Decoupling Adaptive Smith Prediction Model of Flatness Closed-Loop Control and Its Application

Flatness control system is characterized by multi-parameters, strong coupling, pure time delay, which complicate the establishment of an accurate mathematical model. Therefore, a control scheme that combines dynamic decoupling, PI (Proportion and Integral) control and adaptive Smith predictive compensation is proposed. To this end, a dynamic matrix is used to decouple the control system. A multivariable coupled pure time-delay system is transformed into several independent generalized single-loop pure time-delay systems. Then, a PI-adaptive Smith predictive controller is constructed for the decoupled generalized single-loop pure time-delay system. Simulations show that the scheme has a simple and feasible structure, and good control performance. When the mathematical model of the control system is inaccurate, the control performance of adaptive Smith control method is evidently better than that of the ordinary Smith control method. The model is successfully applied to the cold rolling production site through LabVIEW, and the control accuracy is within 5I. This study reveals a new solution to the problem of coupled pure time-delay in flatness control system.

Neural tuning matches frequency-dependent time differences between the ears

The time it takes a sound to travel from source to ear differs between the ears and creates an interaural delay. It varies systematically with spatial direction and is generally modeled as a pure time delay, independent of frequency. In acoustical recordings, we found that interaural delay varies with frequency at a fine scale. In physiological recordings of midbrain neurons sensitive to interaural delay, we found that preferred delay also varies with sound frequency. Similar observations reported earlier were not incorporated in a functional framework. We find that the frequency dependence of acoustical and physiological interaural delays are matched in key respects. This suggests that binaural neurons are tuned to acoustical features of ecological environments, rather than to fixed interaural delays. Using recordings from the nerve and brainstem we show that this tuning may emerge from neurons detecting coincidences between input fibers that are mistuned in frequency.

Pure Time Delay Analysis for Decentralized Networked Control Systems

The network disturbance effect can be considered as either a perturbation or as a pure time delay for the exchanged signals. The network-induced time delay is one of the main challenges when a network is inserted in the feedback loops of a control system. In this paper, our objective is to improve the behavior of a Networked Control System (NCS) by analyzing the time-delay given that the decentralized control design method is adopted. First, we review an observer-based control method for decentralized control systems. Second, we establish a map between the decentralized non-networked system, and the typical NCS state-space representation. The main idea the mapping is to put the Decentralized Networked Control System (DNCS) in a general form and then map the resulting system to the typical NCS form. Third, we derive the global dynamics of the DNCS. Fourth, an upper bound for the time-delay is derived that guarantees the stability of LTI DNCSs. Finally, we present a numerical example that illustrates the applicability of the derived bound.