System Design Problem Research Articles

Fast heuristics for the time‐constrained immobile server problem

AbstractWe propose easy‐to‐implement heuristics for time‐constrained applications of a problem referred to in the literature as the facility location problem with immobile servers, stochastic demand, and congestion, the service system design problem, or the immobile server problem (ISP). The problem is typically posed as one of allocating capacity to a set of M/M/1 queues to which customers with stochastic demand are assigned with the objective of minimizing a cost function composed of a fixed capacity‐acquisition cost, a variable customer‐assignment cost, and an expected‐waiting‐time cost. The expected‐waiting‐time cost results in a nonlinear term in the objective function of the standard binary programming formulation of the problem. Thus, the solution approaches proposed in the literature are either sophisticated linearization or relaxation schemes, or metaheuristics. In this study, we demonstrate that an ensemble of straightforward, greedy heuristics can rapidly find high‐quality solutions. In addition to filling a gap in the literature on ISP heuristics, new stopping criteria for an existing cutting plane algorithm are proposed and tested, and a new mixed‐integer linear model requiring no iterating algorithm is developed. In many cases, our heuristic approach finds solutions of the same or better quality than those found by exact methods implemented with expensive, state‐of‐the‐art mathematical programming software, in particular a commercial nonlinear mixed‐integer linear programming solver, given a five‐minute time limit.

Observer-based control for time-delayed quasi-one-sided Lipschitz nonlinear systems under input saturation

This paper addresses the observer-based controller design problem for nonlinear time-delayed systems under input saturation. The nonlinearities are supposed to satisfy the quasi-one-sided Lipschitz condition, which is less conservative than the one-sided Lipschitz condition. Based on the nonlinear matrix inequalities, control law for nonlinear systems subject to input saturation, time delays, and unavailable states, some sufficient conditions have been developed for an augmented system containing the system state vector and the error vector to ensure the convergence of all states to zero. The paper used a decoupling approach to reduce the complexity of the corresponding observer and controller gain computations. Finally, the effectiveness of the developed results is validated using suitable examples.

Distributed nonlinear fusion filtering for multi-sensor networked systems with random varying parameter matrix and missing measurements

This paper is concerned with the distributed fusion filtering algorithm design problem for multi-sensor nonlinear networked systems (MSNNSs) subject to multiplicative noises, random varying parameter matrix and missing measurements (MMs). In particular, we utilize the Bernoulli random variable with certain statistical features to describe and characterize the MMs phenomenon. By introduce a fictitious noise, the effects from process noise as well as random varying parameter matrix are addressed and a new nonlinear stochastic networked system is obtained. The primary purpose of this paper is to develop a novel fusion filtering scheme of the distributed way and provide the corresponding boundedness evaluation criterion. Firstly, specific upper bounds of filtering error covariance (FEC) are identified and locally minimized at each sampling instant. Subsequently, based on the obtained local filters, a distributed fusion filtering algorithm is designed via adopting the inverse covariance intersection (ICI) fusion idea. Furthermore, the analysis with respect to the upper bound of local FEC is discussed and examined by proposing a sufficient condition under certain constraints regarding the related parameters. Eventually, with the help of the simulation experiments, the usefulness of the proposed fusion filtering algorithm is illustrated.

Game-Theoretic Adversarial Interaction-Based Critical Scenario Generation for Autonomous Vehicles

Ensuring safety and efficiency in the rapidly advancing autonomous vehicle (AV) industry presents a significant engineering challenge. Comprehensive performance evaluations and critical scenario testing are essential for identifying situations that AVs cannot handle. Thus, generating critical scenarios is a key problem in AV testing system design. This paper proposes a game-theoretic adversarial interaction method to efficiently generate critical scenarios that challenge AV systems. Initial motion prediction for adversarial and surrounding vehicles is based on kinematic models and road constraints, establishing interaction action spaces to determine possible driving domains. A novel evaluation approach combines reachability sets with adversarial intensity to assess collision risks and adversarial strength for any state, used to solve behavior values for each interaction action state. Further, equilibrium action strategies for the vehicles are derived using Stackelberg game theory, yielding optimal actions considering adversarial interactions in the current traffic environment. Simulation results show that the adversarial scenarios generated by this method significantly increase incident rates by 158% to 1313% compared to natural driving scenarios, while ride comfort and driving efficiency decrease, and risk significantly increases. These findings provide critical insights for model improvement and demonstrate the proposed method’s suitability for assessing AV performance in dynamic traffic environments.

