Maintaining a stable formation among nonholonomic unicycles presents a significant challenge, particularly when subjected to real-world disturbances such as wheel slipping and motor noise. To tackle this issue, we propose a conditional disturbance utilization (CDU) framework that selectively uses favorable disturbances rather than simply rejecting all. First, the disturbance observer (DO) is employed to establish the informational foundation for subsequent conditional utilization decisions. And, the CDU mechanism dynamically decides whether the disturbance aligns with the desired control objective. Moreover, incorporating bearing rigidity achieves global stability with fast convergence. Specifically, the proposed potential function, constructed from bearing information, mitigates disturbance driven collision risks and ensures safety. Finally, simulations and experiments validate the proposed CDU-based formation control. The results demonstrate substantial improvements in responsiveness, and efficiency when compared to disturbance suppression method, highlighting the superiority of the proposed framework.

This paper is dedicated to a dynamic event-triggeredH∞filtering method of fuzzy Markov jump systems via a mismatched quantization scheme. The system outputs are triggered by a dynamic event-triggered mechanism and then quantized via a mismatched quantizer before being sent to the remote filter. The dynamic triggering scheme with a special diagonal matrix structure threshold is built to reduce the network burden. The quantizer is constructed in a multi-channel paradigm with a time-varying mismatch degree. Then, the remote reduce-order filter is designed to be both fuzzy-rule and mode-dependent. By adopting Finsler's Lemma and the vertex separation method, sufficient conditions are derived in terms of form matrix inequalities. At last, the effectiveness of the proposed method is demonstrated by a tunnel diode circuit.

This paper focuses on the issue of limited information based emergency response control for a specific category of underwater cyber-physical systems (UCPSs) confronted with multiple emergencies. The focus is on a typical underwater vehicle system (UVS), whose communication with the control center may be compromised by Denial-of-Service (DoS) attacks. The occurrence of dual DoS attacks leads to significant information loss, intensifying the complexity of decision-making and emergency response during critical situations. Moreover, abrupt ocean current disturbances or faults will also seriously affect the performance of UVSs. Drawing upon distinct DoS attack scenarios, this study introduces a novel emergency response decision mechanism. A limited information-based disturbance observer (DO) is then proposed to effectively handle various unknown disturbances, ensuring favorable disturbance estimation performance. Subsequently, as for different attack channels, two innovative emergency response controllers are respectively proposed, considering the constraints of limited information. Furthermore, distinct stability criteria are derived by using Lyapunov stability and stochastic analysis techniques to guarantee the stabilization of UVSs. Finally, a series of numerical results is presented to illstrate the efficiency of the proposed algorithm under various emergency scenarios.Note to Practitioners—This study presents an novel framework for emergency automatic control and decision-making in UVSs suffering from different emergencies. While most of previous research primarily concentrated on emergency situations in the physical layer, this article delves into both the information layer and the physical layer to tackle decision-making and response challenges within information-constrained environments. Specifically, a limited information-based DO is designed to dynamically model disturbances, such as ocean currents and actuator faults. The DO adapts its structure based on valuable information obtained from emergency monitoring, ensuring accurate disturbance estimation upon successful transmission. The proposed strategy holds significant practical applicability in UVSs for handling emergencies, and further experimental validations are planned to be conducted on actual underwater vehicles.

Achieving precise depth control of autonomous underwater vehicles (AUVs) poses a considerable challenge owing to packet losses and ocean current disturbances. Consequently, there is an urgent necessity to investigate methods for anti-disturbance depth control of AUVs in scenarios that involve packet losses. In contrast to traditional disturbance estimation techniques that depend on complete state information, which rely on complete states, we propose an intermittent information-based disturbance observer, which leverages most recent data packet to compensate for any current data losses. The novel feature of this new anti-disturbance control lies in its consideration of the issue of information intermittency and its dynamic selection of gains with or without packet losses. Through integration of estimated disturbances, a PI recursive controller has been developed to effectively track desired depth of AUVs. In addition, a stability criterion has been established using the Lyapunov stability theory and stochastic analysis techniques, with calculation of the possibility that the Lyapunov function is bounded through Chebyshev’s inequality. Finally, comprehensive simulations have been performed to demonstrate the tracking effectiveness of the proposed algorithm, and its real-world applicability and reliability have also been confirmed through experimental trials.

