Adaptive neural output-feedback control for nonstrict-feedback time-delay fractional-order systems with output constraints and actuator nonlinearities

Machine learning-based real-time anomaly detection using data pre-processing in the telemetry of server farms

Fast and accurate anomaly detection is critical in telemetry systems because it helps operators take appropriate actions in response to abnormal behaviours. However, recent techniques are accurate but not fast enough to deal with real-time data. There is a need to reduce the anomaly detection time, which motivates us to propose two new algorithms called AnDePeD (Anomaly Detector on Periodic Data) and AnDePed Pro. The novelty of the proposed algorithms lies in exploiting the periodic nature of data in anomaly detection. Our proposed algorithms apply a variational mode decomposition technique to find and extract periodic components from the original data before using Long Short-Term Memory neural networks to detect anomalies in the remainder time series. Furthermore, our methods include advanced techniques to eliminate prediction errors and automatically tune operational parameters. Extensive numerical results show that the proposed algorithms achieve comparable performance in terms of Precision, Recall, F-score, and MCC metrics while outperforming most of the state-of-the-art anomaly detection approaches in terms of initialisation delay and detection delay, which is favourable for practical applications.

Reinforcement learning-based consensus control for MASs with intermittent constraints

In this article, an adaptive optimal consensus control problem is studied for multiagent systems in the strict-feedback structure with intermittent constraints (the constraints appear intermittently). More specifically, by designing a novel switch-like function and an improved coordinate transformation, the constrained states are converted into unconstrained states, and the problem of intermittent constraints is resolved without requiring "feasibility conditions". In addition, using the composite learning algorithm and neural networks to construct the identifier, a simplified identifier-actor-critic-based reinforcement learning strategy is proposed to obtain the approximate optimal controller under the framework of backstepping. Meanwhile, with the aid of the nonlinear dynamic surface control technique, the issue of "explosion of complexity" in backstepping is removed, and the requirements for filter parameters are loosened. Based on Lyapunov stability theory, it is demonstrated that all signals in the closed-loop system are bounded. Finally, two simulation examples are used to verify the effectiveness of the proposed method.

Dynamic learning from adaptive neural control for full-state constrained strict-feedback nonlinear systems

This study focuses on the learning and control issues of strict-feedback systems with full-state constraints. To achieve learning capability under constraints, transformation mapping is utilized to convert the original system with full-state constraints into a quasi-pure-feedback unconstrained system. Utilizing the system transformation technique, only a single neural network (NN) is required to identify the unknown dynamics within the transformed system. Combining the dynamic surface control design, a novel adaptive neural control scheme is developed to ensure that all closed-loop signals are uniformly bounded, and every system state remains within the predefined constraint range. In addition, the precise convergence of NN weights is further transformed into an exponential stability problem for a category of linear time-varying systems under persistent excitation conditions. Subsequently, the converged NN weights are efficiently stored and utilized to create a learning controller to achieve better control performance while abiding by the full-state constraints. The viability of this control strategy is demonstrated via simulations.

Globally Adaptive Neural Network Output-Feedback Control for Uncertain Nonlinear Systems

In this article, a globally neural-network-based adaptive control strategy with flat-zone modification is proposed for a class of uncertain output feedback systems with time-varying bounded disturbances. A high-order continuously differentiable switching function is introduced into the filter dynamics to achieve global compensation for uncertain functions, thus further to ensure that all the closed-loop signals are globally uniformity ultimately bounded (GUUB). It is proven that the output tracking error converges to the prespecified neighborhood of the origin. The effectiveness of the proposed control method is verified by two simulation examples.

A Comprehensive Review of Continuous-/Discontinuous-Time Fractional-Order Multidimensional Neural Networks

The dynamical study of continuous-/discontinuous-time fractional-order neural networks (FONNs) has been thoroughly explored, and several publications have been made available. This study is designed to give an exhaustive review of the dynamical studies of multidimensional FONNs in continuous/discontinuous time, including Hopfield NNs (HNNs), Cohen-Grossberg NNs, and bidirectional associative memory NNs, and similar models are considered in real ( [Formula: see text]), complex ( [Formula: see text]), quaternion ( [Formula: see text]), and octonion ( [Formula: see text]) fields. Since, in practice, delays are unavoidable, theoretical findings from multidimensional FONNs with various types of delays are thoroughly evaluated. Some required and adequate stability and synchronization requirements are also mentioned for fractional-order NNs without delays.

