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The problem of H∞ control for network-based 2D systems with missing measurements is considered. A stochastic variable satisfying the Bernoulli random binary distribution is utilized to characterize the missing measurements. Our attention is focused on the design of a state feedback controller such that the closed-loop 2D stochastic system is mean-square asymptotic stability and has an  H∞ disturbance attenuation performance. A sufficient condition is established by means of linear matrix inequalities (LMIs) technique, and formulas can be given for the control law design. The result is also extended to more general cases where the system matrices contain uncertain parameters. Numerical examples are also given to illustrate the effectiveness of proposed approach

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An H∞ iterative learning controller is designed for networked systems with intermittent measurements and iteration-varying disturbances. By modeling the measurement dropout as a stochastic variable satisfying the Bernoulli random binary distribution, the design can be transformed into H∞ control of a 2D stochastic system described by Roesser model. A sufficient condition for mean-square asymptotic stability and H∞ disturbance attenuation performance for such 2D stochastic system is established by means of linear matrix inequality (LMI) technique, and formulas can be given for the control law design simultaneously. A numerical example is given to illustrate the effectiveness of the proposed results

Stability Analysis of High-Order Iterative Learning Control for a Class of Nonlinear Switched Systems

This paper considers the stability of high-order PID-type iterative learning control law for a class of nonlinear switched systems with state delays and arbitrary switched rules, which perform a given task repeatedly. The stability condition for the proposed high-order learning control law is first established, and then the stability is analyzed based on contraction mapping approach in the sense of λ norm. It is shown that the proposed iterative learning control law can guarantee the asymptotic convergence of the tracking error for the entire time interval through the iterative learning process. Two examples are given to illustrate the effectiveness of the proposed approach

Model-Free Adaptive Control Algorithm with Data Dropout Compensation

The convergence of model-free adaptive control (MFAC) algorithm can be guaranteed when the system is subject to measurement data dropout. The system output convergent speed gets slower as dropout rate increases. This paper proposes a MFAC algorithm with data compensation. The missing data is first estimated using the dynamical linearization method, and then the estimated value is introduced to update control input. The convergence analysis of the proposed MFAC algorithm is given, and the effectiveness is also validated by simulations. It is shown that the proposed algorithm can compensate the effect of the data dropout, and the better output performance can be obtained

Sparse Multiuser Receiver Design in Large Scale Array System

This paper focuses on the problem of utilizing millimeter wave (mmWave) and Terahertz (THz) massive hybrid arrays to serve multiple users simultaneously. The mmWave and THz massive arrays are characterized by wide bandwidth and high gain, leading to extensive application prospects. Moreover, a hybrid structure array can combine multiple antenna signals through the phase shifter network. Compared with the full digital array, it is a cost-effective technique that can be functional with fewer radio frequency (RF) chains. However, owing to the adoption of a discrete Fourier transform (DFT) structure, most traditional massive hybrid arrays, which allocate one chain to each user, are restricted to scenarios where the number of RF chains is more than that of users. Otherwise, even users with ideal channel conditions and short distances are inherently difficult to assign an independent chain. Thus, it will limit the scale of users that the base station (BS) can support. Inspired by the above analysis, this paper develops a method to provide service for more users with limited RF chains. Firstly, an analog matrix designing method based on the minimax criterion, which enables arrays access to multiple users, is proposed to guarantee each user a good array gain. Secondly, we establish a receiver designing scheme by the GAMP algorithm to receive signals from multiple users at the same time. Additionally, good bit error rate (BER) performance can be obtained under the condition that the observation matrix is not of full rank. Finally, numerical simulations demonstrate the effectiveness of our proposed method

A 16 7 16-Element Slot Array Fed by Double-Layered Gap Waveguide Distribution Network at 160 GHz

In this article, a slot array with double-layered full-corporate-fed distribution network by ridge gap waveguide (RGW) in the G-band is presented. The array antenna proposed in this article contains $16\times 16$ -element radiation slots fed by air-filled ridge gap waveguide distribution network that achieves high-efficiency. Gap waveguide technology avoids the demand for perfect electrical contact in millimeter waves, therefore the expensive diffusion bonding and the laser welding processes are not demanded. Moreover, the high-accurate Computerized Numerical Control (CNC) machining is applied for the fabrication. Due to the limited layout space for the distribution network, two types of universal stepped cavity power dividers are presented in this article. The proposed array antenna is fed by a standard WR-5 waveguide at the bottom. Furthermore, the tested outcomes show that the proposed $16\times 16$ -element array has a gain larger than 30 dBi with over 50% antenna efficiency in the frequency range of 155–171 GHz