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In this paper, an event‐triggered adaptive improved terminal sliding mode controller is proposed for addressing the consensus tracking problem of multi‐manipulators system under the weak communication networks. Firstly, an improved terminal sliding surface is designed to reduce the steady‐state error of system. Then, by devising a variable‐rate reaching law for sliding surface, the control chattering is greatly weakened. Furthermore, a trigger mechanism with time‐varying threshold is incorporated into the designed controller to adjust the update frequency of the control law for conserving communication resources. This strikes a balance between tracking accuracy and resources conservation. Eventually, the tracking performance of the weak‐connected system is greatly improved under the proposed algorithm. Based on Lyapunov stability theory, it is proven that the consensus tracking error of the system asymptotically converges to zero. Simulation results verify the effectiveness and performance improvement of the proposed scheme. In this paper, an event‐triggered adaptive improved terminal sliding mode controller is proposed for addressing the consensus tracking problem of multi‐manipulators system under the weak communication networks. Simulation results verify the effectiveness and performance improvement of the proposed scheme.

This article proposes that biologically plausible theories of behavior can be constructed by following a method of “phylogenetic refinement,” whereby they are progressively elaborated from simple to complex according to phylogenetic data on the sequence of changes that occurred over the course of evolution. It is argued that sufficient data exist to make this approach possible, and that the result can more effectively delineate the true biological categories of neurophysiological mechanisms than do approaches based on definitions of putative functions inherited from psychological traditions. As an example, the approach is used to sketch a theoretical framework of how basic feedback control of interaction with the world was elaborated during vertebrate evolution, to give rise to the functional architecture of the mammalian brain. The results provide a conceptual taxonomy of mechanisms that naturally map to neurophysiological and neuroanatomical data and that offer a context for defining putative functions that, it is argued, are better grounded in biology than are some of the traditional concepts of cognitive science.

This article provides an update of the Theory of Event Coding (TEC), which claims that perception and action are identical processes operating on the same codes – event files consisting of integrated networks of sensorimotor feature codes. The original version of the theory emphasized its representational underpinnings, but recent theoretical developments provide the basis for a more integrated view consisting of both the codes that are shared between perception and action in the control processes operating on these codes. Four developments are discussed in more detail: The degree to which the integration and retrieval of event files depends on current goals, how metacontrol states impact the handling of event files, how feature binding relates to event learning, and how the integration of non-social events relates to the integration of social events. Case examples using various versions of the Simon task are used to explain how the new version of TEC explains interactions between perception and action in non-social and social situations.

An improved fractional-order integral sliding mode control (FOISMC) method is proposed in this study to address the performance degradation of lane-keeping control for intelligent vehicles under complex conditions. In this approach, a fractional-order integral sliding surface is constructed by introducing a composite tracking error e , and is combined with an improved fractional-order variable power reaching law to design a lane-keeping controller based on FOISMC. Subsequently, the Lyapunov stability theory is employed to analyze the system’s stability, and a proof is provided to demonstrate that the system states can reach the fractional-order integral sliding surface in finite time. Finally, a co-simulation model was established using MATLAB/Simulink and CarSim to evaluate the proposed control strategy. The results demonstrate that, even under high-speed conditions with crosswind disturbances, the FOISMC outperforms both TISMC and FOPID. Specifically, the maximum lateral position error is reduced by 10.1% and 68.7%, respectively, while the root mean square error is decreased by 8% and 60.9%. These results confirm that the proposed approach not only achieves a significant improvement in control accuracy but also exhibits strong robustness.

