System Design Problem Research Articles

Networked Iterative Learning Control Under Changing Operating Mode of Agents and Configuration of Information Network

The paper considers the iterative learning control (ILC) design problem of a network system under changing operating mode of subsystems (agents) and configuration of information network. The network system consists of identical agents, which are discrete linear dynamic plants operating in a repetitive mode. The operating modes of agents depend on their parameters and the reference trajectory, which must be tracked with required accuracy at output of system. The configurations of information network define the group of functioning agents and the type of information exchange between them. The mode and the configuration change takes place in accordance with certain external rules. The control design is based on the divergent method of the vector Lyapunov function. For reducing the transient error caused by the mode change and the connection of new agents, a special rule for switching the ILC law is proposed. The results of modeling the obtained control law for a group of manipulators with flexible link are presented.

Design Optimization of Noise Filter Using Quantum Annealer

The use of quantum annealers in black-box optimization to obtain the desired properties of a product with a small number of trials has attracted attention. However, the application of this technique to engineering design problems is still limited. Here, we demonstrate the applicability of black-box optimization with a quantum annealer to the design of electric circuit systems, focusing on $\pi$-type noise filters as an example. We develop a framework that uses quantum annealing to find the optimal location of electrical components and conductor paths connecting the components, and confirm that the learning process appropriately works over a number of trials to efficiently search for a design with high performance. The results show the potential applicability of quantum annealing to design problems of electric circuit systems.

Quality Diversity Optimization Method for Bilinear Matrix Inequality Problems in Control System Design

Quality Diversity Optimization Method for Bilinear Matrix Inequality Problems in Control System Design

Effectiveness of Computational Thinking in Problem Based Learning

National Education Policy 2020 demands the education system to acclimate problem based and discovery oriented learning. A problem used as a tool for teaching and learning can help to achieve the learning outcomes and also enhance cognitive and critical thinking skills. Self-directed learning is one of the essential skills needed for the today’s working professionals. Problem based learning and computational Thinking methodologies have been used in varying contexts to achieve the desired course outcomes. This paper proposes a model to solve and analyze case studies using both the approaches. The model was tested using open book exams as assessment method for system design problems and computational thinking was found to be effective when combined with the problem based learning methodology. The instruments were validated using cronbach’s alpha co-efficient and even the student feedback has been positive for the process. The model was analyzed using the case study research method. The results appear to be promising and can be used for most computer science courses which are algorithmic or problem solving in nature. Combining computational thinking with problem solving can be an effective way of course delivery and open book exams can be an effective assessment method. Keywords—Computational Thinking; Problem Based Learning; System Design; Open Book Exams

Dynamic game coverage control algorithms for multiple mobile agents through virtual repulsive force

To improve the coverage efficiency of mobile agents, the related information of the agents is generally required partially or completely during the coverage control process, which may lead to a dramatic control cost and energy consumption increase. How to balance the improvement in coverage rate and the reduction of control cost is an important issue in the coverage control area of mobile agents. This paper addresses the dynamic cooperative game coverage control algorithm design problem of a multi-agent system under an incomplete information condition. First, by separating the moving multi-agent system into several subgroups, where each agent can only obtain the position information of the neighbor agents, the coverage control problem of the moving multi-agent system is transformed into a dynamic cooperative game coverage problem of moving multi-groups under incomplete information conditions. Then, the cooperative game rules of the subgroups are presented, and a virtual repulsive force-based dynamic coverage game decision strategy of the agent system is developed to compute the new candidate positions of the agents. Third, the moving multi-agent system is considered a rigid virtual structure, and the coverage control problem of the system is turned into a new one with the virtual structure as a reference frame. Thus, the displacement path planning design of the coverage control for a multi-agent system is simplified. Simulation results reveal that the dynamic cooperative game coverage algorithm based on the virtual repulsive force can effectively realize the coverage control requirement of the moving agent system under the incomplete information condition and can remarkably reduce the cost of coverage control. In addition, compared with several existing coverage algorithms, using the dynamic cooperative game coverage algorithm proposed in this paper requires fewer moving times of agents while obtaining a higher coverage rate.

