Computational algorithms for linear control systems: a brief survey

This paper presents a brief survey of computational algorithms for the analysis and synthesis of linear control systems described in the state space. An attempt is made to select the most efficient methods for analysis of the stability, controllability and observability, the reduction into canonical forms, the pole assignment synthesis and the synthesis of optimal systems with quadratic cost. Some aspects of the development of mathematical software for solving these problems are also discussed.     

Evolutionary algorithms for constrained engineering problems

Evolutionary computation techniques have been receiving increasing attention regarding their potential as optimization techniques for complex problems. Recently these techniques were applied in the area of industrial engineering; the most-known applications include scheduling and sequencing in manufacturing systems, computer-aided design, facility layout and location problems, distribution and transportation problems, and many others. Industrial engineering problems usually are quite hard to solve due to a high complexity of the objective functions and a significant number of problem-specific constraints; often an algorithm to solve such problems requires incorporation of some heuristic methods. In this paper we concentrate on constraint handling heuristics for evolutionary computation techniques. This general discussion is followed by three test case studies: truss structure optimization problem, design of a composite laminated plate, and the unit commitment problem. These are typical highly constrained engineering problems and the methods discussed here are directly transferrable to industrial engineering problems.     

Genetic algorithms: a survey

Genetic algorithms provide an alternative to traditional optimization techniques by using directed random searches to locate optimal solutions in complex landscapes. We introduce the art and science of genetic algorithms and survey current issues in GA theory and practice. We do not present a detailed study, instead, we offer a quick guide into the labyrinth of GA research. First, we draw the analogy between genetic algorithms and the search processes in nature. Then we describe the genetic algorithm that Holland introduced in 1975 and the workings of GAs. After a survey of techniques proposed as improvements to Holland's GA and of some radically different approaches, we survey the advances in GA theory related to modeling, dynamics, and deception     

Evolutionary algorithms in multiply-specified engineering. The MOEAs and WCES strategies

This paper addresses multi-objective optimization from the viewpoint of real-world engineering designs with lots of specifications, where robust and global optimization techniques need to be applied. The problem used to illustrate the process is the design of non-linear control systems with hundreds of performance specifications. The performance achieved with a recent multi-objective evolutionary algorithm (MOEA) is compared with a proposed scheme to build a robust fitness function aggregation. The proposed strategy considers performances in the worst situations: worst-case combination evolution strategy (WCES), and it is shown to be robust against the dimensionality of specifications. A representative MOEA, SPEA-2, achieved a satisfactory performance with a moderate number of specifications, but required an exponential increase in population size as more specifications were added. This becomes impractical beyond several hundreds. WCES scales well against the problem size, since it exploits the similar behaviour of magnitudes evaluated under different situations and searches similar trade-offs for correlated objectives. Both approaches have been thoroughly characterized considering increasing levels of complexity, different design problems, and algorithm configurations.     

Robot control systems: A survey

Robot manipulators have attracted considerable interest from researchers both in universities and industry during recent years. This interest covers a broad spectrum from task planning, robot language and artificial intelligence to mechanics, sensing and control. This survey paper addresses the area of robot position control and attempts to give an overview of the basic problems involved and some existing solutions.     

Multi-model control of MIMO systems: location and control algorithms

This paper presents a multi-model control scheme that depends on the multiple representation of a process using linear models. A dynamic system can be represented by several models, each of which is different in either the simplifications involved, the reductions involved, or the dynamic characteristics. A new tracking multi-model control algorithm for deterministic systems is proposed. An auxiliary input called the ‘state correction’ is calculated and applied to the models so as to minimize a performance index which is a function of the difference between the process outputs and the model outputs. A simulation study is given to show the potential of the proposed algorithm.     