Command-Filtered Neuroadaptive Output-Feedback Control for Stochastic Nonlinear Systems With Input Constraint

In this article, an adaptive neural-network (NN) command-filtered output-feedback control strategy is proposed for a class of stochastic nonlinear systems (SNSs) with the actuator constraint. The problem of "explosion of complexity" existing in the conventional backstepping design procedure for SNSs is successfully resolved based on the command filter technique, and the error compensation mechanism is introduced to remove effectively the influence of filtered error. By using the NNs to identify the unknown nonlinear functions, a neural-network-based state observer is designed to estimate the unmeasurable states of the SNSs. Based on the quartic Lyapunov function, the stability of stochastic closed-loop systems is analyzed. It is proved that all signals of the closed-loop systems are bounded in probability, and the tracking error approaches a small neighborhood of the origin in probability. Finally, the effectiveness of the developed control algorithm in this article is verified by a comparison example.

Fixed-Time Adaptive Neural Network Control for Nonlinear Systems With Input Saturation

This study concentrates on the tracking control problem for nonlinear systems subject to actuator saturation. To improve the performance of the controller, we propose a fixed-time tracking control scheme, in which the upper bound of the convergence time is independent of the initial conditions. In the control scheme, first, a smooth nonlinear function is employed to approximate the saturation function so that the controller can be designed under the framework of backstepping. Then, the effect of input saturation is compensated by introducing an auxiliary system. Furthermore, a fixed-time adaptive neural network control method is given with the help of fixed-time control theory, in which the dynamic order of controllers is reduced to a certain extent since there is only one updating law in the entire control design. Through rigorous theoretical analysis, it is concluded that the proposed control scheme can guarantee that: 1) the output tracking error can converge to a small neighborhood near the origin in a fixed time and 2) all signals in the closed-loop system are bounded. Finally, a numerical example and a practical example based on the single-link manipulator are provided to verify the effectiveness of the proposed method.

FTC Design for Switched Fractional-Order Nonlinear Systems: An Application in a Permanent Magnet Synchronous Motor System

In this article, an adaptive fault-tolerant control (FTC) method and a fractional-order dynamic surface control (DSC) algorithm are jointly proposed to deal with the stabilization problem for a class of multiple-input-multiple-output (MIMO) switched fractional-order nonlinear systems with actuator faults and arbitrary switching. In each MIMO subsystem and each switched subsystem, the neural networks (NNs) are utilized to identify the complicated unknown nonlinearities. A fractional filter DSC technology is adopted to conquer the issue of "explosion of complexity," which may occur when some functions are repeatedly derived. The common Lyapunov function method is used to restrain arbitrary switching problems in the system, and the actuator compensation technique is introduced to tackle the failure faults and bias faults in the actuators. By combining the backstepping DSC design technique and fractional-order stability theory, a novel NN adaptive switching FTC algorithm is proposed. Under the operation of the proposed algorithm, the stability and control performance of the fractional-order systems can be guaranteed. Finally, a simulation example of a permanent magnet synchronous motor (PMSM) system reveals the feasibility and effectiveness of the developed scheme.

Adaptive composite dynamic surface neural control for nonlinear fractional-order systems subject to delayed input

In the article, the adaptive composite dynamic surface neural controller design problem for nonlinear fractional-order systems (NFOSs) subject to delayed input is discussed. A fractional-order auxiliary system is first designed to solve the input-delay problem. By using the developed novel estimation models, the defined prediction errors and the states of error system can decide the weights of radial basis function neural networks (RBFNNs). During the dynamic surface controller design process, the developed fractional-order filters are designed to handle the complexity explosion problem when the classical backstepping control technique is utilized. It is shown that the designed adaptive composite neural controller ensures that all the system state variables are bounded and the tracking error of the considered system finally tends to a small neighborhood of zero. Finally, the results of the simulation explain the feasibility of the developed controller. In addition, the developed controller can also be applied to single input and single output(SISO) nonlinear systems subject to a unitary input function.

Guaranteed cost-based feedback control design for fractional-order neutral systems with input-delayed and nonlinear perturbations

Time delay in actuators is mainly caused by electrical and mechanical components. The effect is visible in the system response particularly when changing in the input command. Therefore, input delay is a problem in the control system design that must be taken into account. Besides, ignoring uncertainty in the dynamic models may compromise the controller design. Thus, how to mitigate the effect of this issue on the system stability and performance is a challenging topic. This article deals with the stabilization of fractional neutral systems considering input-delayed and nonlinear perturbations using the guaranteed cost-based feedback control technique. The main focus is to design the state- and output-feedback controllers to achieve a good performance. The stability criteria are formulated in the Lyapunov sense, which are described in terms of matrix inequalities. The proposed idea is validated using simulations.