Reflections on the Design of Systems that Impact Computers and Society

Having spent the past year post-retirement working with my alma mater, New Mexico State University's (NMSU) Computer Science department to broaden computing, increase student engagement, and to improve graduation completion, as well as reflecting on the state of computing in society at large, I thought I'd share some observations. In March of this year, I had the opportunity to participate in the SIGCSE 2022 Technical Symposium. I was struck by Dr. Shaundra Daily's plenary keynote, entitled "Diversifying Computing: Real Change Must Come from Within", and her use of the phrase "navigating systems that were not designed for me" as she described her exploration of STEM as a first-generation college student, as both a dance and an engineering student, and as a graduate student preparing for motherhood lacking flexibility during her pregnancy, no maternity leave, no livable stipend, and a lack of affordable childcare, as well as the coping strategies she needed to develop to deal with academic culture. In my work with NMSU this past spring, co-teaching a problem solving course, my work this fall advising CS students, and my board roles serving on the National Academy of Science, Engineering, and Medicine's Roundtable for Systemic Change in Undergraduate STEM Education co-chairing the "Culture of STEM" workgroup, on the Computing Alliance of Hispanic Serving Institution's (CAHSI) Advisory Board, and on the Computing Research Association for Widening Participation (CRA-WP), co-editing the "Expanding the Pipeline" column, it's clear that system design adversely impacts society in terms of determining not only who gets to participate in the design of computer hardware and software, but also who gets to advance in social and economic mobility. Academic institutions are complex systems in need of an overhaul, by the University of California's academic workers strike for better pay and benefits. The design and commercialization of AI without fully understanding the implications of bias and ethics is inherently a system design problem. The application to everything from AI generated art and images (and how to spot deep fakes), the ability of large language models (LLMs) to create volumes of text generated articles that appear legitimate with the capacity to spread hate and misinformation globally are but just a few examples of the potentially horrific impact to society, because humans cannot work at the pace and scale to validate and/or authenticate them, with few if any meaningful domestic and international laws or policies in place to safeguard us.

A Signal Flow Graph Approach to the Resolution of Spherical Triangles Using CORDIC

Jack Volder’s original motivation for the COordinate DIgital Computer (CORDIC) was the real-time digital solution of spherical triangle equations employed in airborne navigation, for which he presented a solution flow diagram without detailing its construction. In fact, without a strong guidance, it is not easy to express the solutions of linear algebraic problems such as those involved when solving spherical triangles as cascades of CORDIC operations—called by Volder CORDIC solution-flow diagrams—and thus to devise a system solution on CORDIC processing units. As it gives a bird’s eye view of the system design problem, a signal flow graph representation of the underlying system of linear equations provides such guidance; the operations leading to the problem solution are uncovered by performing a sequence of partial flow reversals on the graph. The approach is illustrated by the problem where two sides and the included angle of a spherical triangle are given, called SAS problem, that is encountered in applications as diverse as air navigation, lattice filters for adaptive processing, and dexterous robotic hands. The solutions thus obtained are at least as efficient as existing ones, whenever available.

Discrete gain scheduling control approach to elliptical orbit rendezvous system with actuator saturation

AbstractThis paper studies the discrete gain scheduling control design problem of elliptical orbit spacecraft rendezvous system with actuator saturation. Due to the presence of actuator saturation, the dynamic performance of the spacecraft rendezvous system degrades significantly. In order to improve the dynamic performance of the system, a discrete gain scheduling control approach is adopted to construct a group of time‐invariant ellipsoidal invariant sets, which can be used to determine the switching points of the discrete gain scheduling control. By choosing some discrete parameter values, the discrete gain scheduling control is obtained from a solution of a periodic Riccati matrix differential equation. Under the control obtained, the dynamic performance of the system is much improved while accomplishing successfully the rendezvous mission of the spacecraft. Finally, a practical example is provided to show the effectiveness of the proposed control design approach.

