International Journal of Adaptive Control and Signal Processing最新文献

This article investigates the $��{�\ell �}_{�\infty �}�$ zonotopic state estimation for a class of discrete-time switched neural networks (SNNs) with mode-dependent average dwell time (MDADT)switching. First, by building appropriate mode-dependent Lyapunov functions, some sufficient conditions are established to guarantee the stability and $��{�\ell �}_{�\infty �}�$ performance for the considered system. Utilizing the formulated criteria, we propose a codesign scheme for switching signals and the mode-dependent nonlinear observer. In addition, a time-varying state zonotope is constructed, and with this as the basis, estimated bounds are calculated accordingly. Eventually, an illustrative example is utilized to demonstrate the efficacy and superiority of the results derived in this article.

The fault estimation and dynamic output feedback control problems of switched nonlinear systems, influenced by actuator and sensor faults, disturbances and stochastic additive noise, are explored in this paper. In the process of fault estimation, the stochastic noise is decoupled by the observer. The order of the observer can be adjusted within a certain range to harmonize the trade-off between the cost involved in estimation and its accuracy. Based on the estimated information, the nonlinear dynamic output feedback controller is constructed, and it can compensate for the impact of faults on the system. The LMI condition is obtained so that the closed-loop system is asymptotically bounded in mean square. Regional pole constraints are proposed to optimize the performance of the system. Finally, two simulation examples are given.

In this paper, a predefined-time control scheme, which intends to stabilize the concerned nonlinear time-delay systems possessing input unmodeled dynamics, is developed. During the controller design procedure, a predefined-time controller exported by following the backstepping technique is applied to the stabilizer of such concerned system, and the Lyapunov-Krasovskii functionals (LKFs), M-filter are respectively introduced to deal with the time delays and input unmodeled dynamics. Meanwhile, the fractional power piecewise function is constructed in the stabilizer to avoid the singularity problem caused by differentiating the virtual control laws at the origin. In addition, the neural networks are introduced to deal with the apparent nonlinearities by basing on their approximation ability. In stability analysis, under the proper selection of Lyapunov function candidates(LFC), we can verify that all signals in the formulating closed-loop system can possess the property of semi-global uniformly ultimately bounded (SGUUB) within a given predefined-time, and simulations, which cover theoretical and practical simulations, can reflect the availability of the developed scheme.

This article researches a predefined-time adaptive fuzzy tracking control problem for a class of nonlinear systems with unknown hysteresis. Unlike the previous investigations on predefined-time control, unknown input hysteresis is considered within this article. The existence of unknown input hysteresis leads to a control gain whose direction is unknown. The Nussbaum function is introduced to get over this difficulty. Lemma 8 is applied to ensure that an integral term with a Nussbaum function is bounded. Thus, the predefined-time stability criterion is ensured. Furthermore, an adaptive fuzzy control scheme is put forward. The proposed scheme guarantees that the tracking error is able to converge to the vicinity of the origin in an expected time. The practicability of the designed scheme is validated by an electromechanical system.

The research object of coupling identification is for multivariate systems. It is required to study and explore recursive and iterative identification methods for multivariable systems when there exists information coupling and/or parameter coupling between their subsystems. For a multivariable system, namely a multiple-input multiple-output system, after parameterization, its identification model contains the same information vector in all its subsystems. For a multi-input ARX system where there exists the information vector coupling, this paper derives the coupled identification model and investigates recursive parameter identification methods for such partially-coupled information vector systems, and presents a hierarchical recursive gradient identification algorithm and a hierarchical multi-innovation recursive gradient identification algorithm. Finally, the simulation example is provided to show the effectiveness of the proposed algorithms.

In this paper, we address the problem of adaptive consensus tracking control for a class of incommensurate fractional-order switched nonlinear multi-agent systems with external disturbance. All the followers in this study are described as arbitrarily switched heterogeneous fractional-order systems (FOSs), wherein the difficulty of incommensurate fractional order is solved by employing the continuity of fractional differentiation, and the derivative order of the adaptation laws is not determined by the order of the systems. A distributed adaptive control scheme is proposed within the framework of the backstepping control method, common Lyapunov function, and radial basis function neural networks (RBFNNs). By adjusting the parameters appropriately, all the signals of the multi-agent systems (MASs) are bounded, and the consensus tracking error eventually converges to a small neighborhood of the origin. Finally, the effectiveness of the proposed control strategy is verified through a numerical simulation example.

