Integrated Fault Estimation and Fault-Tolerant Tracking Control for Lipschitz Nonlinear Multiagent Systems

Distributed Adaptive Fault-Tolerant Control for Multiagent Systems via Virtual-Actuator-Based Reconfiguration

This article aims to develop a virtual-actuator-based control scheme for the consensus tracking problem of multiagent systems (MASs) against actuator faults and mismatched disturbances. The proposed scheme has a double-layer structure. In the cyber layer, the nominal controller is designed with neighboring information for the fault-free case. While in the physical layer, the fault compensator, working as the virtual actuator, is applied to reconfigure faulty plants adaptively. This design enjoys the advantages that the nominal controller needs no adjustment and all its properties can be preserved after failure. Moreover, the proposed control scheme is distinguished by the following features: 1) the commonly imposed rank condition on outage faults is removed; 2) the norm bound of the leader's input is allowed to be unknown even though the topologies are switching and directed; and 3) there is no need to use the estimates of faults in the virtual actuator design, which means the negative impacts caused by the inaccurate fault estimation can be avoided. Finally, a numerical example is given to validate the effectiveness of the theoretical results.

Adaptive super-twisting control for leader-following consensus of second-order multi-agent systems based on time-varying gains

In this paper, robust distributed consensus control is designed based on adaptive time-varying gains for a class of nonlinear multi-agent systems (MAS) in the presence of uncertain parameters and external disturbances with unknown upper bounds. Due to various conditions and constraints, different dynamical models for the agents can be considered in practice. On the basis of a continuous homogeneous consensus method which has been proposed for the nominal nonlinear MAS, the discontinuous and continuous adaptive integral sliding mode control strategies are particularly designed and extended to accomplish exact and precise consensus for non-identical MASs influenced by imposed perturbations. However, it is noted that in practical problems, the exact upper bound of perturbations is unknown. Then, the proposed controllers have been improved in an adaptive scheme to overcome this drawback. In addition to the adaptive estimation strategy and time-varying gains, which address considered uncertain parameters in the dynamics of the following agents, the designed distributed super-twisting sliding mode strategy for nonlinear agents adjusts the gain of the control inputs and guarantees that the proposed protocol performs properly without any setbacks of the chattering phenomenon. The illustrative simulations depict the robustness, accuracy, and effectiveness of the designed methods.

Fault tolerant consensus control of multi-agent systems under dynamic event-triggered mechanisms

In this paper, the fault-tolerant consensus control (FTCC) problem is studied for multi-agent systems (MASs) with the dynamic event-triggered mechanism (DETM). To ease the communication burden, DETM governed by an additional internal dynamical variable is introduced. A novel fault-tolerant controller is presented to mitigate system performance degradation caused by failures, where a simple fault compensator is constructed through a protocol-based observer. Then, some sufficient conditions are established to examine the bounded consensus while optimizing the predetermined quadratic cost criterion. In addition, the explicit expression of the desired controller is also parameterized through orthogonal decomposition. Finally, two simulation results are made for the sake of verifying the effectiveness of the developed FTCC scheme, the fault compensation test as well as the quadratic cost one.

Resilient Adaptive Neural Control for Uncertain Nonlinear Systems With Infinite Number of Time-Varying Actuator Failures

Existing studies on adaptive fault-tolerant control for uncertain nonlinear systems with actuator failures are restricted to a common result that only system stability is established. Such a result of not being asymptotically stable is a tradeoff paid for reducing the number of online learning parameters. In this article, we aim to obviate such restrictions and improve the bounded error control to asymptotic control. Toward this end, a resilient adaptive neural control scheme is newly proposed based on a new design of the Lyapunov function candidates, a projection-associated tuning functions method, and an alternative class of smooth functions. It is proved that the system stability is guaranteed for the case of an infinite number of failures and when the number of failures is finite, asymptotic tracking performance can be automatically recovered, and besides, an explicit bound for the tracking error in terms of L₂ norm is established. Illustrative examples demonstrate the methods developed.

Finite-Time Dynamic Allocation and Control in Multiagent Coordination for Target Tracking

A new finite-time dynamic allocation and control scheme is developed in this article for multiple agents tracking a moving target. Based on a competitive manner, the dynamic allocation is achieved by k-winners-take-all (k-WTA), which can be realized by a novel finite-time dual neural network with the adaptive-gain activation function. Then, a finite-time disturbance compensation-based control law is proposed for agents to conduct capturing task or return to the specified point in vigilance. The finite-time stability of the system is guaranteed through Lyapunov analysis. Finally, the efficiency of the proposed scheme is illustrated by simulations in which the situation with higher target velocity than trackers is considered.

Actuator-Integrated Fault Estimation and Fault Tolerant Control for Electric Power Steering System of Forklift

For the problem of actuator-integrated fault estimation (FE) and fault tolerant control (FTC) for the electric power steering (EPS) system of a forklift, firstly, a dynamic model of a forklift EPS system with actuator faults was established; then, an integrated FE and FTC design was proposed. The nonlinear unknown input observer (NUIO) was proposed to estimate the system states and actuator faults, and an adaptive sliding mode FTC system was constructed based on it. The gain of the observer and controller is obtained by H∞ optimization and one-step linear matrix inequality (LMI) formula operation in order to realize the overall optimal design of an FTC system. Finally, the experimental results show that when actuator failure occurs, the proposed integrated FE and FTC were more accurate than the decentralized design to estimate the system states and the actuator faults. The proposed fault-tolerant controller can more effectively restore the power assist performance of the steering power motor in case of failure and effectively ensure the safety and reliability of the forklift EPS system.