Event-Triggered Control for Multiagent Systems With Sensor Faults and Input Saturation

This paper focuses on the problem of adaptive neural network (NN) tracking control for a class of pure-feedback stochastic nonlinear systems. Via the dynamic surface control (DSC) technique and neural networks' approximation capability, a novel adaptive NN control scheme is proposed. Without using the mean value theorem, an affine variable at each step is constructed. By introducing the additional first-order low-pass filter for the actual control input, the algebraic loop problem in pure-feedback stochastic nonlinear systems is solved. It is proved that the proposed controller ensures that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) in probability while the tracking error converges to a small neighborhood of the origin. Finally, a numerical example is provided to illustrate the effectiveness of the proposed method.

In this article, an event-triggered (ET) fractional-order adaptive tracking control scheme (ATCS) is studied for the uncertain nonlinear system with the output saturation and the external disturbances by using the nonlinear disturbance observer (NDO) and the neural networks (NNs). Based on NNs, the system uncertainties are approximated. An NN-based NDO is designed to estimate the bounded disturbances. Combining the NNs, the output of the designed NDO, the fractional-order theory, and the ET mechanism, an ATCS is proposed under the output saturation. According to the stability analysis, all the closed-loop signals are semiglobally uniformly ultimately bounded based on the investigative ATCS. The simulation results and the comparative experiment verifications are shown to indicate the viability of the developed control scheme.

Fuzzy rule-based neural appointed-time control for uncertain nonlinear systems with aperiodic samplings

For nonlinear cyber-physical systems (CPSs) with input saturation and denial-of-service (DoS) attacks, an adaptive secure control strategy is investigated in this paper. To estimate the unmeasurable states, a novel switched neural network (NN) observer is designed, where two sub-observers can be freely switched from each other. Considering unnecessary packet transmissions, an improved event-triggered mechanism (ETM) is introduced to reduce the communication burden in the controller-to-actuator channel. In addition, in order to simultaneously solve the input saturation and unknown control direction problems, a Nussbaum-type function is introduced to the control design process. Finally, it is proven that all closed-loop signals are the semiglobal uniformly ultimately bounded, and simulation results demonstrate the effectiveness of the proposed secure control scheme.

In this paper, an event-triggered adaptive control scheme is proposed by backstepping for a class of strict feedback systems with the saturation actuator. The unknown parameters and modelling errors are also considered. By constructing the auxiliary signal system and introducing auxiliary signals into the controller design, the uncertainties caused by unknown saturation effects are compensated for. Based on this auxiliary signal, an event-triggered adaptive control scheme is constructed. The main contributions of this proposed controller can be summarized as follows: (1) A new event-triggered mechanism is proposed by setting a maximum triggering time interval limit. It integrates time triggering in the event triggering mechanism, and avoiding the phenomenon of system performance not being optimized due to control signals not being updated for a long time. It was also proven that this proposed triggering mechanism can prevent the occurrence of Zeno-behavior; (2) The unknown constant parameters in the upper bound function of the unknown nonlinear terms are considered. By estimating these parameters the compensation accuracy for such unknown uncertainties is improved. Finally, simulation results validate the effectiveness of this proposed controller.

Prescribed performance dynamic surface control for trajectory-tracking of unmanned surface vessel with input saturation

This article considers the trajectory tracking control for unmanned surface vessels with unknown time-variant external disturbances and input saturation. The strategy mainly consists of event-triggered reset sub-controller and nonlinear disturbance observer–based compensation sub-controller. To reduce network transmissions, and in the meanwhile, guarantee the desirable closed-loop behavior, the event-triggered reset control is proposed where the reset law and the event-triggered mechanism are designed separately. Both of static and dynamic event-triggered reset controllers are designed. Their corresponding stability is demonstrated using Lyapunov stability theory. Finally, numerical simulation results are presented to demonstrate the effectiveness and robustness of the proposed trajectory tracking control strategy.

