[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

This article considers the stability problem of truncated sampled-data control systems with impulsive effects. We propose the concept of average sampling interval (ASI), in which the constraint on the lower/upper bound of sampling intervals is removed to handle the sampling intervals from the holistic perspective. Then, in the framework of the input delay method, we transform the addressed system into an equivalent switched time-delay system subject to the impulsive effects that result from the truncated sampled-data control law. Interestingly, it is shown that the truncation of the sampled-data control input, which is an extension of the classical zero-order hold function, can play a stabilizing role in the presence of impulsive effects. Next, based on the ASI concept, the idea of average truncated length is introduced and the corresponding relaxed stability criteria are derived for the truncated sampled-data control systems. Particularly, the potential destabilizing impact of impulsive effects on the stability is theoretically revealed. It is also shown that under certain conditions, a truncated sampled-data system can remain stable if the magnitude of the impulsive effects is sufficiently small. Finally, two illustrative examples are presented to show the validity and also the advantages of the obtained results.

Stability of Truncated Sampled-Data Control Systems With Impulsive Effects

This paper investigates the problem of event-triggered finite-time extended dissipative control for a class of switched linear systems. We propose a novel event-triggered method that allows frequent system switching over an event-triggered interval, which is different from the previous work that only one switching happened over the event-triggered interval. By average dwell time and a novel controller-mode-dependent Lyapunov function method, we give sufficient conditions for finite-time extended dissipative analysis of the closed-loop switched linear system. LMIs are used for the design of the controller. Finally, numerical examples are given to illustrate the effectiveness of the proposed method.

Finite-time Event-triggered Extended Dissipative Control for a Class of Switched Linear Systems

Asynchronous event-triggered finite-time control for networked switched T-S fuzzy systems

Impulsive control for attitude stabilization in the presence of unknown bounded external disturbances

Aided by a modified event-triggered communication policy (ETCP), this article addresses the dissipativity-based control synthesis problem for semi-Markovian switching systems (SMSSs) with simultaneous multiplicative probabilistic faults on sensors and actuators modules. The resulting model under consideration is more extensive, which covers semi-Markovian switching coefficients, transmission delays, and randomly occurring sensors and actuators faults in a unified systematic analytical framework instead of investigating separately in some existing works. More specifically, the probabilistic faults are assumed to happen on both the sensors and actuators modules simultaneously, and the distortion probability for each sensor and actuator is irrelevant, which can be characterized by multiplicate mutually independent stochastic variables that obeys certain statistical features and probabilistic distribution delineate on the interval [ 0 , ✠ ] ( ✠ ≥ 1 ) . To reduce the bandwidth usage, a novel event-triggered strategy is designed. Additionally, in the light of this newly developed ETCP, and considering the effects of the signal transmission delays and multitudinous probabilistic failures, a generalized and more realistic faulty pattern for SMSSs is presented, which is more fit for real applications. Hereby, the principal superiority of the established new type faulty pattern lies in its practicality and generality, which contains some previous faulty models as special scenarios. By constructing an appropriate semi-Markovian Lyapunov functional (SMLF) together with mathematical analysis technique and matrix inequality decoupling operation, sojourn-time-dependent sufficient conditions for determining both the control gain matrices and triggered configuration coefficients are developed and formulated in terms of a group of feasible linear matrix inequalities (LMIs). Eventually, several practical examples are exploited to substantiate the validity and practicability of the developed control design methodology.

Dissipativity-based synthesis for semi-Markovian systems with simultaneous probabilistic sensors and actuators faults: A modified event-triggered strategy.

Kaibo Shi

Papers

Stability of Truncated Sampled-Data Control Systems With Impulsive Effects

Finite-time Event-triggered Extended Dissipative Control for a Class of Switched Linear Systems

Asynchronous event-triggered finite-time control for networked switched T-S fuzzy systems

Impulsive control for attitude stabilization in the presence of unknown bounded external disturbances

Dissipativity-based synthesis for semi-Markovian systems with simultaneous probabilistic sensors and actuators faults: A modified event-triggered strategy.