Author: IEEE Transactions on Automatic Control - new TOC

By incorporating the redefined error monitor function into the network design, an error redefinition neural network (ERNN) is proposed to control mobile redundant manipulators to execute the tracking task in this article. The global asymptotic stability and the strong antidisturbance capability of the ERNN are proved theoretically. Furthermore, the ERNN can overcome the overshoot and constant disturbance. Meanwhile, the ERNN is input-to-state stable, while the bounded time-varying disturbance is considered as the control input.

Checking uniform attractivity of a time-varying dynamic system without a strict Lyapunov function is challenging as it requires the characterization of the limiting behavior of a set of trajectories. In the context of hybrid nonlinear time-varying systems, characterizing such limiting or convergent behaviors is even harder due to the complexity stemming from both continuous-time variations as well as discrete-time jumps. In this article, an extension of the standard hybrid time domain is introduced to define limiting behaviors, using set convergence, when time approaches either positive infinity or negative infinity. In particular, it is shown how to characterize limiting behaviors under the condition that an output signal approaches zero. Such limiting behaviors and their associated limiting systems can be used to verify uniform global attractivity. Particularly, a generalization of the classic Krasovskii–LaSalle theorem is obtained for hybrid time-varying systems. Two examples are used to demonstrate the effectiveness of the results.

A probabilistic Boolean network (PBN) is a discrete-time system composed of a collection of Boolean networks between which the PBN switches in a stochastic manner. This article focuses on the study of quotients of PBNs. Given a PBN and an equivalence relation on its state set, we consider a probabilistic transition system that is generated by the PBN; the resulting quotient transition system then automatically captures the quotient behavior of this PBN. We therefore describe a method for obtaining a probabilistic Boolean system that generates the transitions of the quotient transition system. Applications of this quotient description are discussed, and it is shown that for PBNs, controller synthesis can be performed easily by first controlling a quotient system and then lifting the control law back to the original network. A biological example is given to show the usefulness of the developed results.

Two stochastic model predictive control algorithms, which are referred to as distributionally robust model predictive control algorithms, are proposed in this article for a class of discrete linear systems with unbounded noise. Participially, chance constraints are imposed on both of the state and the control, which makes the problem more challenging. Inspired by the ideas from distributionally robust optimization (DRO), two deterministic convex reformulations are proposed for tackling the chance constraints. Rigorous computational complexity analysis is carried out to compare the two proposed algorithms with the existing methods. Recursive feasibility and convergence are proven. Simulation results are provided to show the effectiveness of the proposed algorithms.

Cohesion in networks during transitions from one consensus value to another, i.e., the ability of agents to respond in a similar manner during the transition, can be as important as achieving the new consensus value. Existing decentralized network control strategies mainly concern with the convergence speed to the final consensus value. However, even with increased convergence speed, the level of cohesion loss during transitions can be large. This loss of cohesion during transition (and tracking of varying consensus values) can be alleviated using a recently developed delayed self reinforcement (DSR) approach. However, the current DSR-based approach assumes ideal conditions with agents having instant access to neighbor information—without network delays arising during sensing or communication between neighbors, as well as computation of control actions of each agent, which can cause instability. The main contributions of this article are to use the Rouchè’s theorem to 1) prove the stability of the DSR approach if the network delay is not too large; and 2) compute an estimate of the acceptable network delay margin (DM) for stability. Additionally, a simulation example is used to illustrate the estimation approach for network DMs with DSR, and show that cohesion is maintained with DSR even with network delays when compared to the case without DSR.

Presents the table of contents for this issue of the publication.

This article investigates the robust cooperative output regulation problem of heterogeneous uncertain linear multiagent systems with switching topologies and infinite distributed communication delays. A novel distributed observer is proposed for each agent to estimate the state of the so-called exosystem by taking consideration of both switching topologies and infinite distributed communication delays. A distributed output feedback controller is then developed based on the internal model principle and the estimated state without using the prior knowledge of communication delays. It is shown via the newly developed Lyapunov-like method that the robust cooperative output regulation problem can be solved under some mild assumptions. Finally, a numerical example is given to demonstrate the effectiveness of the proposed controller.

This article is devoted to the problem of detectability of a large class of linear stochastic systems with time varying coefficients simultaneously affected by state multiplicative white noise perturbations and Markovian switching. The main contribution of this article is to propose a Popov–Belevich–Hautus-type test, which is equivalent to the detectability of the considered stochastic systems in the sense that all unstable modes produce some nonzero output. The proposed setting unifies in some sense the discrete-time and continuous-time ones, dissimilarly to the vast majority of existing works that study discrete and continuous time separately.

This article investigates the problem of how to control nonlinear systems with delays in the state by memoryless linear feedback. The notions of semiglobal asymptotic stabilization and sublevel sets are introduced in the context of time-delay systems. With the aid of Razumikhin theorem, we develop a semiglobal design method for the construction of Lyapunov functions, associated sublevel sets, and delay-free linear state feedback laws, step-by-step, achieving semiglobal asymptotic stabilization for time-delay nonlinear systems in a lower triangular form. In contrast to the global stabilization of nonlinear systems with delays in the state, which is usually achieved by dynamic state feedback (Lin and Zhang, 2020), the significance of this work is to point out that a tradeoff of the control objectives, e.g., semiglobal versus global stabilization, makes it possible to control a class of time-delay nonlinear systems by static linear feedback. Extensions to nonlinear systems with globally asymptotically locally expoentially stable (GALES)-like inverse dynamics are also included in this article.

This article provides a quantitative and qualitative evaluation of the role of interconnection directionality in a general class of quadratic performance metrics for double-integrator networks. We first develop an analysis framework that can be used to evaluate the quadratic performance metrics of networks defined over a general class of directed graphs. A comparison between systems whose directed graph Laplacians are normal and their undirected counterparts unveils an interplay between the interconnection directionality and the control strategy that determines network performance. We show that directionality can significantly degrade performance; however, well-designed feedback can exploit directionality to mitigate this degradation or even improve performance.