Decentralized adaptive neural control of nonlinear interconnected large-scale systems with unknown time delays and input saturation

This paper presents a neural adaptive controller for a class of nonlinear system in the presence of input saturation and arbitrary unknown information by integrating the design framework of neural networks (NNs) with the dynamic surface control (DSC) and backstepping technique. NNs are directly used to deal with unknown nonlinear terms. By introducing a well-defined smooth function and a Nussbaum function, a novel robust adaptive control scheme is developed to guarantee the states of system to be semi-globally uniformly ultimately bounded (SGUUB). Owing to the introduction of DSC, the problem of ‘explosion of complexity’ inherent in the backstepping design is effectively eliminated. Simulation results demonstrate the effectiveness and simplicity of the proposed method.

Distributed adaptive output consensus control of a class of heterogeneous multi-agent systems under switching directed topologies

This article investigates the adaptive neural network (NN) event-triggered containment control problem for a class of nonlinear multiagent systems (MASs). Since the considered nonlinear MASs contain unknown nonlinear dynamics, immeasurable states, and quantized input signals, the NNs are adopted to model unknown agents, and an NN state observer is established by using the intermittent output signal. Subsequently, a novel event-triggered mechanism consisting of both the sensor-to-controller and controller-to-actuator channels are established. By decomposing quantized input signals into the sum of two bounded nonlinear functions and based on the adaptive backstepping control and first-order filter design theories, an adaptive NN event-triggered output-feedback containment control scheme is formulated. It is proved that the controlled system is semi-globally uniformly ultimately bounded (SGUUB) and the followers are within a convex hull formed by the leaders. Finally, a simulation example is given to validate the effectiveness of the presented NN containment control scheme.

This work concentrates on the adaptive resilient dynamic surface controller design problem for uncertain nonlinear lower triangular stochastic cyber-physical systems (CPSs) subject to unknown deception attacks based on a switching threshold event-triggered mechanism. The adverse effect of deception attacks on the stochastic CPSs is that the exact system state variables become unavailable. Furthermore, it should be emphasized that the coexistence of unknown nonlinearities, stochastic perturbations, and unknown sensor and actuator attacks makes it a very difficult and challenging event to implement the control design. To get the desired controller, radial basis function (RBF) neural networks (NNs) are introduced so that the design obstacle caused from the unknown nonlinearities can be easily solved. On this basis, in order to save resources and effectively transmit, the event-triggered control scheme based on a switching threshold strategy is further considered. In the backstepping design process, the dynamic surface control (DSC) technique is presented to deal with the issue of "explosion of complexity." By skillfully designing a new coordinate transformation and the attack compensators, the problem of unknown deception attacks is successfully handled. Under our proposed control scheme, all the closed-loop signals are bounded in probability and the stabilization errors converge to an adjustable neighborhood of the origin in probability. Finally, the simulation results on the double chemical reactor show the validity of the proposed design scheme.

Fully distributed dynamic event-triggered reliable COR of nonlinear MASs with communication link faults and actuator faults for directed graphs

Robust time-varying formation control for underactuated autonomous underwater vehicles with disturbances under input saturation

In this brief, finite-time backstepping sliding mode control technique is investigated to handle with the yaw control of non-affine nonlinear unmanned aerial vehicle (UAV) helicopters with actuator faults and unknown disturbances. Using a taylor series expansion technique, the control input in non-affine nonlinear system is presented as an explicit expression, which is convenient for controller design. Based on it, nonlinear disturbance observer is designed to estimate unknown compound disturbance. Furthermore, finite-time convergence backstepping sliding mode controller is designed to force tracking errors to small bounded sets in finite time, in which dynamic surface control avoids explosion of complexity and the sliding mode variable enhances system robustness. Then, the Lyapunov function is employed to verify the stability of the control system. Finally, the validity of proposed scheme adopted in yaw-channel dynamic model of UAV helicopters under actuator faults is demonstrated via simulation results.