Search

This paper proposes a non‐singular fixed‐time fault‐tolerant adaptive tracking control method for a kind of multi‐input and multi‐output nonlinear system, which is in form of non‐strict feedback and is subject to actuator faults. The major contribution, compared to other practical fixed‐time fault‐tolerant control methods, is that the fixed time can be calculated precisely. By applying fuzzy logic systems, the identification of unknown nonlinearities is achieved, and by the boundness of fuzzy basis functions, the algebraic loop problem is avoided. Based on the fixed‐time stability theory, the practical fixed‐time fault‐tolerant adaptive tracking control method is proposed with adaptive back‐stepping control technology, in which the control singular problem is overcome by applying the adding power integration method. By the Lyapunov function theory, the practical fixed‐time stability of the closed‐loop system is theoretically proved. And even in the case of unknown actuator faults, the tracking error of the controlled system for the given command signal can still converge to a small neighbourhood of zero within a fixed time, regardless of the system initial state. Finally, the proposed control method is successfully implemented on a multi‐motor control platform, proving its effectiveness, and multiple comparative simulations are carried out, demonstrating its superiority. [ABSTRACT FROM AUTHOR]

In this article, an adaptive swarm control within saturated input based on the nonlinear coupling degree is proposed. Swarm control is a forward study in kinematics and dynamics of the swarm system. However, the coupling degree in the previous work can only manifest whether the agents in the swarm being connected or not, ignoring the connection strength. As a result, the nonlinear coupling degree is proposed, which is more suitable for practical engineering than the previous coupling degree. Based on the nonlinear coupling degree, we put forward novel swarm kinematics and dynamics. Besides, the effects of input saturation and nonlinear dynamics should be considered for this novel swarm control based on the nonlinear coupling degree. Therefore, we introduce the backstepping method to design an adaptive swarm controller. With this controller, the input saturation auxiliary system is designed to reduce the effects of input saturation, and a radial basis function neural network (RBF-NN) is introduced to approximate the nonlinear dynamics. To overcome the differential explosion in the backstepping method, a command filter is put forward to reduce the amount of calculation and reduces the difficulty of the controller design. It is proved that the proposed controller ensures stability based on the Lyapunov stability theory. Finally, the simulation results of a multiagent system composed of six omnidirectional mobile robots illustrate the validity of the proposed controller. [ABSTRACT FROM AUTHOR]

This paper studies the communication smooth switching problem of time-varying formation control. When the formation system performs topology switching, sudden changes in communication will cause communication failure and affect formation control. To avoid this adverse impact, we introduce a smooth transition function of communication topology. At the same time, for the formation system with unknown nonlinear dynamics, an adaptive backstepping controller combining the backstepping control method and the fuzzy logic system is designed. Then, based on Lyapunov stability theory, we prove the stability of the designed controller. The results of the simulation experiments can verify the effectiveness of the designed controller. [ABSTRACT FROM AUTHOR]

In this paper, we propose an adaptive swarm controller for a kind of high-order self-organized system. There are always unknown heterogeneous nonlinear dynamics and unmeasured states in practical systems, which may lead to poor control effects and even system instability. To eliminate these possible problems, a radial basis function neural network approximator is designed to approximate unknown heterogeneous nonlinear dynamics, and a neural network high-gain state observer method is introduced to estimate the unmeasured states of intelligent units. Besides, a novel sliding mode switching approach law is designed to improve sliding mode control. Based on these works, an adaptive swarm controller is proposed to ensure trajectory tracking. With the designed adaptive swarm controller, the high-order self-organized system can achieve aggregation, dispersion, and formation switching in the process of swarm movement. Based on Lyapunov stability theory, we prove the stability of the proposed controller. Finally, according to numerical simulation, the effectiveness of the designed controller is proved. [ABSTRACT FROM AUTHOR]