Neural-Network-Based Event-Triggered Adaptive Control of Nonaffine Nonlinear Multiagent Systems With Dynamic Uncertainties.

This paper is concerned with adaptive neural control of nonlinear strict-feedback systems with nonlinear uncertainties, unmodeled dynamics, and dynamic disturbances. To overcome the difficulty from the unmodeled dynamics, a dynamic signal is introduced. Radical basis function (RBF) neural networks are employed to model the packaged unknown nonlinearities, and then an adaptive neural control approach is developed by using backstepping technique. The proposed controller guarantees semiglobal boundedness of all the signals in the closed-loop systems. A simulation example is given to show the effectiveness of the presented control scheme.

Robust adaptive neural tracking control for a class of nonlinear systems with unmodeled dynamics using disturbance observer

This paper addresses the issue of fixed-time neural adaptive event-triggered control for nonstrict-feedback nonlinear systems with full-state constraints, input dead-zone, and saturation. Radial basis function neural networks (RBFNNs) are used to identify the unknown nonlinearities. The paper considers both input saturation and dead-zone effects, approximating these non-smooth nonlinearities with a non-affine smooth function and then transforming them into an affine form using the mean value theorem. The approach integrates backstepping recursive design with a varying threshold event-triggered condition to create an event-triggered neural adaptive fixed-time control algorithm that employs barrier Lyapunov functions (BLFs) and RBFNNs. By applying the fixed-time stability criterion, the proposed controller ensures that the tracking error converges to a smaller region within a fixed time and that all variables in the closed-loop system remain bounded. Finally, two simulation examples are provided to demonstrate the effectiveness of the proposed method.

Disturbance observer based adaptive neural control of uncertain MIMO nonlinear systems with unmodeled dynamics

In this study, an adaptive neural backstepping control scheme is proposed for a class of strict‐feedback non‐linear systems with unmodelled dynamics, dynamic disturbances and input saturation. To solve the difficulties from the unmodelled dynamics and input saturation, a dynamic signal and smooth function in non‐affine structure subject to the control input signal are introduced, respectively. Radial basis function (RBF) neural networks are used to approximate the packaged unknown non‐linearities, and an adaptive neural control approach is developed via backstepping, which guarantees that all the signals in the closed‐loop system are semi‐globally uniformly ultimately bounded in mean square. The main contributions of this note lie in that a control strategy is provided for a class of strict‐feedback non‐linear systems with unmodelled dynamics uncertainties and input saturation, and the proposed control scheme does not require any information of the bound of input saturation non‐linearity. Simulation results are used to show the effectiveness of the proposed control scheme.

In this paper, an event-triggered adaptive decentralised control strategy for a class of switched interconnected nonlinear systems is presented, which considers full-state constraints and unmodeled dynamics, simultaneously. In the controller design process, the approximation capability of radical basis function neural networks (RBF NNs) is used to estimate the unknown functions of the system. The interference caused by unmodeled dynamics is overcome by introducing a dynamic signal. In addition, the barrier Lyapunov function (BLF) is constructed for each subsystem to dispose the influence of state constraints. An adaptive control scheme with event-triggered mechanism is proposed to reduce communication burden. It is shown that the proposed event-triggered controller and an adaptive neural decentralised control strategy are designed such that all the signals in the closed-loop system are guaranteed to be bounded, the tracking errors of the system converge to a small neighbourhood of the origin and the full state constraints are not violated. Finally, a simulation result shows the effectiveness of the developed approach.

Robust adaptive distributed dynamic surface consensus tracking control for nonlinear multi-agent systems with dynamic uncertainties

In this paper, the issue of adaptive neural event-triggered control (ETC) is studied for uncertain block-structure multi-input multi-output (MIMO) constrained non-affine nonlinear systems with unmodeled dynamics. A dynamic signal produced by the auxiliary system based on the property of unmodeled dynamics is employed to solve the dynamical disturbances. The unknown continuous function obtained at each step of recursion is estimated by using radial basis function neural networks (RBFNNs). Utilizing logarithmic function as an invertible mapping, the uncertain constrained MIMO non-affine system is changed into a novel unconstrained block-structure MIMO nonaffine system. Using improved dynamic surface control (DSC) strategy, adaptive event-triggered control scheme is developed for the transformed non-affine system based on relative threshold mechanism. According to the Lyapunov method, all the signals in the closed-loop system are shown to be semi-globally uniformly ultimately bounded (SGUUB). Output constraint requirements are not triggered, and Zeno behavior is avoided. A constrained pure-feedback system and a kind of 2-DOF flexible manipulator system are used to illustrate the theoretical findings.

Adaptive neural tracking control of pure-feedback nonlinear systems with unknown gain signs and unmodeled dynamics

In this paper, the problem of adaptive neural control is considered for a class of strict-feedback nonlinear systems with unmodeled dynamics, dynamic disturbances and unknown input saturation. During the controller design, radial basis functions(RBF) neural networks are applied to model the unknown nonlinearities, and an adaptive neural control scheme is developed via backstepping, which guarantees that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded in mean square. A simulation example is provided to show the effectiveness of the proposed control scheme.