Real-world actions often comprise a series of movements that cannot be entirely planned before initiation. When these actions are executed rapidly, the planning of multiple future movements needs to occur simultaneously with the ongoing action. How the brain solves this task remains unknown. Here, we address this question with a new sequential arm reaching paradigm that manipulates how many future reaches are available for planning while controlling execution of the ongoing reach. We show that participants plan at least two future reaches simultaneously with an ongoing reach. Further, the planning processes of the two future reaches are not independent of one another. Evidence that the planning processes interact is twofold. First, correcting for a visual perturbation of the ongoing reach target is slower when more future reaches are planned. Second, the curvature of the current reach is modified based on the next reach only when their planning processes temporally overlap. These interactions between future planning processes may enable smooth production of sequential actions by linking individual segments of a long sequence at the level of motor planning.

In this article, we challenge the usefulness of “attention” as a unitary construct and/or neural system. We point out that the concept has too many meanings to justify a single term, and that “attention” is used to refer to both the explanandum (the set of phenomena in need of explanation) and the explanans (the set of processes doing the explaining). To illustrate these points, we focus our discussion on visual selective attention. It is argued that selectivity in processing has emerged through evolution as a design feature of a complex multi-channel sensorimotor system, which generates selective phenomena of “attention” as one of many by-products. Instead of the traditional analytic approach to attention, we suggest a synthetic approach that starts with well-understood mechanisms that do not need to be dedicated to attention, and yet account for the selectivity phenomena under investigation. We conclude that what would serve scientific progress best would be to drop the term “attention” as a label for a specific functional or neural system and instead focus on behaviorally relevant selection processes and the many systems that implement them.

Due to the limited knowledge, experience and ability of a single expert, an increasing number of practical multi-criteria sorting (MCS) problems require the participation of multiple experts, which are called MCS problems in the context of group decision-making (MCS-GDM problems for short). To obtain consensual sorting results for alternatives, consensus reaching processes need to be considered in MCS-GDM problems. In this paper, two consensus-based TOPSIS-Sort-B algorithms are developed to deal with MCS-GDM problems. We first develop a minimum adjustment optimization model to obtain consensual boundary profiles by considering different experts’ boundary profiles. Based on individual decision matrices and the collective decision matrix, individual and group sorting results of alternatives can be obtained by using the TOPSIS-Sort-B method, respectively. Afterwards, different local adjustment strategy-based feedback adjustment mechanisms that can meet different needs are proposed to help experts adjust their assessments, and two consensus-based TOPSIS-Sort-B algorithms are designed to obtain consensual sorting results for MCS-GDM. Finally, a numerical example for green building rating and detailed simulation experiments are presented to justify the proposed algorithms and compare different feedback adjustment mechanisms.

When carrying out a sequence of movements, humans can plan several steps in advance to make the movement smooth.When carrying out a sequence of movements, humans can plan several steps in advance to make the movement smooth.

Due to the uncertainty of decision environment and differences of decision makers’ culture and knowledge background, multi-granular HFLTSs are usually elicited by decision makers in a multi-attribute group decision making (MAGDM) problem. In this paper, a novel consensus model is developed for MAGDM based on multi-granular HFLTSs. First, it is defined the group consensus measure based on the fuzzy envelope of multi-granular HFLTSs. Afterwards, an optimization model which aims to minimize the overall adjustment amount of decision makers’ preference is established. Based on the model, an iterative algorithm is devised to help decision makers reach consensus in MAGDM with multi-granular HFLTSs. Numerical results demonstrate the characteristics of the proposed consensus model.

This paper proposes an improved double power reaching law integral SMC algorithm to overcome the chattering, large overshoot, slow response. This improved algorithm has two advantages. Firstly, the designed control law can reach the approaching equilibrium point quickly when it is away from or close to the sliding surface. The chattering and response speed problems can be resolved. Secondly, the proposed algorithm has a good anti-jamming performance, and can maintain a good dynamic quality under the condition of the uncertain external disturbance. Finally, the proposed algorithm is applied to the open-loop unstable magnetic suspension system. Theoretical analysis and Matlab simulation results show that the improved algorithm has better control performances than the traditional SMC and the power reaching law integral SMC algorithm, such as less chattering, smaller overshoots, and faster response speed.