State Observer-Based Iterative Learning Control Design for Discrete Systems Using the Heavy Ball Method

The paper considers a state observer-based iterative learning control design problem for discrete linear systems. To accelerate the convergence of the learning error, a combination of the heavy ball method from optimization theory and the vector Lyapunov function method for a class of two-dimensional systems known as repetitive processes is used to develop a new design. A supporting numerical example is given, including a comparison with an existing design.

Risk and Value Informed Structural Health Monitoring System Design for the Miter Gates

The ultimate goal of a Structural Health Monitoring and Prognostics system (SHM/DP) is to interrogate the data gathered in situ to infer the state of the structure (diagnostics), evaluate the system's evolution over time (prognos-tics), and leverage the diagnostic and prognostic results to facilitate decision-making. As a compelling business proposition, SHM/DP is expected to deliv-er economic returns through data-informed lifecycle management decisions, surpassing the estimated costs encompassing the design, initial deployment operations, and maintenance of the SHM/DP system. Therefore, it is prudent to approach a system design problem with a cost-benefit analysis perspective, aiming to achieve a design that maximizes the Value of Information (VoI) ac-quired by an information-acquisition system, evaluated in a pre-posterior sense. In this paper, our focus is on a sensor optimization framework that aims to maximize the VoI over the lifecycle of a miter gate structure. We em-ploy the proposed Bayesian sensor optimization framework to assess the ef-fectiveness of various Value of Information (VoI) metrics in quantifying the overall benefits of an SHM/DP system. Additionally, we explore the impact of the decision maker's risk profile and the adopted decision policies on the system design.

Design and Analysis of Load Frequency Control for a Two-Area Power System Using Conventional PID and FPID Controllers

Power system stability is required to maintain a continuous balance between power generation and load demand. Frequency control is also a major function of automatic generation control and one of the most important control problems in power system design and operation. So, a robust control system must be implemented for controlling the actual power and frequency. This paper mainly focuses on the design of a two-area power system with a rated frequency of 50 Hz, which is used in Myanmar, using the fuzzy-PID method to control the load frequency. It also aims to imply from one area to another when demand suddenly increases or decreases. It involves applying mathematical formulas and models to analyze how an automatic generation control works. The plan is first implemented in a single area before being modified to a two-area power system with and without controllers. The output results are simulated with MATLAB/ SIMULINK.

Toward digital design at the exascale: An overview of project ICECap

High performance computing has entered the Exascale Age. Capable of performing over 1018 floating point operations per second, exascale computers, such as El Capitan, the National Nuclear Security Administration's first, have the potential to revolutionize the detailed in-depth study of highly complex science and engineering systems. However, in addition to these kind of whole machine “hero” simulations, exascale systems could also enable new paradigms in digital design by making petascale hero runs routine. Currently, untenable problems in complex system design, optimization, model exploration, and scientific discovery could all become possible. Motivated by the challenge of uncovering the next generation of robust high-yield inertial confinement fusion (ICF) designs, project ICECap (Inertial Confinement on El Capitan) attempts to integrate multiple advances in machine learning (ML), scientific workflows, high performance computing, GPU-acceleration, and numerical optimization to prototype such a future. Built on a general framework, ICECap is exploring how these technologies could broadly accelerate scientific discovery on El Capitan. In addition to our requirements, system-level design, and challenges, we describe some of the key technologies in ICECap, including ML replacements for multiphysics packages, tools for human-machine teaming, and algorithms for multifidelity design optimization under uncertainty. As a test of our prototype pre-El Capitan system, we advance the state-of-the art for ICF hohlraum design by demonstrating the optimization of a 17-parameter National Ignition Facility experiment and show that our ML-assisted workflow makes design choices that are consistent with physics intuition, but in an automated, efficient, and mathematically rigorous fashion.