A mathematical programming model with present value method for optimum design of flexible cellular manufacturing systems

In this article, an integer mathematical programming model for the design problem of flexible cellular manufacturing systems is proposed. The developed mathematical programming model whose objective function is to minimize total design cost including cost of operating parts on machines, cost of using tools on machines, and cost of assigning employees to cells, also contains the present value method. Thus, it is ensured that the operational costs that occur along a certain time period are also taken into account. LINGO 19.0 optimization program software is used for the optimum solution of the integer mathematical programming model with the present value method whose objective function is to minimize the total design cost. In this article, the application of the model and the analysis related to the model are shown using a developed example problem. In addition, by ensuring the optimum design of flexible cellular manufacturing systems, the results of which alternative routes are used for processing parts, which machines are located in which cells, and which employees are assigned to which cells, are obtained. Then, a sensitivity analysis is presented to show the importance of alternative routes of parts.

Harnessing interpretable machine learning for holistic inverse design of origami

This work harnesses interpretable machine learning methods to address the challenging inverse design problem of origami-inspired systems. We established a work flow based on decision tree-random forest method to fit origami databases, containing both design features and functional performance, and to generate human-understandable decision rules for the inverse design of functional origami. First, the tree method is unique because it can handle complex interactions between categorical features and continuous features, allowing it to compare different origami patterns for a design. Second, this interpretable method can tackle multi-objective problems for designing functional origami with multiple and multi-physical performance targets. Finally, the method can extend existing shape-fitting algorithms for origami to consider non-geometrical performance. The proposed framework enables holistic inverse design of origami, considering both shape and function, to build novel reconfigurable structures for various applications such as metamaterials, deployable structures, soft robots, biomedical devices, and many more.

Key Technology of Intelligentized Welding Manufacturing and Systems Based on the Internet of Things and Multi-Agent

With the development of the Internet of Things (IoT), Big Data, Artificial Intelligence technology, and the emergence of modern information technologies such as intelligent manufacturing, welding systems are changing, and intelligentized welding manufacturing and systems (IWMS) utilizing these technologies are attracting attention from both academia and industry. This paper investigates sensing technology, multi-information sensor fusion technology, feature recognition technology, the quality prediction method, control method, and intelligent welding production line application in the IWMS. Combining IoT technology and multi-agent systems, a hierarchical structure model welding manufacturing system (IoT-MAS) in the form of “leader-following” was constructed. The multi-agent welding manufacturing system has the advantages of distribution, intelligence, internal coordination and so on. The IoT-MAS consists of several sub-agents, which are divided into five categories according to their functions and internal processing logic. Combined with the functions of the intelligent welding manufacturing system, the agent structure of the whole welding process was proposed, and the matching communication technology and algorithm were designed. The intelligent welding manufacturing system based on IoT-MAS proposed in this paper can effectively solve the integrated design problem of large welding manufacturing systems.

Role of Deep Learning in Wireless Communications

Traditional communication system design has always been based on the paradigm of first establishing a mathematical model of the communication channel, then designing and optimizing the system according to the model. The advent of modern machine learning techniques, specifically deep neural networks, has opened up opportunities for data-driven system design and optimization. This article draws examples from the optimization of reconfigurable intelligent surface, distributed channel estimation and feedback for multiuser beamforming, and active sensing for millimeter wave (mmWave) initial alignment to illustrate that a data-driven design that bypasses explicit channel modelling can often discover excellent solutions to communication system design and optimization problems that are otherwise computationally difficult to solve. We show that by performing an end-to-end training of a deep neural network using a large number of channel samples, a machine learning based approach can potentially provide significant system-level improvements as compared to the traditional model-based approach for solving optimization problems. The key to the successful applications of machine learning techniques is in choosing the appropriate neural network architecture to match the underlying problem structure.

