ISA transactions最新文献

This article studied the actor-critic learning scheme for safe leader-follower affine formation maneuver control of networked quadrotors under external disturbances, sensor deception attacks, and injection attacks on the actuators. The followers aim to track formation maneuvers such as scaling, shearing, translation, and rotation determined by the leaders. Motivated by increasing safety and performance requirements during formation maneuvering, the dynamic states of the quadrotors are constrained within prescribed safety constraints. A barrier Lyapunov function is employed to ensure that the safety constraints are not violated. Then, a distributed sliding mode control with actor-critic learning is formulated to facilitate accurate leader-follower affine formation maneuvers and reject malicious cyber-attack signals. The input gains that appear due to the attacks might corrupt the control direction. The Nussbaum gain function is coupled to the controller to tackle this problem. The actor system estimates the uncertain dynamics and malicious attack signals, while the critic network evaluates the control performance through the estimated long-term performance index. The overall stability of the closed-loop system has been proven to be bounded using the Lyapunov stability theorem. Finally, simulation results demonstrate the capability of the presented control method.

This paper discusses the admissible consensus tracking problem for nonlinear singular multi-agent systems under sampled-data event-triggered mechanisms, where the nonlinear dynamics are unknown. In the sampled-data framework, both static and dynamic event-triggered mechanisms are designed based on the observers, and the dynamic event-triggered mechanism has superior performance. Moreover, in order to eliminate the effect of bounded consensus caused by the double estimation problem, this study proposes a distributed adaptive event-triggered control protocol. This protocol is designed to establish sufficient conditions for achieving admissible consensus tracking of singular multi-agent systems. Finally, the theoretical results are verified by three numerical examples.

This paper investigates the event-triggered disturbance rejection control problem for a class of uncertain Lipschitz nonlinear systems with time-varying disturbances. The design process to address this problem involves the construction of a state and disturbance joint observer, along with the design of an event-triggered controller that incorporates a designable minimum inter-event time. To design the joint observer, time-varying disturbances are initially transformed into multiple intermediate variables by employing a linear combination of system states and disturbance derivatives. Subsequently, the recursive observers are constructed to obtain intermediate variable estimations, while the recursive mechanism is utilized to compensate for the extra derivative term arising from disturbance transformation. Then, states and disturbance estimations are used to develop the event-triggered disturbance rejection controller. The event-triggered protocol includes a mandatory resting interval and a dynamic trigger variable that monotonically increases during the resting interval, ensuring a guaranteed minimum inter-event time while also decreasing the trigger frequency. Finally, we provide numerical examples to showcase the effectiveness of the proposed method. Simulation results show that the proposed method is able to reduce the steady-state error by 59.6% and extend the minimum trigger interval by at least 2.44 times compared to existing methods.

Resilient consensus control refers to a system's ability to maintain agreement among its components despite disruptions, failures, or malicious attacks. This paper introduces a resilient control algorithm for a group of robotic manipulators to achieve leader-follower consensus within their workspace, despite actuator faults and deception attacks affecting exchanged signals. The proposed approach leverages adaptive control algorithms to develop the control laws. It begins by introducing an adaptive fault-tolerant tracking control to ensure tracking performance despite model uncertainties, actuator faults, and external disturbances. An adaptive observer is then developed to mitigate the effects of false data injections caused by deception attacks. A key feature of this control framework is its ability to operate without requiring fault and attack detection, thereby improving the system's robustness and applicability. The stability of the network and the convergence of the filtered errors are demonstrated using Lyapunov techniques and the equivalence principle. The proposed control framework is validated through numerical simulations involving a network of four heterogeneous manipulators, with results confirming the approach's effectiveness in enhancing system reliability.

Tool wear monitoring (TWM) is important for machining accuracy and workpiece surface quality, the electric current signal method can't realize the visualization of TWM in variable cutting depth cutting, and the force signal method has difficulties in data acquisition. Combining the characteristics of both methods, the paper proposes a novel TWM method by estimating milling torque and constructing wear coefficient. Firstly, a time-shifted modal decomposition method (TSMD) is proposed to remove the high-frequency interference from the current data, and extract the milling torque component from the feed current. Then, a prediction model driven by current data is established, which estimates instantaneous torque using the residual-enhanced dual embedding channel attention model (RL-EDCA). Finally, the tool wear energy coefficient is proposed based on the characteristics of machine-tool power consumption, its overall change trend is consistent with the trend of tool wear amount as the experimental result, which realizes the visual monitoring of tool wear state in curved surface machining, and provides a new framework for TWM in small batch production.