Resilience and performance quantification of dynamic reconfiguration

Dynamic reconfiguration is an adaptive resilience mechanism that can help address several system design problems. Adaptation through dynamic reconfiguration can improve quality of service, increase fault-tolerance, help recover from failures, and prevent and recover from cyber attacks. This mechanism acts primarily by reconfiguring one or more of a system’s resources. While system reconfiguration is advantageous, it may bring disadvantages such as performance and availability degradation during reconfiguration intervals. In this work, we quantify the effectiveness of dynamic reconfiguration as a system resilience mechanism and its impact on performance. We define a failure function that captures the effect of dynamic reconfigurations on a system’s resilience to failures and develop metrics that capture the impact of reconfigurations on a system’s execution time and probability of failure. We also derive analytic models that predict the effectiveness of dynamic reconfigurations on execution time and resilience to failures. Several theorems regarding the tradeoff between resilience to failures and performance and availability are presented. Finally, we define an optimization problem, formalized with the help of these theorems, to determine the optimal reconfiguration frequency to meet performance-resilience tradeoffs.

Adaptive event-triggered optimal [formula omitted] tracking control for uncertain nonlinear systems: Comparative analysis applied to autonomic tractor-trailer system

In this paper, an event-triggered tracking optimization control method is proposed for nonlinear uncertain systems with H∞ discounted cost. First, the linearization method using the infinite series expansion theorem is given. And the tracking control design problem is solved by appropriate state augment. Secondly, the zero sum differential game strategy is introduced to address the control design problem of uncertain systems with event-triggered inputs. The actor-critic-disturbance networks framework is used to approximate the optimization of event- triggered controller strategy, optimization costs, and the disturbance strategy, respectively. Due to the consideration of discrete time and continuous time dynamics, we put the impulsive system method to use and choose appropriate Lyapunov functions to prove stability and boundedness, at the same time, there will be no infinite triggering situation. Finally, the effectiveness of the proposed scheme is demonstrated by an example of a practical tractor system with n trailers through simulation compared with another typical design method.

Research on optimal design method of dynamic stress parameters of helical gear based on orthogonal experimental design

Aiming at the reliability optimization design problem of two-grade helical cylindrical gear transmission system, the UG parameterized model based on expression and ADAMS dynamic simulation model under real working conditions are integrated in ISIGHT platform, and the distribution law of peak contact force on the tooth surface of helical gear pair in the transmission system at different meshing times is obtained. Considering the uncertainty of material properties, load conditions and geometric parameters, the sample set was generated according to the orthogonal experimental design method, and the Kriging approximation model was established. On this basis, the contact strength reliability of helical gear was analyzed. The mathematical model of meshing efficiency reliability optimization of helical gear pair was established and solved by Hooke-Jeeves Direct Search method. The results show that the meshing efficiency and reliability of the optimized helical gear pair are improved, and the peak value of the contact force is obviously reduced.

Concept of combined strain-stress state control of long span structures

Purpose: Argumentation of necessity of special protective system design to protect structures in seismic zones and remote areas from specific loads. Such protective system combines seismic isolation and internal force control systems.Research findings: Restrictions of long-span structures construction are reviewed. Such restrictions are caused by building conditions embodied in National Building Codes and Standards. Combined and separate use of seismic isolation and internal force control systems is a critical specific load protection. Present inventions and experience in the field of seismic isolation and internal force control systems are reviewed.Value: The article determines problems of protective system design based on the control for combined strain-stress state.

Network design and vehicle routing problems in road transport systems: Integrating models and algorithms

In this paper, an integrated approach is proposed that incorporates the road network design problem and the vehicle routing problem, which are very often studied separately. This approach can be adopted when the routes to follow in a transport system have to be designed (e.g., for public transit with fixed and variable routes, or for freight distribution) jointly with the network topology and capacity. The goal is to minimise congestion and the impacts on the network from passengers and freight vehicles. The design (control) variables consist of the optimal vehicle routes and optimised road network; they are both discrete (link topology for passenger and freight, routes for passengers and freight vehicles, etc.) and continuous (link capacity in terms of reserved lanes, signal settings, etc.). The problem is modelled in congested transportation networks (e.g., in an urban centre). A heuristic algorithm is adopted to find the best network configuration and the best vehicle routes. The proposed model and the adopted algorithm are applied in a test system to analyse the limits of the methodology, to verify the advantage obtained from the joint method and its applicability. In the test system, the joint procedure gives good results in terms of cost decreases. The reported methods can help public and private stakeholders in making decisions regarding transportation policy, especially in the urban environment.