Since denial-of-service (DoS) attacks lead to the instability of multi-agent systems (MASs) and the event-triggered mechanism can reduce the communication resources, studying the event-triggered adaptive fuzzy secure consensus control (SCC) problem for nonlinear multi-agent systems (NMASs) under DoS attacks is a very important topic. However, the existing adaptive secure control approaches assume that the leader signal of the system is known under DoS attacks. Furthermore, the existing event-triggered adaptive secure control works only focus on the event-triggered mechanism for the controller-to-actuator channel of NMASs under DoS attacks, and the existing dual-channel event-triggered adaptive control schemes for both the sensor-to-controller channel and controller-to-actuator channel do not consider DoS attacks. Therefore, this article studies the dual-channel event-triggered adaptive fuzzy SCC issue for NMASs under DoS attacks. Since the communication topology of NMASs is disrupted by DoS attacks, the leader signal and its high-order derivatives are unavailable, a distributed resilient observer is designed to estimate them. A dual-channel event-triggered mechanism for the sensor-to-controller and controller-to-actuator channels is designed. Based on this event-triggered mechanism, and by introducing a high-order chain-like filter into the backstepping control design process to solve the nondifferentiable problem of the virtual controller caused by the triggered output signals, an event-triggered adaptive fuzzy SCC method is developed by fuzzy logic systems (FLSs). The developed SCC method ensures that the NMASs are stable, and the consensus tracking errors converge to a small neighborhood around zero. Furthermore, it saves the dual-channel communication resources. Finally, we apply the developed event-triggered SCC approach to control multiple Euler–Lagrangian systems, and the simulation and comparison results verify its effectiveness.

The control performance of the automation system significantly influences the reliability of industrial processes and product quality. This article focuses on developing an integrated architecture for monitoring control performance, and recovering from degradation in industrial control systems under abnormal operating conditions. Initially, an online performance evaluation matrix is identified, which demonstrates variations of control performance in dynamic processes. It is produced by parameterizing the performance value function using the recursive Bellman equation, and it provides more full-dimensional data than standard scalar indicators. On this basis, a performance degradation detection method is proposed by measuring the deviation between the performance evaluation matrix identified in normal and abnormal conditions. Furthermore, a redundancy-based self-healing control approach using dynamic feedback compensation allows the closed-loop system to recover from performance degradation without changing the predesigned controller. The control gain of the proposed self-healing controller is obtained by model-free reinforcement learning, avoiding the requirement for an accurate representation of complex industrial systems. Finally, the effectiveness of the proposed approach is verified by a benchmark study on the three-tank system.

A pattern-moving-based probabilistic density evolution analysis and control framework is proposed for non-Newtonian mechanical systems, which are complex nonlinear systems governed by statistical regularities. Due to the high uncertainty in the output of non-Newtonian mechanical systems resulting from statistical regularities, pattern category variables are constructed to describe system changes over time instead of state or output variables. Furthermore, the operational properties of pattern category variables are measured by posterior probability density, and a system dynamic description of pattern motion probability density evolution is proposed. Combining pseudo-partial-derivative (PPD) with pattern-moving probability density evolution, a data-driven method is designed to predict the posterior probability density of system output pattern categories. Based on this, a statistical control strategy called pattern-moving probability density evolution control is further designed. Finally, an extended state observer (ESO) is used to design an estimation algorithm for PPD within the controller. The effectiveness of both the parameter estimation and control algorithms is validated through theoretical analysis, and the performance of the control system is verified through numerical simulation.

This work is focused on the neural adaptive output feedback control of decentralized uncertain nonlinear systems subject to bounded disturbance, unmodeled dynamics, and uncertainties. Specifically, pervasive and general decentralized systems are studied, and auxiliary variables are constructed to account for the unmodeled dynamics and the uncertainties associated with high-order interconnection terms. Neuro-observer is introduced to obtain the unmeasured states online, and output feedback adaptive control is implemented. Then, adaptive dynamic surface controller is designed by Lyapunov method and the problem of “explosion of complexity” in traditional backstepping method is eliminated. By initialization technique, not only the ultimately uniformly boundedness of all closed-loop signals is obtained but also the transient performance of tracking errors can be guaranteed in the sense of $��{�L�}_{�\infty �}�$ norm. Numerical and practical simulation examples are performed to validate the effectiveness of the proposed control scheme.