In dynamic systems, complex time-varying noise characteristics impose challenges for multi-sensor data fusion. This paper proposes a deep learning-driven noise-adaptive filtering approach for multi-sensor data fusion. A parallel gated recurrent unit (GRU) and one-dimensional convolutional neural network (1D-CNN) architecture jointly extracts temporal dependencies and local spatial patterns from raw measurement sequences to estimate sensor noise variances. Each sensor then uses these variance estimates for more accurate adaptive local filtering. Based on these refined local estimates, the fusion center employs an event-triggered scheme to drastically cut communications while preserving fusion accuracy. Comprehensive experiments in a high-fidelity Unreal Engine–AirSim quadrotor unmanned aerial vehicle (UAV) simulation under nominal, drift, abrupt, and extreme noise modes confirm that our pipeline achieves superior global state estimation while dramatically reducing data transmission burden.

Ground-to-air communication is a critical technology for establishing an Internet of Things (IoT) network system, especially in emergency situations. We are investigating the trajectory planning problem of a data collection IoT network assisted by an unmanned aerial vehicle (UAV). This article aims to solve the data collection Dubins traveling salesman problem (DCDTSP) for UAVs in a three-dimensional and complex obstacle environment. To optimize the paths for UAVs in data collection from terminals to UAVs, a novel releasing-collecting-recycling (RCR) framework has been established for heterogeneous multi-UAVs. In the UAV release step, we propose a multi-height hierarchical target clustering (MHTC) algorithm to enhance the efficiency of multi-target clustering. In the data collection step, a bundling ant colony system (BACS) is developed to minimize the length of the obstacle avoidance path while still meeting the communication throughput constraint. Meanwhile, the dynamic adaptive window probabilistic roadmap (DAWPRM) algorithm has been enhanced to address the obstacle avoidance distance in BACS. In the UAV recycling step, we propose a time synchronous Dubins recycling strategy to plan the simultaneous arrival trajectory for multiple UAVs with a constrained turning radius. The results of simulation experiments showed that the proposed RCR framework is optimal for finding Pareto solutions for DCDTSP.

This paper is focused on the event-based secure control issue for cyber-physical systems (CPSs) under aperiodic denial-of-service (DoS) attacks. Malicious DoS attacks disrupt the communication between the controller and the actuator. The finite attack resources of malicious attackers are taken into consideration, and the DoS attacks are characterized using an aperiodic model. In contrast to prior results, the present study tackles the issue of secure controller design by considering the attributes of the DoS attack, instead of employing a switched system approach to address the aforementioned concerns. More specifically, under aperiodic DoS attacks, sufficient criteria are established to guarantee that the closed-loop CPSs can achieve bounded stability. Then, within a time-varying attack period, the relationship between the attack active interval and the attack silent interval is derived. Without satisfying the derived conditions, the system’s stability will deteriorate. Moreover, an event-based secure control scheme under aperiodic DoS attacks is designed. To verify the efficacy of the derived theory, a wheeled mobile robot system under aperiodic DoS attacks is illustrated. Note to Practitioners—CPSs have been widely utilized in various domains, such as aerospace and intelligent transportation. However, the openness of networks provides attackers with numerous opportunities for malicious assaults, consequently leading to a degradation in system performance. Consequently, researching the security issues of CPSs under malicious attacks is of utmost urgency. This paper focuses on the issue of event-triggered secure control for CPSs in the presence of energy-constrained aperiodic DoS attacks. The event-triggered communication mechanism is introduced to reduce the computational burden. The criteria for ensuring the bounded stability of CPSs under aperiodic DoS attacks are proposed. The relationship between the attack active interval and the attack silent interval is derived, which is incorporated into the proposed criteria. A wheeled mobile robot system is given to validate the effectiveness of the proposed method. In the future, an active defense control method will be proposed to counter malicious attacks.