An event-triggered neural adaptive cooperative control is proposed for the towing system (TS) with model parameter uncertainties and unknown disturbances. Different from ordinary multi-vessel formation control, the tugs and unactuated offshore platform in the TS are connected together by towlines, and the resultant tension of the towlines serves as the actual drag force for the platform. Initially, based on the radial basis function neural network (RBFNN), an adaptive RBFNN is designed to compensate unknown disturbances and model parameter uncertainties of the TS, and we use minimal learning parameter (MLP) algorithm to reduce the online learning parameters of adaptive RBFNN. Combined with dynamic surface technology and event-triggered control (ETC) mechanism, an event-triggered neural adaptive virtual controller is designed to obtain the desired drag force of the platform. According to the quadratic programming algorithm, the desired drag force is allocated as the desired tensions of towlines. Subsequently, the desired towline length and the desired position information of the tugs are obtained sequentially through the towline model and the position relationship between the tugs and the platform. Then, according to the desired positions of tugs, an event-triggered neural adaptive distributed cooperative controller is designed for achieving the multi-tug towing of the offshore platform. The ETC mechanism is introduced to reduce the communication burden within the TS and the execution frequency of the tugs’ thrusters. Finally, the stability of the closed-loop system is proven using the Lyapunov theory, and the ETC mechanism proves that no Zeno behavior occurs. The effectiveness of the ETC mechanism and the MLP-based adaptive RBFNN on the controllers of TS is verified through simulations and comparison analysis.

Adaptive neural dynamic surface control of MIMO pure-feedback nonlinear systems with output constraints

This paper investigates the event-triggered-based adaptive bipartite finite-time tracking control problem of nonlinear nonstrict-feedback coopetition multi-agent systems (MASs) with the time-varying disturbances. First, the major design difficulties generated by the entirely unknown nonlinear functions containing all states are solved by utilizing the approximation property of radial basis function neural networks (RBF NNs) and the structural feature of Gaussian functions. Then in the backstepping procedure, the issue of “explosion of complexity” is handled by combining the adaptive neural approach and the command filter technique, which simplified the complexity of the controllers for all the agents. Meanwhile, the novel compensation signals are designed, which skillfully eliminate the error influence caused by the filters. Moreover, to save the communication resources, the relative threshold event-triggered control (ETC) scheme is presented for the designed controllers, and there is no Zeno phenomenon. Overall, it is shown that the new proposed control approach drives the tracking errors to the desired neighborhood of the origin in an almost fast finite time, and all the signals in the closed-loop systems are almost fast finite-time bounded. Finally, the numerical and practical simulation results are both given to show the validity of the obtained design method <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper presents the event-triggered adaptive bipartite finite-time tracking control scheme for the nonlinear nonstrict-feedback coopetition MASs with the time-varying disturbances, which can be used to model most practical systems such as sensor network, submarine underwater robot and space satellites. Although the bipartite control problem has been studied a lot, the bipartite finite-time tracking control problem for nonlinear nonstrict-feedback coopetition MASs with the time-varying disturbances is still open and challenging. The proposed approach guarantees that the tracking errors can converge to the desired neighborhood of the origin in an almost fast finite time. In addition, the ETC strategy is proposed to address the limited power consumption. A remarkable point for this paper is that the obtained results are demonstrated by the simulation examples, which shows the validity of the proposed approach.

In this paper, an event-triggered adaptive neural control issue is addressed for a class of switched unknown strict-feedback nonlinear system under constraint output. To deal with the unknown nonlinear system, the radial basis function neural networks (RBFNNs) are employed to approximate the unknown nonlinear functions. Under adaptive backstepping technique, associated with barrier Lyapunov function method, an event-triggered controller is designed to ensure that the system's output signal follows a given reference signal. meanwhile, the system output signal meets the asymmetric constraint requirement. The proposed control strategy is guaranteed to solve the presented problem. Finally, a simulation example is presented to demonstrate the efficacy of the proposed scheme.

SummaryStochastic adaptive dynamic surface control is presented for a class of uncertain multiple‐input–multiple‐output (MIMO) nonlinear systems with unmodeled dynamics and full state constraints in this paper. The controller is constructed by combining the dynamic surface control with radial basis function neural networks for the MIMO stochastic nonlinear systems. The nonlinear mapping is applied to guarantee the state constraints being not violated. The unmodeled dynamics is disposed through introducing an available dynamic signal. It is proved that all signals in the closed‐loop system are bounded in probability and the error signals are semiglobally uniformly ultimately bounded in mean square or the sense of four‐moment and the state constraints are confirmed in probability. Simulation results are offered to further illustrate the effectiveness of the control scheme.