Delay‐dependent H∞ observer‐based control for uncertain neutral systems via LMI optimization approach

Abstract

In this paper, the delay‐dependent H∞ observer‐based control for a class of uncertain neutral systems with time‐varying delays is considered. The linear matrix inequality (LMI) optimization approach is used to design the H∞ robust control with disturbance attenuation. The control and observer gains are given from the LMI feasible solutions. Some numerical examples are given to illustrate the use of the main result.     

Robustness of reduced‐order observer‐based feedback control for discrete two‐time‐scale systems

Abstract

A design method to achieve robust stability and performance criteria for discrete two‐time‐scale systems controlled by low‐order observer‐based compensators is proposed. Sufficient conditions for stability and performance robustness are established by an easy extension of the small gain theorem. The theoretical analysis is illustrated by a numerical example.     

An efficient genetic algorithm for TSK‐type neural fuzzy identifier design

Abstract

In this paper, an efficient genetic algorithm (EGA) of the Takagi‐Sugeno‐Kang (TSK) ‐type neural fuzzy identifier (TNFI) is proposed for solving various identification problems. For the proposed EGA method, the better chromosomes will be initially generated while the better mutation points will be determined to perform efficient mutation. The advantages of the proposed learning algorithm are that, first, it converges quickly and the obtained fuzzy rules are precise. Secondly, the proposed EGA method only requires a small population sizes.     

Evolutionary methods for multidisciplinary optimization applied to the design of UAV systems†

The implementation and use of a framework in which engineering optimization problems can be analysed are described. In the first part, the foundations of the framework and the hierarchical asynchronous parallel multi-objective evolutionary algorithms (HAPMOEAs) are presented. These are based upon evolution strategies and incorporate the concepts of multi-objective optimization, hierarchical topology, asynchronous evaluation of candidate solutions, and parallel computing. The methodology is presented first and the potential of HAPMOEAs for solving multi-criteria optimization problems is demonstrated on test case problems of increasing difficulty. In the second part of the article several recent applications of multi-objective and multidisciplinary optimization (MO) are described. These illustrate the capabilities of the framework and methodology for the design of UAV and UCAV systems. The application presented deals with a two-objective (drag and weight) UAV wing plan-form optimization. The basic concepts are refined and more sophisticated software and design tools with low- and high-fidelity CFD and FEA models are introduced. Various features described in the text are used to meet the challenge in optimization presented by these test cases.     

Integrating grey sequencing with the genetic algorithm–immune algorithm to optimise touch panel cover glass polishing process parameter design

The touch panel cover glass is one of the important parts and components that determine touch panel quality. The quality requirement of touch panel cover glass emphasises the stability of glass thickness. As this factor directly influences the induction effect and touch of the touch panel, the parameter conditions for the cover glass polishing process have significant impact. This study integrated grey sequencing with the Genetic algorithm–Immune algorithm to optimise the parameter design for the touch panel cover glass polishing process. The experimental measurement value was the thickness value of the processed glass, and the uniformity of glass thickness after processing was discussed. The optimum processing combination influencing the process conditions is as follows: the ambient temperature is 22 (°C), the processing pressure is 0.04 (Mpa), the processing time is 30 (min), the machine speed is 70 (rpm), the polishing solution concentration is 1.4 (g/cm³), the central particle size of polishing powder is 1.4 (um) and the process capability C_pk is 1.75, which is better than the process capability of C_pk 1.41 of the response surface methodology and the process capability of C_pk 1.37 of the Taguchi experimental design.     

Robust mixed H 2/H ∞ controller for uncertain neutral state‐input delayed systems via LMI optimization approach

Abstract

In this paper, the problem of designing a robust mixed H ₂/H _∞ controller for a class of uncertain neutral state‐input delays system is considered. Based on Lyapunov‐Krasovskii functional theory, a delay‐dependent criterion is derived for the existence of a desired mixed H ₂/H _∞ controller, which can be easily constructed by certain feasible linear matrix inequalities (LMIs). Furthermore, a convex optimization problem is formulated to solve for a robust mixed H ₂/H _∞ controller which achieves the minimization of an upper bound of the closed‐loop H ₂ perforance measure.     