This article addresses the issue of security in cyber–physical systems (CPSs) in the context of malicious actuator false data injection (FDI) attacks. Building on a stochastic physical dynamics model, the proposed approach distinguishes itself by employing a moving target defense (MTD) strategy to enable proactive protection, which is an aspect rarely addressed in existing related works. The system model is formulated as a family of controllable submodels based on controllability. These controllable submodels are regarded as moving targets. A residual-based attack detector is introduced to justify whether an attack occurs or not. When an alarm is triggered, the current running controllable submodel is switched to another controllable one. Furthermore, an MTD-based security controller is devised to proactively mitigate actuator attacks. Sufficient conditions for the design of security control gains are formulated, using which the CPSs can preserve the mean-square exponential stability with the desired disturbance rejection level. Finally, the effectiveness of the proposed control strategy is demonstrated through a comparative simulation based on a practical physical system.

This article is focused on security analysis and control problems of cyber–physical systems (CPSs) under Denial-of-Service (DoS) attacks, which jam the controller-actuator channel. Considering the limited attack energy of malicious adversaries, DoS attacks are described by a periodic model. Different from existing results, both the security analysis and secure controller design problems are addressed based on the characteristics of DoS attack model rather than utilizing the switching system theory to solve the aforementioned problems. First, sufficient conditions are derived to ensure that the resultant closed-loop CPS under DoS attacks can preserve the exponential stability, and the critical value of the attack period is derived, below which the stability is deteriorated. Second, the relation between our proposed conditions and reinforcement learning-based control is established, based on which the security of reinforcement learning-based control can be evaluated effectively. Finally, both DoS attacks and external disturbances are considered in a unified framework, and sufficient conditions are proposed to evaluate the security of the closed-loop CPSs and design a secure controller. Finally, a mobile robot is adopted to validate the efficacy of the proposed methods.

In this article, a novel adaptive fixed-time fuzzy control algorithm is designed for uncertain Euler–Lagrange (EL) systems with actuator control input saturation. In contrast to existing algorithms, this article explores a faster fixed-time backstepping control algorithm. It enables the system to achieve fixed-time convergence with a faster convergence rate and obtain a smaller upper bound of the convergence time. To address the problem of actuator control input saturation, a novel fixed-time auxiliary system is constructed, involving coordinate transformation of the system's error variables to mitigate the effects of saturation. In response to the unknown dynamics (including model uncertainty, external disturbance, etc.) of the EL system, this article designs an adaptive interval type-2 fuzzy neural network for estimation and compensation. Stability analysis confirms that the tracking error can achieve faster fixed-time convergence. Simulation and experimental results demonstrate that the proposed control algorithm can enhance dynamic and steady-state tracking control performance.

This article addresses the secure control issue for cyber-physical systems (CPSs) under aperiodic denial-of-service (DoS) attacks. Malicious DoS attacks disrupt the communication between the controller and the actuator. The finite attack resources of malevolent attackers are taken into consideration, and the DoS attacks are characterized using an aperiodic model. In contrast to prior results, the present study tackles the issues of security analysis and secure controller design by considering the attributes of the DoS attacks, instead of employing a switched system approach to address the aforementioned concerns. First, under aperiodic DoS attacks, sufficient criteria are established to guarantee that the closed-loop CPSs can attain asymptotical stability. Second, within a time-varying attack period, the relationship between the attack active interval and the attack silent interval is derived, if this relation is not satisfied, the stability of the system will deteriorate. Finally, a unified framework is developed to address the external disturbances and aperiodic DoS attacks. Sufficient criteria are introduced for evaluating the security of CPSs, and a corresponding secure control scheme is also designed. To verify the efficacy of the derived theory, a wheeled mobile robot system under aperiodic DoS attacks is illustrated.