Single- and multi-objective optimization of an aircraft hot-air anti-icing system based on Reduced Order Method

Geometric optimization of piccolo tube provides a cost-effective approach to shorten the design circle of hot-air anti-icing system and maximize the utilization efficiency of bleed air. An optimization methodology for aircraft hot-air anti-icing systems based on Reduced Order Method (ROM) is developed. ROM based on Proper Orthogonal Decomposition (POD) and Radial Basis Function neural network (RBF) is constructed to evaluate objective and constraint functions of single- and multi-objective optimizations. POD is adopted for data compression and characteristics extraction for the anti-icing performance snapshot matrix. Then, a lower-dimensional approximation for the snapshot matrix is derived from the projection subspace consisting of a set of basis modes. RBF neural network is introduced to construct the mapping between the design variables of piccolo tube geometric configuration samples and the linear combination coefficients of basis modes. The piccolo tube is parameterized by five design variables and is optimized by the developed methodology. The predicted results indicate that the constructed ROM can provide the distributions of surface temperature and runback water with high accuracy and computational efficiency. The optimal results indicate that the developed optimization methodology is efficient and reliable for handling single- and multi-objective design problems of aircraft piccolo tube hot-air anti-icing systems.

Stabilizing control design for state-dependent impulsive dynamical linear systems

The stabilizing control design problem for state-dependent impulsive dynamical linear systems (SD-IDLS) is tackled in this paper. This class of systems consists of a continuous-time, linear time-invariant system combined with discrete-time linear time-invariant dynamics in a prescribed region of the state space. The former regulates the evolution of the system between any two consecutive resetting events, while the latter governs the instantaneous state jumps occurring whenever the system trajectory intersects a resetting set, which is a portion of the state space assumed to be time-independent. Stabilization of SD-IDLS through a state feedback control design is specifically discussed in this paper, by making use of a candidate quadratic control Lyapunov function. By considering the conical hulls of the resetting set subregions and imposing some cone copositivity properties on the chosen control Lyapunov function, a sufficient and constructive condition for the global asymptotic stabilization of SD-IDLS is provided. Such a result, based on the solution of a feasibility problem that involves a set of Linear Matrix Inequalities (LMIs), is shown to be less conservative and more numerically amenable with respect to other results available in the literature. An example illustrates the effectiveness of the proposed approach.

A variable gain impulsive observer for Lipschitz nonlinear systems with measurement noises

This paper investigates the variable gain impulsive observer design problem for Lipschitz nonlinear systems. It is assumed that the measurements are contaminated by noise and received by observer at aperiodic instants. To establish a tractable design condition for impulsive observers, the piecewise linear interpolation method is used to construct the variable gain function. To quantify the impact of the measurement noises and exogenous disturbance on the estimation error, a Lyapunov-based condition for establishing exponential input-to-state stability (EISS) property of the observation error dynamics is presented. Then it is shown that the EISS condition can be expressed as a set of linear matrix inequalities (LMIs) by introducing a piecewise quadratic Lyapunov function. A convex optimization problem is proposed in which the EISS gain is minimized. Comparisons with the existing methods show the effectiveness of the proposed design technique.

Fully distributed adaptive time-varying formation control of singular multiagent systems

This paper addresses the fully distributed adaptive time-varying formation protocols design problem for singular multiagent systems with directed topology. The pinning control method is introduced into the design of the adaptive distributed protocols, which can effectively adjust the formation position among agents and greatly relax the constraints of the communication topology among agents. Then, the distributed algorithm that independent of the global information of the communication graph is designed to solve the proposed fully distributed protocols. Next, the sufficient conditions of time-varying formation control are provided by the feedback pulse elimination method and stability analysis. Finally, the time-varying formation simulation results are presented to verify the feasibility of the proposed theory.

Multi‐loop networked control system design subject to interchange attack

AbstractThis paper deals with the design problem of multiloop networked control system (NCS) under interchange attack. This is a new type of attack where the transmitted data are swapped between different control loops which can be more destructive than other types of attacks, such as denial‐of‐service (DoS) attack. In this context, multiple sampled‐data control loops are first considered that use a shared communication network for data transmission. Then, the overall closed‐loop systems with the proposed attack are formulated in a unified stochastic delay system. The proposed framework is also applicable to DoS attack. Based on Lyapunov–Krasovskii theory and stochastic analysis techniques, some conditions in terms of linear matrix inequalities (LMIs) are derived for the control design problem. Finally, two inverted pendula are utilized for simulation to show the effectiveness of the proposed method. Also, the effects of DoS attack and interchange attack are compared.