In this paper, a novel adaptive general type-2 fuzzy model-based control (AGT2-FMBC) is proposed for networked control systems (NCSs) with packet dropouts. A general type-2 fuzzy model (GT2FM) is designed to represent the nonlinear system with uncertainties online, wherein the antecedents of fuzzy rules are defined by general type-2 fuzzy sets and the consequents by Takagi-Sugeno type models. Utilizing the Bernoulli distribution, packet dropouts in the two communication channels are effectively modeled. To mitigate their negative effect, we introduce buffers at the input of both the controller and the nonlinear system. A general type-2 fuzzy controller (GT2FC) is proposed to achieve optimal tracking performance based on the parallel distributed compensation approach. The GT2FC shares the same antecedent parts with the GT2FM, while its consequent parts serve as local inverse model controllers for corresponding consequent parts of the GT2FM. To reduce computation time in the type-reducer, a direct defuzzification method is utilized, introducing two factors to determine the participation of the lower and upper bounds. We introduce a novel algorithm to provide adaptive online laws for the parameters of AGT2-FMBC, ensuring convergence through the Lyapunov stability theorem. The robustness of the proposed controller is demonstrated through its application to three nonlinear NCSs, subject to packet dropouts and uncertainties.

This study addresses the control challenges of distributed prescribed-time maneuvers for underactuated unmanned surface vessels (USVs) operating in affine formation under adverse conditions, including ocean disturbances, unmodeled dynamics, and denial-of-service (DoS) attacks. The research develops a control scheme that enables USVs to perform complex maneuvers such as translation, shearing, rotation, and scaling, despite intermittent communication failures due to periodic DoS attacks. The approach integrates a distributed prescribed-time observer (DPTO) for each vessel to monitor local time-varying desired states, coupled with an adaptive prescribed-time local tracking control (APTLTC) strategy that drives the USV to track the desired states. The effectiveness and robustness of this control solution are validated through theoretical analysis and simulation, demonstrating significant resilience against network disruptions. This study contributes to safer maritime operations by providing a robust control framework for underactuated USVs under cyber-physical threats.

In this paper, a novel extended state observer-based backstepping control scheme is proposed for strict-feedback nonlinear systems with unmeasured states suffering from false data injection (FDI) attacks. An extended state observer is designed to achieve simultaneous online estimation of system states and FDI attacks. A secure output feedback tracking control scheme with an attack compensation method is proposed to reduce the influence of FDI attacks. It is proven that the proposed scheme can guarantee that the closed-loop system is semi-global uniformly ultimately bounded. Moreover, it is shown that the observation errors can be as small as desired with an adjustable parameter and the tracking error can converge to a small neighborhood of the origin. Finally, two simulation examples verify the efficiency of the proposed approach.

This article aims to provide several criteria with sufficient conditions for Nabla discrete fractional-order systems to satisfy a certain H_∞ performance index. Firstly, the instability region is approximated by a series of union sets of fan-shaped domains, and the mathematical expression of each fan-shaped domain is given. Secondly, by using the above approximation method and the generalized Kalman-Yakubovič-Popov lemma, some criteria with linear matrix inequality (LMI) formulations for the boundedness of the H_∞ norm of the transfer function matrix for Nabla discrete FOSs are derived. Thirdly, an LMI-based H_∞ state feedback controller design method is provided by using the projection lemma, which is aimed to stabilize the system and optimize its H_∞ performance index simultaneously. Finally, the effectiveness of the proposed approach is verified by the accurate numerical solutions and system state responses obtained from two simulation examples.

Open-channel irrigation systems (OCIS) are widely recognized as the simplest, most widely adopted, and economically advantageous method of water transportation in agriculture. However, these systems often experience substantial water losses due to seepage and leaks, which are closely correlated with the channel levels. The control mechanisms commonly discussed for OCIS primarily emphasize preserving constant channel levels, leading to constant losses. In order to overcome this limitation, the present study offers a paradigm change in control objectives, thereby facilitating the management of channel levels and flow variability. The new objectives seek to address user water demands, mitigate channel water levels, prevent overflow instances, and limit ecological and infrastructural impacts. A request-based operational approach is utilized in implementing a nonlinear model predictive control (NMPC) technique. The NMPC employs a simple modeling framework that can effectively describe the behavior of OCIS in various operational settings. It also integrates a receding-horizon cost function to optimize water allocation and minimize water levels. The provided design of the control strategy includes a set of feasible conditions that ensure the operability and stability of the controlled system. The efficacy of the proposed control technique has been verified by experimentation on a well-established testbed documented in previous academic papers. This validation process has demonstrated a notable 50% decrease in water waste while simultaneously ensuring sufficient water supply to users.