The problem of learning-based control for robots has been extensively studied, whereas the security issue under malicious adversaries has not been paid much attention to. Malicious adversaries can invade intelligent devices and communication networks used in robots, causing incidents, achieving illegal objectives, and even injuring people. This article first investigates the problems of optimal false data injection attack scheduling and countermeasure design for car-like robots in the framework of deep reinforcement learning. Using a state-of-the-art deep reinforcement learning approach, an optimal false data injection attack scheme is proposed to deteriorate the tracking performance of a robot, guaranteeing the tradeoff between the attack efficiency and the limited attack energy. Then, an optimal tracking control strategy is learned to mitigate attacks and recover the tracking performance. More importantly, a theoretical stability guarantee of a robot using the learning-based secure control scheme is achieved. Both simulated and real-world experiments are conducted to show the effectiveness of the proposed schemes.

This article investigates the problem of optimal tracking control for cyber-physical systems (CPSs) when the cyber realm is attacked by Denial-of-Service (DoS) attacks which can prevent the control signal transmitting to the actuator. Attention is focused on how to design the optimal tracking control scheme without using the system dynamics and analyze the impact of DoS attacks on tracking performance. First, a Riccati equation for the augmented system, including the system model and the reference model is derived under the framework of dynamic programming. The existence and uniqueness of its solution are proved. Second, the impact of the successful DoS attack probability on tracking performance is analyzed. A critical value of the probability is given, beyond which the solution to the Riccati equation cannot converge. The tracking controller cannot be designed. Third, reinforcement learning is introduced to design the optimal tracking control schemes, in which the system dynamics are not necessary to be known. Finally, both a dc motor and an F16 aircraft are used to evaluate the proposed control schemes in this article.

This paper investigates the problem of the resilient control for cyber-physical systems (CPSs) in the presence of malicious sensor denial-of-service (DoS) attacks, which result in the loss of state information. The concepts of DoS frequency and DoS duration are introduced to describe the DoS attacks. According to the attack situation, that is, whether the attack is successfully implemented or not, the original physical system is rewritten as a switched version. A resilient sliding mode control scheme is designed to guarantee that the physical process is exponentially stable, which is a foundation of the main results. Then, a zero-sum game is employed to establish an effective mixed defense mechanism. Furthermore, a defense-based resilient sliding mode control scheme is proposed and the desired control performance is achieved. Compared with the existing results, the differences mainly lie in two aspects, that is, one where a switched model is obtained, based on which the average dwell-time like approach is utilized to derive the resilient control scheme, and the other where the zero-sum game in employed to make the attacks satisfy the concepts of DoS frequency and DoS duration. Finally, simulation results are given to demonstrate the effectiveness of the proposed resilient control approach.

This paper studies the problem of adaptive eventtriggered dynamic output feedback fuzzy control for nonlinear networked control systems. Two crucial factors, packet dropouts and actuator failure, are taken into consideration simultaneously. Takagi-Sugeno fuzzy model is introduced to describe considered systems. The Bernoulli random distribution process is employed to depict the phenomenon of data missing. The actuator failure model is adopted to depict actuator failure. An innovative adaptive event-triggered strategy is built to save computational resource. In the light of Lyapunov stability theory, a fuzzy dynamic output feedback controller is designed to guarantee the stochastic stability and H∞performance for considered systems. Finally, simulation results are provided to demonstrate the usefulness of the proposed control strategy.

In this paper, the adaptive neural network (NN) tracking control problem is addressed for robot manipulators subject to dead-zone input. The control objective is to design an adaptive NN controller to guarantee the stability of the systems and obtain good performance. Different from the existing results, which used NN to approximate the nonlinearities directly, NNs are employed to identify the originally designed virtual control signals with unknown nonlinear items in this paper. Moreover, a sequence of virtual control signals and real controller are designed. The adaptive backstepping control method and Lyapunov stability theory are used to prove the proposed controller can ensure all the signals in the systems are semiglobally uniformly ultimately bounded, and the output of the systems can track the reference signal closely. Finally, the proposed adaptive control strategy is applied to the Puma 560 robot manipulator to demonstrate its effectiveness.