电力系统稳定控制综述

结合与电力系统动态行为有关的研究对象 ,如励磁控制、调速器控制、FACTS控制、负荷频率控制等 ,对包括 L yapunov方法、H∞ 控制、反馈线性化、变结构控制等在内的主要电力系统稳定控制方法及其应用作了简要回顾 ,并指出目前存在的问题及今后的研究方向。     

红外弱小目标检测算法综述

红外探测技术具有不受环境等因素干扰的优势,在红外制导、预警等军事领域的应用日益广泛。随着对红外弱小目标检测技术的研究越来越深入,相应的检测方法越来越多样。本文通过对红外弱小目标图像中目标与背景的特性以及红外弱小目标检测技术难点问题进行分析,根据当前是否利用帧间相关信息,分别从基于单帧红外图像和基于红外序列两个角度,选取了相应的红外弱小目标算法进行对比,对其中典型算法的原理、流程以及特点等进行了详细综述,并对每类检测算法的性能进行了比较。针对红外弱小目标图像信噪比低的特点,对红外弱小目标检测算法的难点问题进行分析,给出了目前各种算法的解决方法和不足,探讨红外弱小目标检测算法的发展方向,即研究计算量小、性能优、鲁棒性强、实时性好和便于硬件实现的算法。     

Generic properties and control of linear structured systems: a survey

In this survey paper, we consider linear structured systems in state space form, where a linear system is structured when each entry of its matrices, like A,B,C and D, is either a fixed zero or a free parameter. The location of the fixed zeros in these matrices constitutes the structure of the system. Indeed a lot of man-made physical systems which admit a linear model are structured. A structured system is representative of a class of linear systems in the usual sense. It is of interest to investigate properties of structured systems which are true for almost any value of the free parameters, therefore also called generic properties. Interestingly, a lot of classical properties of linear systems can be studied in terms of genericity. Moreover, these generic properties can, in general, be checked by means of directed graphs that can be associated to a structured system in a natural way. We review here a number of results concerning generic properties of structured systems expressed in graph theoretic terms. By properties we mean here system-specific properties like controllability, the finite and infinite zero structure, and so on, as well as, solvability issues of certain classical control problems like disturbance rejection, input-output decoupling, and so on. In this paper, we do not try to be exhaustive but instead, by a selection of results, we would like to motivate the reader to appreciate what we consider as a wonderful modelling and analysis tool. We emphasize the fact that this modelling technique allows us to get a number of important results based on poor information on the system only. Moreover, the graph theoretic conditions are intuitive and are easy to check by hand for small systems and by means of well-known polynomially bounded combinatorial techniques for larger systems.     

Recursive algorithms of adaptive control in stochastic systems

Conclusions The obtained results constitute an identification method of analysis of optimal decisions under uncertainty. Within this approach we have constructed a recursive procedure that has a number of fairly useful properties. First of all, it is sequential. Thus it is possible to use each observation for improving the estimates and for adaptive control. In this way we can construct fairly good estimates and a strategy with a small number of observations. In particular, the improvement of the strategy begins with a number of observations n=2. Moreover, it makes it possible to construct an optimal true stationary strategy after finitely many steps of adaptive control. Finally, the above methods make it possible to solve problems of fairly large dimension.Translated from Kibernetika, No. 6, pp. 88–94, November–December, 1981.     

动态环境中的进化算法

目前关于进化算法（EA）的研究主要局限于静态优化问题,然而很多现实世界中的问题是动态的,对于这类时变的优化问题通常并不是要求EA发现极值点,而是需要EA能够尽可能紧密地跟踪极值点在搜索空间内的运行轨迹.为此,综述了使EA适用于动态优化问题的各种方法,如增加种群多样性、保持种群多样性、引入某种记忆策略和采用多种群策略等.     

Evolutionary algorithms for orthogonal frequency division multiplexing-based dynamic spectrum access systems

This paper proposes two evolutionary algorithms (EAs) to perform dynamic spectrum assignment in distributed OFDM-based cognitive radio access networks. To achieve better radio resource utilization, the central spectrum manager (CSM) jointly considers the type of modulation level which can be used by each radio pair when deciding the number of subcarriers to be assigned. Using the piecewise convex transformations, we reformulate the nonlinear integer programming problem to an integer linear programming so that it is possible to obtain the optimal solution. While the solution to the integer linear programming problem is NP-hard, the proposed EAs provide useful suboptimal solutions especially when the number of radios and subcarriers are large. Our first proposed EA adopts the genetic algorithm. Although the reproduction process generates chromosomes which do not fulfill the constraints, our algorithm integrates the invisible walls technique used in particle swam optimization to retain the diversity while constructing chromosomes for the next generation. The second EA adopts the ant colony optimization approach using a directed multigraph. The vertices are used to represent the subcarriers and each edge corresponds to a possible chosen modulation index of a specific radio. We further obtain the performance of the two EAs through simulations and benchmark them against the optimal solution. Our studies show that ant colony algorithm gives better solutions most of the time, however, its computation time increases much faster compared to generic algorithm when the numbers of users and subcarriers increase.     

