[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

This paper studies a number of quantized feedback design problems for linear systems. We consider the case where quantizers are static (memoryless). The common aim of these design problems is to stabilize the given system or to achieve certain performance with the coarsest quantization density. Our main discovery is that the classical sector bound approach is nonconservative for studying these design problems. Consequently, we are able to convert many quantized feedback design problems to well-known robust control problems with sector bound uncertainties. In particular, we derive the coarsest quantization densities for stabilization for multiple-input-multiple-output systems in both state feedback and output feedback cases; and we also derive conditions for quantized feedback control for quadratic cost and H/sub /spl infin// performances.

The sector bound approach to quantized feedback control | NOVA. The University of Newcastle's Digital Repository

Chapter 8 establishes the relationship between the input and output power spectral densities of a linear system. Limitations on results are carefully detailed and the case of oscillator noise is considered. The theory is extended to multiple input-multiple output systems.

Linear System Theory

Neural networks

A recent set of overused population-based methods have been published in recent years. Despite their popularity, most of them have uncertain, immature performance, partially done verifications, similar overused metaphors, similar immature exploration and exploitation components and operations, and an insecure tradeoff between exploration and exploitation trends in most of the new real-world cases. Therefore, all users need to extensively modify and adjust their operations based on main evolutionary methods to reach faster convergence, more stable balance, and high-quality results. To move the optimization community one step ahead toward more focus on performance rather than change of metaphor, a general-purpose population-based optimization technique called Hunger Games Search (HGS) is proposed in this research with a simple structure, special stability features and very competitive performance to realize the solutions of both constrained and unconstrained problems more effectively. The proposed HGS is designed according to the hunger-driven activities and behavioural choice of animals. This dynamic, fitness-wise search method follows a simple concept of “Hunger” as the most crucial homeostatic motivation and reason for behaviours, decisions, and actions in the life of all animals to make the process of optimization more understandable and consistent for new users and decision-makers. The Hunger Games Search incorporates the concept of hunger into the feature process; in other words, an adaptive weight based on the concept of hunger is designed and employed to simulate the effect of hunger on each search step. It follows the computationally logical rules (games) utilized by almost all animals and these rival activities and games are often adaptive evolutionary by securing higher chances of survival and food acquisition. This method's main feature is its dynamic nature, simple structure, and high performance in terms of convergence and acceptable quality of solutions, proving to be more efficient than the current optimization methods. The effectiveness of HGS was verified by comparing HGS with a comprehensive set of popular and advanced algorithms on 23 well-known optimization functions and the IEEE CEC 2014 benchmark test suite. Also, the HGS was applied to several engineering problems to demonstrate its applicability. The results validate the effectiveness of the proposed optimizer compared to popular essential optimizers, several advanced variants of the existing methods, and several CEC winners and powerful differential evolution (DE)-based methods abbreviated as LSHADE, SPS_L_SHADE_EIG, LSHADE_cnEpSi, SHADE, SADE, MPEDE, and JDE methods in handling many single-objective problems. We designed this open-source population-based method to be a standard tool for optimization in different areas of artificial intelligence and machine learning with several new exploratory and exploitative features, high performance, and high optimization capacity. The method is very flexible and scalable to be extended to fit more form of optimization cases in both structural aspects and application sides. This paper's source codes, supplementary files, Latex and office source files, sources of plots, a brief version and pseudocode, and an open-source software toolkit for solving optimization problems with Hunger Games Search and online web service for any question, feedback, suggestion, and idea on HGS algorithm will be available to the public at https://aliasgharheidari.com/HGS.html .

Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts

The optimal control input for linear systems can be solved from algebraic Riccati equation (ARE), from which it remains questionable to get the form of the exact solution. In engineering, the acceptable numerical solutions of ARE can be found by iteration or optimization. Recently, the gradient descent based numerical solutions has been proven effective to approximate the optimal ones. This paper introduces this method to tracking problem for heterogeneous linear systems. Differently, the parameters in the dynamics of the linear systems are all assumed to be unknown, which is intractable since the gradient as well as the allowable initialization needs the prior knowledge of system dynamics. To solve this problem, the method named dynamic regressor extension and mix (DREM) is improved to estimate the parameter matrices in finite time. Besides, a discounted factor is introduced to ensure the existence of optimal solutions for heterogeneous systems. Two simulation experiments are given to illustrate the effectiveness.