This paper is concerned with the problem of secure state reconstruction for cyber-physical systems (CPSs). CPSs are more vulnerable to the cyber world yet to attackers, who can attack any sensor of the considered systems and modify values of attacked sensors to be arbitrary ones. In the design process, both malicious attacks on sensors and unknown input are taken into consideration. First, a linear discrete-time state-space model is utilized to describe such systems, and then a sparse vector is adopted to model attacks. By collecting sensor measurements and using an iterative approach, a new model in descriptor form is obtained, which paves the way for estimating system states under an unknown input situation. Second, the problem of secure state estimation is transformed into an optimal version. A novel sliding-mode observer is proposed to estimate system states from collected sensor measurements corrupted by malicious attacks. In order to guarantee the estimations to be sparse, a projection operator is designed. Third, a projected sliding-mode observer-based estimation algorithm is developed to reconstruct system states, where an event-triggered scheme is integrated to save limited computational resource. In addition to propose such an algorithm, the effectiveness of both projection operator and sliding-mode observer is analyzed. Furthermore, the convergence of the proposed secure estimation algorithm is proved. Finally, some simulation results are given to demonstrate the effectiveness of the proposed algorithm.

This paper studies an output-based adaptive fault-tolerant control problem for nonlinear systems with nonstrict-feedback form. Neural networks are utilized to identify the unknown nonlinear characteristics in the system. An observer and a general fault model are constructed to estimate the unavailable states and describe the fault, respectively. Adaptive parameters are constructed to overcome the difficulties in the design process for nonstrict-feedback systems. Meanwhile, dynamic surface control technique is introduced to avoid the problem of "explosion of complexity". Furthermore, based on adaptive backstepping control method, an output-based adaptive neural tracking control strategy is developed for the considered system against actuator fault, which can ensure that all the signals in the resulting closed-loop system are bounded, and the system output signal can be regulated to follow the response of the given reference signal with a small error. Finally, the simulation results are provided to validate the effectiveness of the control strategy proposed in this paper.

This paper studies the observer-based tracking control problem for discrete-time nonlinear networked control systems with parameter uncertainties and unmeasurable state variables. A network-induced constraint, i.e., the intermittent measurement loss, is considered in the controller design. The uncertain nonlinear system is described by an interval type-2 (IT2) fuzzy Takagi-Sugeno model, in which the lower and the upper membership functions with corresponding coefficients are used to capture and express uncertainties existing in the system. A premise-variables-independent IT2 fuzzy observer is constructed to estimate the unmeasurable state variables, and then a novel IT2 fuzzy tracking controller is designed. Furthermore, sufficient criteria are established to guarantee the resulting closed-loop system to be stochastically stable. Finally, two examples are provided to show the effectiveness of the proposed approach.

This paper studies the problem of reliable filter problem for a category of sensor networks in the framework of interval type-2 fuzzy model. In the filter design, the random link failures, which are caused possibly by missing measurements as well as by probabilistic communication failures, are considered to illustrate more realistic dynamical behaviors of sensor networks. In order to tackle the uncertainties existing in systems, interval type-2 (IT2) fuzzy approach is utilized to establish the model, wherein upper and lower membership functions together with weighting coefficients are employed to express the uncertainties. An distributed IT2 fuzzy filter model is constructed to estimate system states. Using the Lyapunov theory, sufficient conditions have been given to ensure that the filtering error system is mean-square asymptotically stable and satisfies the predefined average $ \mathcal {H}_{\infty }$ performance level. Moreover, the criteria to design the filter parameters are developed through using cone complementary linearization approach. Finally, a practical example is given to validate the proposed method.