This article investigates the state estimation and security control problem for discrete-time dual-rate cyber-physical systems (CPSs) under denial-of-service (DoS) attacks. The asynchrony predicament between different signals of dual-rate CPSs, exacerbated by the impact of cyber attacks on the sensor-to-controller channel, substantially increases the complexity of state estimation and control processes. Based on the signal-to-interference-plus-noise ratio and two-channel probability descriptions, an improved sample-and-hold (SAH) estimator is applied to dual-rate CPSs, ensuring favorable state estimates while enduring low-frequency sampling and DoS attacks. Furthermore, to solve the performance degradation problem posed by the SAH algorithm, an alternating-sampling-prediction (ASP)-based estimation method is proposed. At each fast-update moment, the predictor generates virtual outputs. The estimator can reconstruct complete state information by alternately using incomplete sampling data and iterative predictive information. Compared with the SAH method, the proposed ASP-based approach significantly enhances the control performance of dual-rate CPSs. Building on two valid estimation methods, the corresponding security control inputs are designed, guaranteeing both ideal control performance and resilience against attacks. Using convex optimization analysis, both estimator and controller gains are calculated to realize the stochastic stability of closed-loop dual-rate CPSs. Finally, the effectiveness and intercomparisons of the two estimation methods are shown by simulating a satellite yaw-angle control system and a quadrotor landing control experiment.

Tracking accuracy is critical for practical applications. However, due to the presence of various disturbances and intermittent information caused by packet losses or network attacks, ensuring tracking precision and speed becomes extremely challenging. Consequently, this article proposes a novel control architecture for output tracking with intermittent information, which includes an intermittent information-based disturbance observer (IIBDO) and a flexible antidisturbance control strategy based on conditional disturbance utilization (CDU). The novel IIBDO addresses the issue of information intermittency by compensating for lost information using available signals and their trends. Building on this, a disturbance diagnosis condition (DDC) is introduced to assess whether disturbances are beneficial to system performance. Through integration of DDC and disturbance estimation, the CDU-based flexible antidisturbance control is designed, enabling the system to utilize disturbances rather than merely rejecting them. Sufficient conditions are derived to ensure both tracking and antidisturbance performance, and the potential stabilizing effects of disturbances on the system are also analyzed using Lyapunov stability theory. Finally, comprehensive simulations and experiments confirm the effectiveness of the IIBDO, and the improvement in tracking performance brought about by CDU is also verified.

This article investigates the security control problem of cyber-physical systems (CPSs) with unknown disturbance and denial-of-service (DoS) attacks. To address the impact of DoS attacks on state loss and unknown disturbances on the system, we propose a new full-order disturbance observer. By introducing the “sample and hold” method, the system has achieved a certain degree of attack tolerance, and even in the case of unreliable output information caused by network attacks and disturbance, the observer can simultaneously monitor the system state and unknown disturbance. Finally, through the integration of Lyapunov stability theory and convex optimization analysis, the system gains are determined.

This work proposes an adaptive neural network asymptotic tracking control strategy, capable of ensuring flexible fixed-time prescribed transient behavior for switched nonlinear systems with external disturbances and input saturation. Unlike most existing prescribed performance controls with input constraints that ignore the contradiction in fulfilling the input constraint and the output constraint, the proposed strategy realizes a trade-off between them by constructing a modified fixed-time prescribed performance function. The performance function boundary will increase with the error between the actual input and the desired control input, which can sacrifice the user-specified performance while saturation exists and fix it while no saturation exists, lowering the risk of singularity. Together with the designed performance function, a Zeno-free event-triggered controller is built to enforce that the output error converges asymptotically and satisfies the performance function boundaries within a fixed time. Besides, the complexity explosion problem is circumvented through the command-filtered technique. Finally, two case studies are provided to validate the established control strategy.Note to Practitioners—Due to physical restrictions, there are various system input and output constraints in practical engineering, which severely hampers the application of both traditional and cutting-edge control algorithms, particularly in the chemical, boiler, robot, and other sectors. Regrettably, the existing approaches only consider input and output constraints separately and assume that they can be implemented simultaneously, which is unrealistic in many practical systems. Hence, this work focuses on developing a novel adaptive fixed-time prescribed performance control strategy by introducing a modification signal, which can not only comprehensively balance input and output constraints but also achieve the asymptotic tracking of the system output. Meanwhile, an event-triggered mechanism is introduced to deal with the problem of limited communication bandwidth.

