Region Stabilization of Switched Neural Networks With Multiple Modes and Multiple Equilibria: A Pole Assignment Method

Adaptive Neural Network-Based Observer Design for Switched Systems With Quantized Measurements

This study is concerned with the adaptive neural network (NN) observer design problem for continuous-time switched systems via quantized output signals. A novel NN observer is presented in which the adaptive laws are constructed using quantized measurements. Then, persistent dwell time (PDT) switching is considered in the observer design to describe fast and slow switching in a unified framework. Accurate estimations of state and actuator efficiency factor can be obtained by the proposed observer technique despite actuator degradation. Finally, a simulation example is provided to illustrate the effectiveness of the developed NN observer design approach.

Resilient fixed-time stabilization of switched neural networks subjected to impulsive deception attacks

This article focuses on the resilient fixed-time stabilization of switched neural networks (SNNs) under impulsive deception attacks. A novel theorem for the fixed-time stability of impulsive systems is established by virtue of the comparison principle. Existing fixed-time stability theorems for impulsive systems assume that the impulsive strength is not greater than 1, while the proposed theorem removes this assumption. SNNs subjected to impulsive deception attacks are modeled as impulsive systems. Some sufficient criteria are derived to ensure the stabilization of SNNs in fixed time. The estimation of the upper bound for the settling time is also given. The influence of impulsive attacks on the convergence time is discussed. A numerical example and an application to Chua's circuit system are given to demonstrate the effectiveness of the theoretical results.

H∞Bipartite Synchronization of Double-Layer Markov Switched Cooperation-Competition Neural Networks: A Distributed Dynamic Event-Triggered Mechanism

In this article, the H_∞ bipartite synchronization issue is studied for a class of discrete-time coupled switched neural networks with antagonistic interactions via a distributed dynamic event-triggered control scheme. Essentially different from most current literature, the topology switching of the investigated signed graph is governed by a double-layer switching signal, which integrates a flexible deterministic switching regularity, the persistent dwell-time switching, into a Markov chain to represent the variation of transition probability. Considering the coexistence of cooperative and antagonistic interactions among nodes, the bipartite synchronization of which the dynamics of nodes converge to values with the same modulus but the opposite signs is explored. A distributed control strategy based on the dynamic event-triggered mechanism is utilized to achieve this goal. Under this circumstance, the information update of the controller presents an aperiodic manner, and the frequency of data transmission can be reduced extensively. Thereafter, by constructing a novel Lyapunov function depending on both the switching signal and the internal dynamic nonnegative variable of the triggering mechanism, the exponential stability of bipartite synchronization error systems in the mean-square sense is analyzed. Finally, two simulation examples are provided to illustrate the effectiveness of the derived results.

Event-Triggered Adaptive Neural Network Sensor Failure Compensation for Switched Interconnected Nonlinear Systems With Unknown Control Coefficients

In this article, a decentralized adaptive neural network (NN) event-triggered sensor failure compensation control issue is investigated for nonlinear switched large-scale systems. Due to the presence of unknown control coefficients, output interactions, sensor faults, and arbitrary switchings, previous works cannot solve the investigated issue. First, to estimate unmeasured states, a novel observer is designed. Then, NNs are utilized for identifying both interconnected terms and unstructured uncertainties. A novel fault compensation mechanism is proposed to circumvent the obstacle caused by sensor faults, and a Nussbaum-type function is introduced to tackle unknown control coefficients. A novel switching threshold strategy is developed to balance communication constraints and system performance. Based on the common Lyapunov function (CLF) method, an event-triggered decentralized control scheme is proposed to guarantee that all closed-loop signals are bounded even if sensors undergo failures. It is shown that the Zeno behavior is avoided. Finally, simulation results are presented to show the validity of the proposed strategy.

Stability Analysis and Synthesis for 2-D Switched Systems with Random Disturbance

In this paper, the stability analysis and controller design of two-dimensional (2-D) switched system with random disturbances are studied. First, based on the Fornasini–Marchesini local state space model, a random discrete 2-D switched system model is proposed. Secondly, based on the method of switching quadratic Lyapunov function, Schur complement and linear matrix inequality the criterion of the sufficiency of the asymptotically stable under any switching signal is established. The extended average residence time method is used to obtain a sufficient criterion of the mean square exponential stability of the random discrete 2-D switched system under restricted switching conditions. According to the stability analysis results, the conditions for the existence of the state feedback controller are given and the corresponding state feedback controller is designed. Finally, two examples are given to illustrate the effectiveness of the proposed method.

Fault-Tolerant Adaptive Learning Control for Quadrotor UAVs With the Time-Varying CoG and Full-State Constraints

Most existing control methods for quadrotor unmanned aerial vehicles (UAVs) are based on the primary assumption that the center of gravity (CoG) is fixed and is in the same position as the centroid, which is not necessarily true with swing load as continuously making CoG vary with the swing angle and substantially complicating the dynamic model of UAV. This article presents an adaptive learning and fault-tolerant control scheme for quadrotor UAVs with varying CoG and unknown moment of inertia. First, we establish the dynamic model of quadrotor UAVs in the presence of time-varying CoG, input saturation, and actuator fault. Then, we design a fault-tolerant adaptive learning controller for the quadrotor UAVs and show that both linear and angular velocity tracking errors are ensured to converge to a residual set around zero in the presence of full-state constraints. Furthermore, all signals in the closed-loop system are uniformly ultimately bounded. Simulation studies also confirm the effectiveness of the proposed control method.

A Simple Control Strategy Based on Trajectory Planning for Vertical Acrobot

This paper presents a simple control method on the basis of the trajectory planning for vertical Acrobot to accomplish the control goal of moving the system from the downward initial position (DIP) and steadying the system at the upward target position (UTP). First, for the active link, we frame a trajectory that contains some adjustable parameters. Along the framed trajectory, we can make the active link stabilize at its end angle from its start angle. Furthermore, we change the trajectory parameters to make the passive link also arrive at the zone near the end angle. Next, we devise a PD-based tracking controller to track this planned trajectory. In this way, the vertical Acrobot is swung up to a small zone near the UTP. Then, from the approximate linear model at the UTP, we devise a stabilization controller to stabilize the vertical Acrobot at the UTP. Finally, we implement the simulation to show the validity of the proposed method.