numerical example.

A new online delay estimation-based robust adaptive stabilizer for multi-input neutral systems with unknown actuator nonlinearities.

This paper studies the problem of actuator-nonlinearities compensation in the multi-input uncertain neutral systems. The neutral systems with different unknown time-varying delays, unmodeled dynamics, nonlinear perturbations and disturbances are considered. A new methodology based on the online estimation of the delays to compensate for the effects generated by dead-zone and saturation, acting in series on a system's input are presented. The online estimations of the unknown delays are accomplished by adaptive laws that guarantee the exponential stabilities of the estimated delays. In the presence of state time delay, derivative state time delay, unmodeled dynamics, nonlinear perturbations, disturbances and both input dead-zone and saturation, the asymptotic stability of the closed-loop system is ensured and the state response converge to the origin. Finally, the practicability and the efficacy of the proposed approach is demonstrated via a numerical example.

Anti-windup adaptive PID control design for a class of uncertain chaotic systems with input saturation.

In this paper, the stabilization problem of actuators saturation in uncertain chaotic systems is investigated via an adaptive PID control method. The PID control parameters are auto-tuned adaptively via adaptive control laws. A multi-level augmented error is designed to account for the extra terms appearing due to the use of PID and saturation. The proposed control technique uses both the state-feedback and the output-feedback methodologies. Based on Lyapunov׳s stability theory, new anti-windup adaptive controllers are proposed. Demonstrative examples with MATLAB simulations are studied. The simulation results show the efficiency of the proposed adaptive PID controllers.