Coherent Chaotic Communication Using Generalized Runge–Kutta Method

Computer simulation of continuous chaotic systems is usually performed using numerical methods. The discretization may introduce new properties into finite-difference models compared to their continuous prototypes and can therefore lead to new types of dynamical behavior exhibited by discrete chaotic systems. It is known that one can control the dynamics of a discrete system using a special class of integration methods. One of the applications of such a phenomenon is chaos-based communication systems, which have recently attracted attention due to their high covertness and broadband transmission capability. Proper modulation of chaotic carrier signals is one of the key problems in chaos-based communication system design. It is challenging to modulate and demodulate a chaotic signal in the same way as a conventional signal due to its noise-like shape and broadband characteristics. Therefore, the development of new modulation–demodulation techniques is of great interest in the field. One possible approach here is to use adaptive numerical integration, which allows control of the properties of the finite-difference chaotic model. In this study, we describe a novel modulation technique for chaos-based communication systems based on generalized explicit second-order Runge–Kutta methods. We use a specially designed test bench to evaluate the efficiency of the proposed modulation method and compare it with state-of-the-art solutions. Experimental results show that the proposed modulation technique outperforms the conventional parametric modulation method in both coverage and noise immunity. The obtained results can be efficiently applied to the design of advanced chaos-based communication systems as well as being used to improve existing architectures.

Mixed H∞‐based finite‐time passive filtering for a class of uncertain nonlinear singular systems

AbstractThis article discusses the finite‐time mixed and passive filtering design problem for uncertain nonlinear singular systems. The parameter uncertainties are constrained by well‐defined upper bounds on their magnitudes. The objective is to design a full‐order filter to ensure that the augmented singular system to be finite‐time boundedness(FTB) with and passivity performance. Firstly, a sufficient condition for achieving singular finite‐time boundedness with mixed and passivity performance for uncertain nonlinear singular system is derived by introducing a free matrix. Secondly, a novel criterion is established for analyzing finite‐time and passivity problem of filtering error singular system based on augmented matrix technique. Thirdly, the filtering design conditions are presented and the relevant parameters of the desired filter are determined using linear matrix inequality decoupling principle. Finally, a practical circuit system and a numerical example are provided to demonstrate the feasibility of the proposed scheme.

Dublin Descriptors for Problem Scenarios in Problem Based Learning

Problem-based learning has been one of the major pedagogies that support various learning styles, utilizing problems as a key element in studies. While there are many ways to solve a problem, Computational Thinking can be one of them. Typically, we formulate learning outcomes and design assessments to attain these goals. However, there are cases where a single outcome may not suffice, especially with multi-layered problems. In such scenarios, we propose using Dublin Descriptors and evaluate the study using system design problems. Two case studies were designed, requiring students to reflect on the components of computational thinking. Reflections in the second case study were designed according to the written Dublin Descriptors. We conducted qualitative and quantitative analyses on data from 37 students in two case studies to identify themes related to Bloom's and Dublin Descriptors, using In Vivo, Descriptive, and Focus coding in two rounds of analysis. Statistical inferences suggest that the method holds promise for cases requiring advancement beyond Level 4. Dublin Descriptors show promise in cases where information needs to be communicated from ideas to inferences. Keywords—Computational Thinking; Dublin Descriptors, Problem-Based learning; Reflections, System Design

Content Based Image Retrieval System Using CNN based Deep Learning Models

Digital information search archives on the internet and other platforms have been an essential part of our daily lives. With the use of huge amounts of digital images, it is paramount to develop efficient solutions to retrieve images seamlessly. Due to inadequate textual description, the task of automated image retrieval is quite complicated. Therefore, retrieval of images by analysing their visual content is an exciting task and research-worthy. Text based Image Search (TIBR) is time consuming as initially, every image has to be linked with a keyword or text to be extracted on query search. In the proposed system, an efficient algorithm for Content Based Image Retrieval (CBIR) using pre-trained CNN-based Deep Learning models to extract deep features of an image has been developed, which significantly increases the performance of the image retrieval process. Convolutional Neural Networks (CNN) has evolved as an efficient deep learning solution for the CBIR systems that work in image recognition applications. The results of retrieval are evaluated in terms of precision performance parameters. Emphasis has been given to the comparison of the efficiency of the system using two different pre-trained CNN-based Deep Learning models based on VGG16 and ResNet-50 architecture. Transfer learning enables us to develop robust and efficient working systems using pre-trained models. In the proposed system, pre-trained models have been used to derive features using a deep learning CNN network trained for a large image classification dataset ImageNet. This approach outperforms many contemporary CBIR systems. As most CBIR systems depend on query images and as most users of such systems are non-professional, we have developed a user interface for the built prototype systems. The efficiency of the CBIR systems with pre-trained models VGG16, and ResNet-50 are compared for better image retrieval and reliability. The proposed system has addressed a few existing problems in CBIR system design and developed a suitable solution with less semantic gap and better efficiency in image retrieval.