A new decentralized stabilization design for large‐scale systems

Abstract

A decentralized stabilization problem for a large‐scale system composed of a number of subsystems is investigated. Using Lyapunov stability and the bounds of the solution of the Lyapunov equation, we derive two main results. The first result (Theorem 1) requires checking the negativity of a matrix containing two free parameters to test the decentralized stabilizability of the whole system. The second result (Theorem 2) determines the ranges of two free parameters to satisfy Theorem 1 such that the decentralized local state feedbacks guarantee the whole large‐scale system is stabilized. The matching condition for each subsystem is not necessary in this paper. The results are also summarized using a flow chart which represents the algorithm for decentralized stabilization.     

A new spanning tree-based genetic algorithm for the design of multi-stage supply chain networks with nonlinear transportation costs

The design of configuration and the transportation planning are crucial issues to the effectiveness of multi-stage supply chain networks. The decision makers are interested in the determination the optimal locations of the hubs and the optimal transportation routes to minimize the total costs incurred in the whole system. One may formulate this problem as a 0-1 mixed integer non-linear program though commercial packages are not able to efficiently solve this problem due to its complexity. This study proposes a new spanning tree-based Genetic Algorithm (GA) using determinant encoding for solving this problem. Also, we employ an efficient heuristic that fixes illegal spanning trees existing in the chromosomes obtained from the evolutionary process of the proposed GA. Our numerical experiments demonstrate that the proposed GA outperforms the other previously published GA in the solution quality and convergence rate.     

The GRAI approach to the structural design of flexible manufacturing systems†

This paper is addressed to all persons interested in the structural design of flexible manufacturing systems (FMS) but particularly to analysts who use simulation methods. The approach suggested consists of two procedures. The first is a progressive procedure which shows how to succeed in the design by starting with reasonable and feasible objectives, and then progress on the basis of experience. The second is a decomposition procedure which comprises methods guiding the following four design steps

(a) activity structuring and problem understanding,

(b) specification of simulation system,

(c) resolution of formal problem solving and

(d) implementation of the computational process.     

A genetic algorithm with domain knowledge for weapon‐target assignment problems

Abstract

In this paper, a novel genetic algorithm, including domain specific knowledge into the crossover operator and the local search mechanism for solving weapon‐target assignment (WTA) problems is proposed. The WTA problem is a full assignment of weapons to hostile targets with the objective of minimizing the expected damage value to own‐force assets. It is an NP‐complete problem. In our study, a greedy reformation and a new crossover operator are proposed to improve the search efficiency. The proposed algorithm outperforms its competitors on all test problems.     

Genetic robust controller design for uncertain systems with time‐delays in both state and control input via LMI approach

Abstract

In this paper, the robust control for uncertain delay systems in state and control input is considered. The uncertainty is nonlinear time‐varying perturbations and does not require a matching condition but is norm‐bounded. Based on the constructive use of Lyapunov functional, delay‐dependent stabilizability criteria are proposed to guarantee stability for the systems via linear control. Linear matrix inequality (LMI) and Genetic algorithms are used to design a memoryless state‐feedback controller. Two numerical examples are given to illustrate that the proposed results are less conservative than some others.     

A label‐based information flow control model for object‐oriented systems

Abstract

This paper proposes a label‐based information flow control model to prevent information leakage within object‐oriented systems. It offers the features of: (a) adapting to dynamic object state change, (b) adapting to dynamic role change, (c) preventing indirect information leakage, (d) detailing the control granularity to variables, (e) allowing purpose‐oriented method invocation, (f) controlling method invocation through argument sensitivity, (g) allowing declassification, and (h) allowing only trusted sources to write a variable.     