Adaptive Neural Network Control for a Class of Fractional-Order Nonstrict-Feedback Nonlinear Systems With Full-State Constraints and Input Saturation

This article addresses an adaptive neural network (NN) constraint control scheme for a class of fractional-order uncertain nonlinear nonstrict-feedback systems with full-state constraints and input saturation. The radial basis function (RBF) NNs are used to deal with the algebraic loop problem from the nonstrict-feedback formation based on the approximation structure. In order to overcome the problem of input saturation nonlinearity, a smooth nonaffine function is applied to approach the saturation function. To arrest the violation of full-state constraints, the barrier Lyapunov function (BLF) is introduced in each step of the backstepping procedure. By using the fractional-order Lyapunov stability theory and the given conditions, it proves that all the states remain in their constraint bounds, the tracking error converges to a bounded compact set containing the origin, and all signals in the closed-loop system are ensured to be bounded. Finally, the effectiveness of the proposed control scheme is verified by two simulation examples.

Neural-Networks-Based Prescribed Tracking for Nonaffine Switched Nonlinear Time-Delay Systems

In this article, by using the neural-networks (NNs) separation and approximation technique, an adaptive scheme is presented to deliver the prescribed tracking performance for a class of unknown nonaffine switched nonlinear time-delay systems. The nonaffine terms are indifferentiable and the controllability condition is not required for each subsystem, which allows the considered tracking problem to not be efficiently solved by the traditional adaptive control algorithms. To solve the problem, NNs are utilized to separate and approximate the nonaffine functions, and then the dynamic surface control and convex combination method are utilized to construct a controller and a switching strategy. In addition, an adaptive law is considered for each subsystem to reduce the conservativeness. Under the designed controller and switching strategy, all the signals of the resulting closed-loop system are bounded, and the tracking performance is achieved with a prescribed level.

Disturbance Observer-Based Fault-Tolerant Control for Robotic Systems With Guaranteed Prescribed Performance

The actuator failure compensation control problem of robotic systems possessing dynamic uncertainties has been investigated in this paper. Control design against partial loss of effectiveness (PLOE) and total loss of effectiveness (TLOE) of the actuator are considered and described, respectively, and a disturbance observer (DO) using neural networks is constructed to attenuate the influence of the unknown disturbance. Regarding the prescribed error bounds as time-varying constraints, the control design method based on barrier Lyapunov function (BLF) is used to strictly guarantee both the steady-state performance and the transient performance. A simulation study on a two-link planar manipulator verifies the effectiveness of the proposed controllers in dealing with the prescribed performance, the system uncertainties, and the unknown actuator failure simultaneously. Implementation on a Baxter robot gives an experimental verification of our controller.

Adaptive Synchronization of Fractional-Order Output-Coupling Neural Networks via Quantized Output Control

This article focuses on the adaptive synchronization for a class of fractional-order coupled neural networks (FCNNs) with output coupling. The model is new for output coupling item in the FCNNs that treat FCNNs with state coupling as its particular case. Novel adaptive output controllers with logarithm quantization are designed to cope with the stability of the fractional-order error systems for the first attempt, which is also an effective way to synchronize fractional-order complex networks. Based on fractional-order Lyapunov functionals and linear matrix inequalities (LMIs) method, sufficient conditions rather than algebraic conditions are built to realize the synchronization of FCNNs with output coupling. A numerical simulation is put forward to substantiate the applicability of our results.

Event-triggered adaptive neural networks control for fractional-order nonstrict-feedback nonlinear systems with unmodeled dynamics and input saturation

The event-triggered adaptive neural networks control is investigated in this paper for a class of fractional-order systems (FOSs) with unmodeled dynamics and input saturation. Firstly, in order to obtain an auxiliary signal and then avoid the state variables of unmodeled dynamics directly appearing in the designed controller, the notion of exponential input-to-state practical stability (ISpS) and some related lemmas for integer-order systems are extended to the ones for FOSs. Then, based on the traditional event-triggered mechanism, we propose a novel adaptive event-triggered mechanism (AETM) in this paper, in which the threshold parameters can be adjusted dynamically according to the tracking performance. Besides, different from the previous works where the derivative of hyperbolic tangent function tanh(⋅) needs to have positive lower bound, a new type of auxiliary signal is introduced in this paper to handle the effect of input saturation and thus this limitation is released. Finally, two numerical examples and some comparisons are provided to illustrate our proposed controllers.

Networked Euler-Lagrangian Systems Synchronization under Time-Varying Communicating Delays

This paper investigates the problem of the task-space synchronization control for networked Euler-Lagrange systems. In the considered systems, there are time-varying delays existing in the networking links and every subsystem contains uncertainties in both kinematics and dynamics. By adding new time-varying coupling gains, the negative effects caused by time-varying delays are eliminated. Moreover, to address the difficulties of parametric calibration, an adaptively synchronous protocol and adaptive laws are designed to online estimate kinematics and dynamic uncertainties. Through a Lyapunov candidate and a Lyapunov-Krasovskii functional, the asymptotic convergences of tracking errors and synchronous errors are rigorously proved. The simulation results demonstrate the proposed protocol guaranteeing the cooperative tracking control of the uncalibrated networked Euler-Lagrange systems in the existence of time-varying delays.