This paper studies the problem of adaptive fuzzy control for a category of single-input single-output nonlinear networked control systems with network-induced delay and data loss based on adaptive backstepping control approach. Fuzzy logic systems are used to approximate the unknown nonlinear characteristics existing in the system, while Pade approximation is introduced to handle network-induced delay. Data loss occurs intermittently and stochastically in the data transmitting process, which is regarded as the delay in the controller design. In the framework of adaptive fuzzy backstepping technique, a novel state-feedback adaptive controller is constructed to ensure all signals in the resulting closed-loop system to be bounded and the state variables can be regulated to the origin. Finally, two examples are given to show the validity of the proposed results.

This paper investigates the problem of adaptive fuzzy state-feedback control for a category of single-input and single-output nonlinear systems in nonstrict-feedback form. Unmodeled dynamics and input constraint are considered in the system. Fuzzy logic systems are employed to identify unknown nonlinear characteristics existing in systems. An appropriate Lyapunov function is chosen to ensure unmodeled dynamics to be input-to-state practically stable. A smooth function is introduced to tackle input saturation. In order to overcome the difficulty of controller design for nonstrict-feedback system in backstepping design process, a variables separation method is introduced. Moreover, based on small-gain technique, an adaptive fuzzy controller is designed to guarantee all the signals of the resulting closed-loop system to be bounded. Finally, two illustrative examples are given to validate the effectiveness of the new design techniques.

This paper presents an adaptive fuzzy control approach for a category of uncertain nonstrict-feedback systems with input saturation and output constraint. A variable separation approach is introduced to overcome the difficulty arising from the nonstrict-feedback structure. The problem of input saturation is solved by introducing an auxiliary design system, and output constraint is handled by utilizing a barrier Lyapunov function. Combing fuzzy logic system with the adaptive backstepping technique, the semi-global boundedness of all variables in the closed-loop systems is guaranteed, and the tracking error is driven to the origin with a small neighborhood. The stability of the closed-loop systems is proved, and the simulation results reveal the effectiveness of the proposed approach.

In this paper, the problem of fuzzy filter design is investigated for a class of nonlinear networked systems on the basis of the interval type-2 (IT2) fuzzy set theory. In the design process, two vital factors, intermittent data packet dropouts and quantization, are taken into consideration. The parameter uncertainties are handled effectively by the IT2 membership functions determined by lower and upper membership functions and relative weighting functions. A novel fuzzy filter is designed to guarantee the error system to be stochastically stable with H∞ performance. Moreover, the filter does not need to share the same membership functions and number of fuzzy rules as those of the plant. Finally, illustrative examples are provided to illustrate the effectiveness of the method proposed in this paper.

The problem of fuzzy observer-based controller design is investigated for nonlinear networked control systems subject to imperfect communication links and parameter uncertainties. The nonlinear networked control systems with parameter uncertainties are modeled through an interval type-2 (IT2) Takagi-Sugeno (T-S) model, in which the uncertainties are handled via lower and upper membership functions. The measurement loss occurs randomly, both in the sensor-to-observer and the controller-to-actuator communication links. Specially, a novel data compensation strategy is adopted in the controller-to-actuator channel. The observer is designed under the unmeasurable premise variables case, and then, the controller is designed with the estimated states. Moreover, the conditions for the existence of the controller can ensure that the resulting closed-loop system is stochastically stable with the predefined disturbance attenuation performance. Two examples are provided to illustrate the effectiveness of the proposed method.

In this paper, the problem of fuzzy control for nonlinear networked control systems with packet dropouts and parameter uncertainties is studied based on the interval type-2 fuzzy-model-based approach. In the control design, the intermittent data loss existing in the closed-loop system is taken into account. The parameter uncertainties can be represented and captured effectively via the membership functions described by lower and upper membership functions and relative weighting functions. A novel fuzzy state-feedback controller is designed to guarantee the resulting closed-loop system to be stochastically stable with an optimal performance. Furthermore, to make the controller design more flexible, the designed controller does not need to share membership functions and amount of fuzzy rules with the model. Some simulation results are provided to demonstrate the effectiveness of the proposed results.