An adaptive neural network (NN) control is proposed for an unknown two-degree of freedom (2-DOF) helicopter system with unknown backlash-like hysteresis and output constraint in this study. A radial basis function NN is adopted to estimate the unknown dynamics model of the helicopter, adaptive variables are employed to eliminate the effect of unknown backlash-like hysteresis present in the system, and a barrier Lyapunov function is designed to deal with the output constraint. Through the Lyapunov stability analysis, the closed-loop system is proven to be semiglobally and uniformly bounded, and the asymptotic attitude adjustment and tracking of the desired set point and trajectory are achieved. Finally, numerical simulation and experiments on a Quanser's experimental platform verify that the control method is appropriate and effective. 

Adaptive Neural Network Control of an Uncertain 2-DOF Helicopter With Unknown Backlash-Like Hysteresis and Output Constraints

Event-triggered adaptive consensus for stochastic multi-agent systems with saturated input and partial state constraints

The device faults commonly exist in microgrids (MGs) and often undermine the system stability. However, the existing distributed methods generally design control protocols based on the absence of faults or only considering actuator faults, which limits the applicability of controllers in MG systems. Therefore, this paper investigates an adaptive prescribed fixed-time performance (PFTP) consensus fault-tolerant control (FTC) problem for the voltage regulation of a nonlinear islanded MG inverter-based distributed generators (DGs) with output contactor and actuator fault via the state event-triggered mechanism. The PFTP method for the voltage restoration synchronization error is proposed by constructing a novel performance function to increase the speed of voltage recovery. Based on the estimation properties of fuzzy logic system, an unknown nonlinear function and the residual term of output contactor are estimated. Then, an adaptive state event-triggering mechanism is designed to reduce the communication burden, and its main advantage is that the control protocol is updated in the aperiodic way at the triggering instants. Further, according to backstepping technology, a novel adaptive event-triggered consensus FTC protocol is constructed to achieve the voltage regulation of islanded MG, and a parameter adaptive law is designed to compensate the adverse effects of the output contactor fault. Based on the Lyapunov stability theory, it is proved that the voltage recovery synchronization error satisfies the PFTP, and all signals in the closed-loop systems are cooperatively uniformly ultimately bounded. Finally, the proposed control strategy is also validated by using the MATLAB.

Event-Based Fuzzy Adaptive Consensus FTC for Microgrids With Nonlinear Item via Prescribed Fixed-Time Performance

Observer-based PID control for actuator-saturated systems under binary encoding scheme

In this paper, the design of an event-triggered fault-tolerant control for leader-following consensus in multi-agent systems subject to actuator faults is presented. The problem under consideration is to reduce the control update rate and to guarantee that all the agents follow the trajectories of a leader when one or all the agents are subject to actuators faults. The proposed fault-tolerant strategy is based on distributed virtual actuators without re-tuning the nominal leader-following control. Linear matrix inequalities-based conditions are synthesized to guarantee the stability of the synchronization error and estimation error. The effectiveness of the event-triggered fault-tolerant strategy is illustrated through numerical examples 

Event-triggered fault-tolerant leader-following control for homogeneous multi-agent systems

Neural networks-based adaptive event-triggered consensus control for a class of multi-agent systems with communication faults

Neural networks-based adaptive tracking control of multi-agent systems with output-constrained and unknown hysteresis

This paper studies the event-triggered fault-tolerant control problem of nonlinear multi-agent systems. The goal is to ensure the stability of event-based sampling multi-agent systems when the actuator faults occurs. The neural networks approximate property is used to approximate unknown ideal control parameters, which can reduce the exact requirements of control parameters. Based on the states information of neighboring agents, a distributed fault-tolerant consensus controller is designed for the leaderless multi-agent systems. Moreover, an event-triggered mechanism with special definition of event-triggered error is applied to reduce the amount of communications. In addition, the Zeno behaviour is avoided. By using Lyapunov stability theory, it is proved that all signals are bounded in the closed-loop systems. Finally, a numerical simulation result is presented to prove the effectiveness of the proposed method.

Adaptive neural networks event-triggered fault-tolerant consensus control for a class of nonlinear multi-agent systems

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Neural networks-based adaptive event-triggered consensus control for a class of multi-agent systems with communication faults

Neural networks-based adaptive tracking control of multi-agent systems with output-constrained and unknown hysteresis

Adaptive neural networks event-triggered fault-tolerant consensus control for a class of nonlinear multi-agent systems