ABSTRACT This article investigates the state estimation issue for discrete‐time systems subject to composite disturbances (CD) and state equality constraints, in which the CD include unknown inputs and non‐Gaussian noise. To enhance the estimation performance under the influence of CD, a quadratic estimator incorporating state equality constraints is proposed. Initially, the Kronecker algebra technique is used to compute the second‐order Kronecker powers of the raw vectors and a quadratic dynamical system is formed by combining the original vectors with their corresponding second‐order Kronecker powers. Additionally, a specific condition is imposed to suppress the interference caused by unknown inputs. On this basis, a quadratic state unconstrained estimator (QSUE) is developed based on the minimum variance unbiased criterion. Furthermore, a quadratic state constrained estimator (QSCE) is designed by applying the projection technique to the QSUE. Finally, a simulation example demonstrates that the QSCE achieves better estimation performance than the QSUE and exhibits greater adaptability to CD.

The traditional unscented Kalman filters (UKFs) under the maximum correntropy criterion provide a powerful tool for nonlinear state estimation with heavy-tailed non-Gaussian noise. Nevertheless, the above-mentioned filters may yield biased estimates because the Gaussian kernel function can only handle certain types of non-Gaussian noise. Additionally, the use of statistical linearization methods can result in approximation errors when solving linear observation equations, while the system may also experience observation data loss. Therefore, a new iterative UKF with intermittent observations under the generalized maximum correntropy criterion is proposed for systems with complex non-Gaussian noise, called GMCC-IO-IUKF. Firstly, the connection between the UKF with and without intermittent observations is established by designing a coefficient matrix including intermittent observation variables, so as to derive the UKF with intermittent observations under the maximum correntropy criterion. Secondly, for the measurement update of GMCC-IO-IUKF, a nonlinear regression augmented model that can deal with both prediction and observation errors is established using the coefficient matrix and the nonlinear function . To better adapt to different types of non-Gaussian noise, the generalized Gaussian kernel function is substituted for the traditional Gaussian kernel function. Theoretically, GMCC-IO-IUKF can achieve better estimation performance by directly employing the nonlinear function and the latest iteration value. Finally, a classical target tracking model is used to evaluate the estimation performance and feasibility of our proposed GMCC-IO-IUKF algorithm. It appears from the experiment results that our proposed GMCC-IO-IUKF can not only promote estimation precision but also handle complex non-Gaussian noise flexibly.

This paper investigates the problem of adaptive output feedback control for uncertain nonlinear systems with input/output quantization subject to intermittent denial-of-service (DoS) attacks. When a DoS attack becomes active, the system is unable to obtain output signal. In order to address this challenge, we compensate the output signal by introducing an attack compensator. At the same time, a novel quantization compensator-based (QC) state observer is designed, which uses only the quantized compensated signal to reconstruct the system states. In addition, the problem of over-parameterisation is avoided by using parameter projection technique to design the parameter estimator. By combining Lyapunov stability theory and a modified average dwell time (ADT) approach, the stability of the closed-loop system is ensured. Furthermore, the proposed controller ensures that all closed-loop signals are bounded, and the control performance of mean-square error can be adjusted by appropriately selecting certain design parameters. Finally, the effectiveness of the proposed control strategy is validated through a single-link robot simulation results.Note to Practitioners—Considering that resources are limited, the DoS attacks mentioned in this paper are aperiodic, which means that attackers engage in frequent attacks within a relatively short time frame. This aligns more closely with real-world scenarios of network attacks. When the attack is active, the output signal is not available for the system. In this paper, a new QC state observer is designed, which in turn enables the estimation of the system state signals. Furthermore, this paper considers both input and output quantization, which reduces duplicate transmission of samples and improves the use of communication resources. This makes the work in this paper more relevant to the practical engineering context.

In this article, we propose a dynamic event-triggered adaptive prescribed-time output feedback tracking control strategy for nonlinear systems with unknown external disturbances under denial-of-service (DoS) attacks. The presence of malicious intermittent DoS attacks makes the output signal and states of the system unavailable, which in turn leads to more difficulty in the design of the controller. To overcome the above difficulty, we construct a novel composite observer based on the attack compensator, whereby the system states can be reconstructed. Moreover, with the help of the time-varying constraint function, the prescribed-time tracking control problem for nonlinear systems is transformed into the constraint problem of tracking error. At the same time, a new dynamic event-triggered adaptive prescribed-time safety fuzzy controller is built and remains applicable in the systems that operate continuously after the predefined time. The adaptive fuzzy safety control method proposed in this article ensures that the tracking error converges to the user-specified region in the predefined time, all signals of the closed-loop system remain bounded under intermittent DoS attacks, and the repeated transmission of samples from the controller to the actuator can be further reduced, generating fewer events and saving communication resources. Finally, the simulation results of the single-link robotic arm demonstrate the rationality and effectiveness of the developed control algorithm.