Online control of discrete-event systems: A survey

In control theory, as in other areas of engineering research, there is an inherent tension between the breadth of a technique’s applicability and its mathematical tractability. For the area of discrete-event systems (DES), this manifested itself in a theory of supervisory control that originally provided correct-by-construction guarantees for offline solutions to a restricted kind of deterministic process. Follow-on work extended the reach of these techniques to a number of new settings, notably the development of online control without sacrificing any of the original DES performance guarantees. The ability to enact online control opened the door to applying DES techniques to the adaptive control processes presented by modern technologies: processes with dynamic and time-varying natures, whose characteristics may be understood poorly or not at all. Although many works have built on the seminal work of online control in DES, we believe that these ideas have not reached their full potential due to the difficulty in translating them to adjacent fields. In this survey, we look back at 30 years of research concerning the online control of DES and closely related limited lookahead policies with an eye to making the works accessible to practitioners in the broader control theory and artificial intelligence communities. We conclude with some thoughts on future research directions for the further development and application of online DES control techniques to problems requiring intelligent control in our modern world.     

Error encoding algorithms for networked control systems

A networked control system is characterized by having a feedback loop closed through a local area network. This paper considers methods for scheduling the use of the network to guarantee both stability and controller performance. We propose and validate algorithms for choosing message identifiers for dynamically scheduled networked control systems. Two schemes for selecting priority levels are proposed: a fixed arbitrary grid and an auto-scaling grid. We prove that the system is uniformly ultimately bounded in the case of the fixed encoding scheme, and asymptotically stable with auto-scaling. An inverted pendulum is used to illustrate the encoding methods.     

Structured population genetic algorithms: a literature survey

The Genetic Algorithm (GA) has been one of the most studied topics in evolutionary algorithm literature. Mimicking natural processes of inheritance, mutation, natural selection and genetic operators, GAs have been successful in solving various optimization problems. However, standard GA is often criticized as being too biased in candidate solutions due to genetic drift in search. As a result, GAs sometimes converge on premature solutions. In this paper, we survey the major advances in GA, particularly in relation to the class of structured population GAs, where better exploration and exploitation of the search space is accomplished by controlling interactions among individuals in the population pool. They can be classified as spatial segregation, spatial distance and heterogeneous population. Additionally, secondary factors such as aging, social behaviour, and so forth further guide and shape the reproduction process. Restricting randomness in reproduction has been seen to have positive effects on GAs. It is our hope that by reviewing the many existing algorithms, we shall see even better algorithms being developed.     

Evolutionary neural networks and DNA computing algorithms for dual-axis motion control

A new method is proposed to deal with the dual-axis control of a multi-variables system with two induction motors. Investigation of resolving the cross-coupling problem of dual-axis platform is addressed by a neural net-based decoupling compensator and a sufficient condition ensuring closed-loop stability is derived. An evolutionary algorithm processing the universal seeking capability is proposed for finding the optimal connecting weights of the neural decoupling compensator and the gains of PID controllers. Extensive numerical studies verify the performance and applicability of the proposed design under a variety of operating conditions.     

Uncertain variables and learning algorithms in knowledge-based control systems

This paper concerns a class of knowledge-based control systems with unknown parameters in the knowledge representation describing a plant. For the deterministic case, the logic-algebraic method of determination of the control decisions has been developed. The purpose of this paper is to present an extension of this method for the case with unknown parameters. In the first part, an approach based on uncertain variables is presented. In the second part, the method and algorithms of current knowledge updating in the learning system are proposed. The main idea of this approach consists of a step-by-step estimation of the unknown parameters in the knowledge representation using the successive values of the control decisions and their results. This concept may be considered as an extension of the known ideas of identification and adaptation for the traditional case. This work was presented, in part, at the Third International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–21, 1998