Event-Triggered Tracking Control of Networked Multiagent Systems

This article studies the tracking control problem of networked multiagent systems under both multiple networks and event-triggered mechanisms. Multiple networks are to connect multiple agents and reference systems with decentralized controllers to guarantee their information transmission, whereas the event-triggered mechanisms are to reduce the information transmission via the networks. In this article, each agent has a network to communicate with its controller and reference system, and all networks are independent and asynchronous and have local event-triggered mechanisms, which are based on local measurements and determine whether the local measurements need to be transmitted via the corresponding network. To address this scenario, we first implement the emulation-based approach to develop a novel hybrid model for the tracking control of networked multiagent systems. Next, sufficient conditions are derived and decentralized event-triggered mechanisms are designed to guarantee the desired tracking performance. Furthermore, the proposed approach is applied to derive novel results for the event-triggered observer design problem of networked multiagent systems. Finally, two numerical examples are presented to illustrate the validity of the developed results.

Integration of Deep Learning and Information Theory for Designing Monitoring Networks in Heterogeneous Aquifer Systems

AbstractGroundwater monitoring networks are direct sources of information for revealing subsurface system dynamic processes. However, designing such networks is difficult due to uncertainties in the spatial heterogeneity of aquifer parameters such as permeability (k). This study combines deep learning and information theory with an optimization framework to address network design problems in heterogeneous aquifer systems. The framework first employs a generative adversarial network to parameterize heterogeneous k distribution using a low‐dimensional latent representation. Then, surrogate models are developed based on the deep neural networks to perform uncertainty quantification of pressure heads and solute concentrations at locations of pre‐designed candidate monitoring stations. The monitoring stations are then ranked using the greedy search algorithm based on the maximum information minimum redundancy (MIMR) criterion. In order to depict the importance of each candidate monitoring location, the hotspot maps of the selection probability (Ps) are derived from MIMR repetition results. Comprehensive monitoring networks derived from the hotspot maps are then conducted as the final monitoring stations to improve monitoring information compared to MIMR results. Additionally, nine entropy quantization strategies are compared to evaluate their effects on monitoring network optimization results. Results indicate that caution should be taken when selecting entropy quantization strategies to achieve the accuracy required for model calibration and to improve the efficiency of monitoring optimization. Considering high‐dimensional uncertainties associated with aquifer parameters, the developed framework can provide important insights for monitoring network designs in various earth observational projects.

Optimization based on movable damped wave algorithm for design of photovoltaic/ wind/ diesel/ biomass/ battery hybrid energy systems

The actual energetic situation has several challenges such as pollution, the rarefaction of fossil fuel and the dangers of nuclear. Renewable sources are proposed as a solution and suggested, such as a cost-effectiveness system. The paper deals with the problem of feeding a domestic load with electricity which should respect the ecologies factors, so this work is a design problem of the hybrid renewable energy systems; PV/biomass, PV/diesel/battery, PV/wind/diesel/battery, and wind/diesel/battery to choose the best one of them which feed the load with the lowest cost. The study’s goal is to design a microgrid system by the minimization of the total investment cost with respect to the required technical factor, the minimum allowed renewable energy fraction, and the minimum allowed availability factor. The methodology flowed utilizes frameworks based on a recent algorithm called Movable damped wave algorithm (MDVP). The proposed optimization algorithm is compared with other algorithms to prove its efficacy which are; the artificial electric field algorithm (AEFA), harris hawks optimization (HHO), and the grey wolf optimizer (GWO). The project case study is investigated in Al-Majmaah, Saudi Arabia. The contribution of this work is implementing a recent algorithm that proves its efficacy and finding the best microgrid configuration following many investigations and comparisons. The results confirm that the MDVP is better compared to the other algorithms, its computational time is fast, and its convergence is good; otherwise, the PV/biomass is considered the best configuration in the area of study with a size of 237.698 m2 from PV panel and 954.097 t/year of biomass, which obtained the best Net Present Cost (NPC) of $299504, and a cost of energy (LCOE) assumed as $0.228/kW. A sensitivity analysis is applied to prove the effect of size variation on project factors. The simple observation, by the way, is that any change in the PV size affects the output factors.