This article devises a new dynamic double event-triggered anti-disturbance tracking control scheme for a 2-degree of freedom (DOF) laboratory helicopter subject to external time-varying disturbances and load fluctuation by using the generalized proportional-integral observer technique. The helicopter system is separated into two subsystems in the proposed control method, i.e., the pitch subsystem and the yaw subsystem. Each subsystem includes a discrete-time dynamic double event-triggering mechanism (DDETM), and the control laws of the two subsystems are independent of each other. There are two triggering conditions in the designed double triggering mechanism: one is designed based on the system states and the other is based on the lumped disturbance estimation. These two triggering conditions form a competitive relationship such that the controller updates the control signal as long as one of the triggering conditions is satisfied. Theoretical analysis is provided for achieving the better communication and control performance of the proposed DDETM-based robust control method. Through rigorous stability analysis, it is proved that the closed-loop hybrid system is globally ultimately bounded. At last, numerical simulations show that the suggested control strategy not only reduces the event-triggering number, but also improves the initial dynamic performance of the system.

The present study proposes an adaptive fuzzy tracking control strategy for switched nonlinear systems, capable of effectively addressing false data injection (FDI) attacks and input saturation, while achieving flexible prescribed performance control as well as semi-global uniform ultimate boundness for the resultant system. Compared to the previous work, the provided control strategy exhibits two notable strengths: 1) it introduces a novel modified fixed-time pregiven performance function to effectively balance input saturation and output constraint and 2) the detrimental impacts resulting from FDI attacks are successfully mitigated by implementing the fuzzy logic systems approximation technique in the backstepping procedure. A set of switching fuzzy observers are established to estimate the unobservable states while a first-order differential filter is utilized to handle the complexity explosion problem. Finally, the mass-spring-damper system is given to substantiate the developed approach.

Urban rail transit (URT) is vulnerable to natural disasters and social emergencies including fire, storm and epidemic (such as COVID-19), and real-time origin-destination (OD) flow prediction provides URT operators with important information to ensure the safety of URT system. However, hindered by the high dimensionality of OD flow and the lack of supportive information reflecting the real-time passenger flow changes, study in this area is at the beginning stage. A novel model consisting of two stages is proposed for OD flow prediction. The first stage predicts the inflows of all stations by Long Short-Term Memory (LSTM) in real time, where the dimension is reduced compared with predicting OD flows directly. In the second stage, the notion of separation rate, namely, the proportion of inbound passengers bounding for another station, is estimated. Finally, The OD flow is predicted by multiplying the inflow and separation rate. Experiments based on Hangzhou Metro dataset show the proposed model outperforms the contrast model in weighted mean average error (WMAE) and weighted mean square error (WMSE). Results also suggest that the proposed prediction model performs better on weekdays than on weekends, and with greater accuracy on larger OD flows.

This brief is concerned with iterative learning control (ILC) of constrained multi-input multi-output (MIMO) nonlinear systems under the state alignment condition with varying trial lengths. A modified reference trajectory is constructed to meet the alignment condition by adjusting the reference trajectory to be spatially closed. Resorting to the barrier composite energy function (BCEF) approach, an adaptive ILC scheme is built to guarantee the bounded convergence of the resultant closed-loop system. Illustrative examples are presented to verify the validity of the proposed iteration scheme.

This article is dedicated to data-driven control of networked nonlinear systems with event-triggered output. An improved extended state observer is constructed to estimate unknown disturbances. An output estimator is built on the triggered output and the estimated output. Consequently, triggering conditions for single-input single-output and multiple-inputs and multiple-outputs systems are individually proposed by integrating the estimated disturbances, the true and the estimated tracking errors. Sufficient conditions are established to guarantee that the resultant tracking error systems are uniformly ultimately bounded. The proposed strategies are verified by illustrative numerical examples.

This article addresses the fixed-time control problem for high-order nonlinear systems with unknown nonlinear functions and asymmetric output constraint. First, a continuous state-feedback controller is constructed by revamping the adding a power integrator technique (API), which guarantees that the system output does not exceed the pregiven asymmetric output constraint boundary. Second, with the barrier Lyapunov function (BLF) method and the backstepping technique, a fixed-time stability criterion is constructed. It is rigorously proven that the closed-loop systems are fixed-time stable by the Lyapunov theory. The proposed BLF can not only deal with the symmetric output constraint but also cover the traditional logarithm-type BLF as a special case. Finally, a practical application example of the boiler–turbine unit system is offered to certify the availability of the proposed control algorithm.