Use of axiomatic design principles for analysing the complexity of human–machine systems

Coupling between user goals and user actions has an adverse effect on usability, because it increases the gulf of execution. Based on the principles of axiomatic design, a methodology was developed for identifying and suggesting avenues for eliminating such couplings. A model of a human–machine system is constructed using the following design domains: goal; functional; physical; and action. The mappings between these domains are represented using design equations, which provide a qualitative metric for characterizing the degree of coupling. The use of this methodology is illustrated using several examples, which show that coupling may exist between the goal and functional domains, the functional and physical domains or the physical and action domains. Different loci of coupling have different implications for design improvement. By providing a general and rational criterion, this formalized and analytical methodology has the potential to be a useful tool for design engineers during the early stages of design.     

Stability criteria for neutral systems with time‐varying delays and nonlinear uncertainties

Abstract

The asymptotic stability problem for a class of neutral systems with time‐varying delays and nonlinear uncertainties is investigated in this paper. LMI‐based delay‐dependent criteria are proposed to guarantee the asymptotic stability of the considered systems. New Lyapunov‐Krasovskii functional and Leibniz‐Newton formulae are used to find the delay‐dependent stability results. Finally, some numerical examples are illustrated to show the improved results from using this method.     

MFXRLS‐based adaptive feed‐forward controller implemented with velocity sensor identified by frequency response to improve actuator speaker performance in ANC systems

Abstract

To improve performance of a “dual voice coil” (DVC) actuator speaker in an active noise control (ANC) system, a speaker model that includes coupling dynamics and source of noise pressure is first derived to design an adaptive feed‐forward controller based on modified, filtered‐X, recursive‐least‐squares (MFXRLS) algorithm in this investigation. A novel velocity sensor measuring velocity of the speaker face is further developed by use of a frequency‐response method. Two transfer functions required for the velocity sensor are identified in two steps: (i) the adaptive feed‐forward controller is applied to keep speaker face velocity zero to identify the first transfer function; and (ii) the second transfer function is then obtained experimentally using the first transfer function. Performance of the established velocity sensor is similar to that of a Polytec OFV2100 laser velocity transducer. This velocity sensor is then incorporated with the adaptive feed‐forward controller to control the DVC actuator speaker in the ANC system. For a sinusoidal command input of frequency below 390 Hz, the controlled speaker acquires a unit‐gain magnitude and zero phase degree, showing that the controller can effectively reduce effects of the speaker dynamics, including coupling dynamics.     

Tailoring the particle swarm optimization algorithm for?the?design of offshore oil production risers

In offshore oil production activities, risers are employed to connect the wellheads at the sea-bottom to a floating platform at the sea surface. The design of risers is a very important issue for the petroleum industry; many aspects are involved in the design of such structures, related to safety and cost savings, thus requiring the use of optimization tools. In this context, this work presents studies on the application of the Particle Swarm Optimization method (PSO) to the design of steel catenary risers in a lazy-wave configuration. The PSO method has shown good efficiency for some applications, but its performance is dependent on the values selected for the parameters of the algorithm. Therefore, this work describes some variants of the method, and presents results of several experiments performed to analyze the behavior of its parameters, trying to improve the performance of the method and tailor it for the application to the design of riser systems. The resulting method and its best set of parameters can then be taken as the default values in an implementation of the PSO method in the in-house OtimRiser computational tool, oriented to the design of risers, and also incorporating other optimization methods based on evolutionary concepts.     

Comparison of the Luus–Jaakola optimization procedure and the genetic algorithm

The success of both genetic algorithms (GA) and the Luus–Jaakola (LJ) optimization procedure in engineering optimization and the desire for efficient optimization methods arising from practical experience make the comparison of these two methods necessary. The GA and the LJ optimization procedure are compared in terms of convergence speed and reliability in obtaining the global optimum. Instead of using the number of function evaluations, this study uses computation time for comparison of convergence speed, which is more precise. Although for some problems, such as parameter estimation for the catalytic cracking process of gas oil, both GA and LJ converge to the optimum rapidly and show high reliability; in most cases, the LJ optimization procedure was found to be faster than GA and exhibited higher reliability in obtaining the global optimum. Furthermore, the LJ optimization procedure is easier to program.