Optimal Tracking Control for Unknown Linear Systems with Finite-Time Parameter Estimation.

This work investigates the exponential stability problem of inertial delayed neural networks (IDNNs) with hybrid impulses by taking advantage of the concepts of average impulsive interval and average impulsive gain. Firstly, the second-order differential system can be transformed into two first-order differential equations by using the variable substitution method. Secondly, the impulsive sequence involves stabilizing impulses and destabilizing impulses simultaneously. Furthermore, both the cases of Ta <∞ and Ta =∞ are considered. Then, based on the approach of comparison principle, some sufficient conditions on exponential stability of IDNNs subject to hybrid impulses are established. Finally, two numerical examples are shown to verify the effectiveness of the theoretical results.

Hybrid-Impulses-Based Control for Exponential Stability of Inertial Delayed Neural Networks Using Average Impulsive Gain Strategy

This paper investigates the event-based asynchronous finite-time control for a class of cyber-physical switched systems under Denial-of-Service (DoS) attacks. Considering the attack’s characteristics, we put forward a novel attack-instant-constrained hybrid event-triggered scheme (HETS), which can not only contribute to reducing the network transmission overload, but also well descibe the network denial service behavior under attack interference. An asynchronous and ZOH-based controller is delicately constructed to mitigate the influence of DoS attacks and network-induced delay. A double-mode dependent Lyapunov–Krasovskii functional (LKF) is developed to set up some sufficient finite-time stability criteria for the concerned systems in view of the asynchronous switching effect. Finally, an application example of the urban railway system is offered to verify the proposed control algorithm. 

Hybrid event-based asynchronous finite-time control for cyber-physical switched systems under denial-of-service attacks

Reliable memory sampled-data control (MSDC) for Takagi–Sugeno (T–S) fuzzy system is considered in this study. The MSDC approach includes a signal transmission delay, is utilized to stabilize the concerned T–S fuzzy system. Unlike existing Lyapunov–Krasovskii functional (LKF) approaches, a looped functional approach is considered in this paper that minimizes conservatism in designing the desired control gains. A Free-Matrix-Based integral inequality is employed to obtain the upper bounds of the integral terms in the derivative of the looped functional. The sufficient conditions are derived to show the existence of reliable MSDC law and to assure the asymptotic stability of T–S fuzzy systems. The effectiveness and significance of the proposed approach are shown through numerical examples. 

Reliable Memory Sampled-Data Control for T–S Fuzzy Systems

The process of parameter adaptation and identification relies on the excitation of the measurable signals. To ensure at least interval excitation, designed control or noise input is often introduced. The main concern in this paper is the safety of adaptation process, i.e. the empirically chosen noise input may lead system state into unsafe region. First, a safe adaptation framework is presented to achieve both finite-time parameter identification and guaranteed safety. The element-wise parameter estimation is obtained using finite-time dynamic regressor extension and mixing (FT-DREM), by which a worst-case estimation error can also be computed. In this situation, a point-wise safe strategy based on adaptive control barrier function (aCBF) is used to filter the designed control or noise input. Some criteria are proposed to reduce the conservativeness of aCBF method. The robustness of the proposed algorithms is analyzed and a robust algorithm for safe adaptation is also presented. In the end, a simulation experiment of adaptive cruise control with estimation for slope resistance is studied to illustrate the effectiveness of the proposed methods.

Kaibo Shi

Papers

Optimal Tracking Control for Unknown Linear Systems with Finite-Time Parameter Estimation.

Hybrid-Impulses-Based Control for Exponential Stability of Inertial Delayed Neural Networks Using Average Impulsive Gain Strategy

Hybrid event-based asynchronous finite-time control for cyber-physical switched systems under denial-of-service attacks

Reliable Memory Sampled-Data Control for T–S Fuzzy Systems

Robust Finite-Time Adaptation Law for Safety-Critical Control