Based on output triggering, an adaptive prescribed-time tracking control strategy is proposed for a class of uncertain strict-feedback nonlinear systems with unknown control gains in this article. The nondifferentiability of the virtual control signals is identified as the most prominent design difficulty in this research. In order to solve the above difficulty, a new fuzzy state observer is built by using triggered output signal and fuzzy logic systems (FLSs), which in turn generates alternative continuous states. Simultaneously, the estimated signals are utilized to design virtual control signals, making certain that the virtual control signals have a well-defined first derivative. On this basis, by introducing a time-varying constraint function, a new adaptive prescribed-time fuzzy controller is constructed, so that the controller can still be applied to continuously operating systems after the predefined time. Additionally, we introduce the command filtering technique to mitigate repeated differentiation of virtual control signals during the backstepping design process. Combining the constructed logarithmic Lyapunov functions and bounded control technique with backstepping design, it is possible to guarantee that the established adaptive prescirbed-time event-triggered control method satisfies the following: 1) within the predefined time, the tracking error converges to the user-specified region and 2) the full range of signals involved in the closed-loop system is kept bounded. At last, the results of the single-link arm simulation example verify the reasonableness and effectiveness of the established control scheme.

During vehicle driving, all aspects of data monitorings are not accurate enough for the vehicle, and there may be packet loss of measurement data. In addition, the accuracy of vehicle data is more difficult to guarantee when the vehicle state is continuously changing, which may lead to some potential safety hazards during driving. Consequently, many algorithms, which only use the statistical characteristics of packet loss information, have been proposed to improve the accuracy. However, with the rapid development of technology, the time-stamp technique in sensor networks can obtain packet loss information at the current moment. In contrast, although the time-stamp technique can effectively improve the filter performance, it cannot analyze the convergence of the Riccati equation. Therefore, this paper proposes a modified EKF algorithm for balancing these two algorithms, and meanwhile, simulation experiments test and verify the effectiveness and feasibility of our proposed algorithm.

In this article, we study the issue of adaptive neural output-feedback controller design for a class of uncertain switched time-delay nonlinear systems with nonlower triangular structure. The prominent contribution of this article is that the delay-dependent stability criterion of nonswitched nonlinear systems is successfully extended to that of switched nonlower triangular nonlinear systems. The design algorithm is listed as follows. First, a switched state observer is designed such that the error dynamic system can be generated. Second, neural networks, adaptive backstepping technique, and variable separation method are, respectively, applied to construct a common controller for all subsystems, in which the Lyapunov-Krasovskii functionals are deliberately constructed such that the average dwell-time scheme can be employed to guarantee the stability and performance of the closed-loop system, despite the existence of time delays. Third, the stability analysis process confirms in detail that all the variables of the closed-loop system are semiglobally uniformly ultimately bounded. Finally, simulation study is given to show the validity of the proposed control approach.

This study firstly investigates the estimation problem for systems with multiplicative noise and measurement delay. Based on the innovation analysis approach, the estimators are developed in terms of a Riccati equation and a Lyapunov equation. The equations are of the same dimension as the plant; therefore compared with the augmentation approach, the presented approach lessens the computational demand. Then the linear quadratic regulation (LQR) problem for input delay systems is discussed based on non‐augmented approach, and the controller is given in terms of a backward Riccati equation and a backward Lyapunov equation. Finally, the authors establish a duality between the estimation problem for measurement delay systems with multiplicative noise and the LQR problem for deterministic input delay systems with constraint conditions.

This paper is concerned with estimation problem for discrete-time systems with packet dropping. A new optimal filter is derived by minimizing the mean squared estimation error. An optimal smoother is also derived in a similar way. Both estimators are designed by solving one deterministic Riccati equation. Both the convergence of the estimation error covariance and mean square stability of the estimator are proved under standard assumption. It is shown that the new estimator has smaller error covariance and has wider applications as compared with the MMSE estimator. One of the key techniques adopted in this paper is the introduction of the innovation sequence for the multiplicative noise systems.