On the Global Stability of Nonlinear Hurwitz Control Systems

The problem of designing nonlinear control systems by the algebraic polynomial-matrix method using quasilinear models is considered. The quasilinear models of nonlinear plants are easily created on the basis of their nonlinear equations in the Cauchy form. To create these models only the differentiability of the plants nonlinearities is required. The solution to the design problem of the nonlinear Hurwitz control systems using the algebraic polynomial-matrix method is available if the quasilinear model of the plant is controllable. The design with application of this method consists of creating the quasilinear model of the nonlinear plant, generating several polynomials, composing and solving a system of linear algebraic equations. The theorem about the global stability of the equilibrium of the nonlinear systems, represented in the quasilinear model, is proved by the method of Lyapunov functions. The numerical examples of the design of nonlinear Hurwitz control systems are given. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article is concerned with the creation of stable nonlinear control systems with nonlinear elements since the linear systems can’t fulfill the quality requirements demanded from modern control systems. Additionally, the known design methods of nonlinear systems, such as the input-state feedback linearization, backstepping, passivity and others, require the transformation of the original nonlinear equations of the plant into some special form. These transformations are often very difficult to find and to execute. A new algebraic polynomial-matrix design method of the nonlinear control systems using the quasilinear models of nonlinear plants is proposed in the article. This method can be applied if the nonlinearities of the plant are differentiable, and the quasilinear model of the plant is controllable. The theorem about global stability of the equilibrium is proved for the systems designed with this approach. The quasilinear models are easily created on the basis of the original equations in the Cauchy form for a given nonlinear plant. The algebraic polynomial-matrix design method is very simple: the quasilinear model is created; several polynomials are calculated with the use of this model and the linear algebraic equations system is composed. The solution of this system allows us to write down the expression which defines the control law as a nonlinear function of the plant’s state variables. This system will be globally or locally stable if the conditions of the theorem or the corollaries, proved in this article, are satisfied. The numerical examples illustrate the application of the suggested approach to the design of nonlinear Hurwitz control systems for the nonlinear plants. The main advantages of this approach: the quasilinear models are created quite easily; the nonlinear control law is found as a solution to the system of linear equations. The results can be applied to the creation of nonlinear control systems of nonlinear plants in many industries: shipbuilding, aircraft construction, automobile construction, agriculture and many others.

A design and analysis of computer experiments based mixed integer linear programming approach for optimizing a system of electric vehicle charging stations

This paper formulates a mixed integer linear programming (MILP) model to optimize a system of electric vehicle (EV) charging stations. Our methodology introduces a two-stage framework that integrates the first-stage system design problem with a second-stage control problem of the EV charging stations and develops a design and analysis of computer experiments (DACE) based system design optimization solution method. Our DACE approach generates a metamodel to predict revenue from the control problem using multivariate adaptive regression splines (MARS), fit over a binned Latin hypercube (LH) experimental design. Comparing the DACE based approach to using a commercial solver on the MILP, it yields near optimal solutions, provides interpretable profit functions, and significantly reduces computational time for practical application.

Finite-time stabilization of discrete-time conewise linear systems

The finite-time stabilizing control design problem for discrete-time conewise linear systems is tackled in this paper. Such a class of systems consists of the union of ordinary linear time-invariant subsystems, whose dynamics are defined in prescribed conical regions, constituting a conical partition of the state space. By imposing some cone-copositivity properties to a suitable piecewise quadratic function, two sufficient conditions are preliminarily derived concerning the system’s finite-time stability. By building on them, novel results are then presented for the system’s finite-time stabilization through a piecewise linear output feedback controller. Such results are based on the solution of feasibility problems involving sets of Linear Matrix Inequalities (LMIs). A numerical example illustrates the effectiveness of the proposed approach.