This article discusses the issue of disturbance rejection and anti-windup control for a class of complex systems with both saturating actuators and diverse types of disturbances. At the input port, to better characterize those irregular disturbances, exogenous dynamic neural network (DNN) models with adjustable weight parameters are first introduced. A novel disturbance observer-based adaptive control (DOBAC) technique is then established, which realizes the dynamic monitoring for the unknown input disturbance. To handle the system disturbance with a bounded norm, the attenuation performance is concurrently analyzed by optimizing the$L_{1}$gain index. Moreover, the PI-type dynamic tracking controller is proposed by integrating the polytopic description of the saturating input with the estimation of the input disturbance. The favorable stability, tracking, and robustness performances of the augmented system are achieved within a given domain of attraction by employing the convex optimization theory. Finally, using DNN-based modeling for three kinds of different irregular disturbances, simulation studies for an A4D aircraft model are conducted to substantiate the superiority of the designed algorithm.

In this brief, sufficient conditions are proposed for the existence of the compact sets in the neural network controls. First, we point out that the existence of the compact set in a classical neural network control scheme is unsolved and its result is incomplete. Next, as a simple case, we derive the sufficient condition of the existence of the compact set for the neural network control of first-order systems. Finally, we propose the sufficient condition of the existence of the compact set for the neural-network-based backstepping control of high-order nonlinear systems. The theoretic result is illustrated through a simulation example.

In this paper, adaptive neural dynamic surface control (DSC) is developed using radial basis function neural networks (NNs) for a class of pure-feedback nonlinear systems with full state constraints and dynamic uncertainties. Based on a one-to-one nonlinear mapping, the pure-feedback system with full state constraints is transformed into a novel pure-feedback system without state constraints. The dynamic uncertainties are dealt with using a dynamic signal. Using modified DSC and mean value theorem as well as Nussbaum function, two adaptive NN control schemes are proposed based on the transformed system. The designed control strategy removes the conditions that the upper bound of the control gain is known, and the lower bounds and upper bounds of the virtual control coefficients are known. It is shown that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded, and the full state constraints are not violated. Two numerical examples are provided to illustrate the effectiveness of the proposed approach.

This paper presents a novel non-Gaussian stochastic control framework for the problem of disturbance estimation and rejection by combining fuzzy identification technology with disturbance observer design. First, fuzzy logic models are used to approximate the output probability density functions (PDFs) of non-Gaussian processes such that the task of PDF shape control can be reduced to a fuzzy weight dynamics modeling and control problem. Next, Takagi-Sugeno fuzzy models with multiple disturbances are employed to describe the nonlinear relations between fuzzy weight dynamics and the control input, in which a novel disturbance-observer-based PI-type fuzzy feedback controller is designed to ensure the system stability and convergence of the tracking error to zero. Meanwhile, the disturbance estimation and attenuation performance as well as the state constrained requirement can also be guaranteed. Moreover, the novel composite observer is constructed by augmenting the disturbance estimation into the full-state estimation. The satisfactory tracking performance and full-state observation effect can be achieved by the designed optimization algorithm. Finally, simulations for paper-making process are given to show the efficiency of the proposed approach.

This paper presents a new type of control framework for dynamical stochastic systems, called statistic tracking control (STC). The system considered is general and non-Gaussian and the tracking objective is the statistical information of a given target probability density function (pdf), rather than a deterministic signal. The control aims at making the statistical information of the output pdfs to follow those of a target pdf. For such a control framework, a variable structure adaptive tracking control strategy is first established using two-step neural network models. Following the B-spline neural network approximation to the integrated performance function, the concerned problem is transferred into the tracking of given weights. The dynamic neural network (DNN) is employed to identify the unknown nonlinear dynamics between the control input and the weights related to the integrated function. To achieve the required control objective, an adaptive controller based on the proposed DNN is developed so as to track a reference trajectory. Stability analysis for both the identification and tracking errors is developed via the use of Lyapunov stability criterion. Simulations are given to demonstrate the efficiency of the proposed approach.

A new design scheme of direct adaptive neural network controller for a class of nonlinear systems with unknown function control gain is proposed in this paper. The design is based on the principle of sliding mode control and the approximation capability of multilayer neural networks (MNNs). By adopting the adaptive compensation term of the upper bound function of the sum of residual and approximation error, the closed-loop control system is shown to be globally stable, with tracking error converging to zero. Simulation results demonstrate the effectiveness of the approach.