Research Articles
Power systems
Mehran Haghighi; Hamid Karimi; Shahram Jadid
Abstract
A home energy management system optimizes the electrical demand of household appliances according to price-based demand response programs. In this paper, we proposed a price-based demand response for a smart home with different types of appliances according to customer satisfaction, which also used electric ...
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A home energy management system optimizes the electrical demand of household appliances according to price-based demand response programs. In this paper, we proposed a price-based demand response for a smart home with different types of appliances according to customer satisfaction, which also used electric and thermal storage systems. In the proposed method, various appliances were considered in the smart home modeled by the energy hub system. A multi-objective daily management is proposed which considered electricity costs and customer satisfaction simultaneously to provide comprehensive management for smart homes. This paper presents a multi-objective optimization approach that not only minimizes operating costs and maximizes customer satisfaction but also reduces environmental impacts through emission reduction, creating a more sustainable energy management system. After solving the multi-objective problem, the technique for order preference by similarity to the ideal solution was used to rank the solutions, considering the preference of the decision-maker. The proposed model investigates the response of the smart home energy system in different conditions. Also, stochastic optimization was applied to model the probabilistic nature of demands, PV, and wind energy. The simulation results demonstrate that the proposed method reduces the consumer dissatisfaction by 79.9% and reduces the range from 199 to 40
Research Articles
Control
Naser Taghva Manesh; Ali Madady
Abstract
This paper introduces a novel optimal iterative learning control scheme for continuous-time systems with multiple-inputs and multiple-outputs and linear time-varying dynamics. While iterative learning control has been extensively studied in the discrete-time domain, the development of optimal iterative ...
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This paper introduces a novel optimal iterative learning control scheme for continuous-time systems with multiple-inputs and multiple-outputs and linear time-varying dynamics. While iterative learning control has been extensively studied in the discrete-time domain, the development of optimal iterative learning control for continuous-time systems remains limited due to the lack of lifted-formulations and associated mathematical challenges. The proposed method transforms the original optimal iterative learning control problem into a linear quadratic tracking-like problem, enabling the derivation of an explicit close-loop control law that ensures both tracking performance and control effort minimization. Unlike many existing approaches that rely on learning algorithms involving derivative terms, which are often sensitive to measurement noise, the proposed design avoids such terms and remains computationally efficient. Moreover, the monotonic convergence of the tracking error and the associated cost function are proved by rigorous mathematical analysis. Theoretical results are supported by four comprehensive simulation examples, including comparisons with several existing iterative learning control methods. Quantitative evaluations confirm that the proposed optimal scheme significantly outperforms previous techniques in terms of convergence speed and error reduction rate. This contribution offers a new framework for the optimal control of continuous-time systems with multiple inputs and outputs in repetitive tasks and provides a foundation for future extensions to constrained, nonlinear, or partially measurable systems.
Research Articles
Optimization
Shahpour Rahmani; Nasser Yazdani
Abstract
High-speed rail systems, operating at speeds up to 350 km/h, face significant challenges in delivering reliable network connectivity due to frequent handovers, signal degradation, and network congestion. This paper proposes the 5G-R framework, an optimized solution integrating beamforming, network slicing, ...
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High-speed rail systems, operating at speeds up to 350 km/h, face significant challenges in delivering reliable network connectivity due to frequent handovers, signal degradation, and network congestion. This paper proposes the 5G-R framework, an optimized solution integrating beamforming, network slicing, railway-specific Long Short-Term Memory (LSTM) algorithms, and Multi-Access Edge Computing (MEC) to enhance connectivity performance. By leveraging real-time train data, such as speed and GPS location, the framework optimizes handover prediction and traffic management, achieving robust performance in diverse environments. Compared to 4G LTE and standard 5G, the 5G-R framework demonstrates significant improvements, including a 250 Mbps throughput, 15 ms latency, and 95% handover success rate. Network slicing optimizes resource allocation, reducing congestion by approximately 30%, while MEC enables low-latency control for train systems. Field trials along the Beijing-Zhangjiakou railway (174 km, urban/suburban) and simulations validate the framework’s adaptability across urban and rural routes. Designed for compatibility with the Future Railway Mobile Communication System (FRMCS), the 5G-R framework lays a foundation for future advancements, including 6G and satellite communications. Future research should focus on optimizing performance in extreme environments and densely populated routes to support autonomous transport systems. This optimization-driven approach establishes a scalable model for next-generation rail communication systems.
Research Articles
Control
Maryam Shahriari-kahkeshi; Seyed Hojat Nourian
Abstract
This work proposes an adaptive resilient control for uncertain nonlinear cyber-physical systems (CPSs) under deception attacks. It is assumed that attacker injects false data into the commands exchanged between the controller and actuator over the communication channels. The injected false data affects ...
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This work proposes an adaptive resilient control for uncertain nonlinear cyber-physical systems (CPSs) under deception attacks. It is assumed that attacker injects false data into the commands exchanged between the controller and actuator over the communication channels. The injected false data affects the control input in both additive and multiplicative forms. To deal with the uncertain dynamics of the system and additive term of cyber-attacks, the radial basis function-neural networks (RBF-NNs) are invoked. Also, to handle adverse effects of multiplicative term of cyber-attack, the Nussbaum-type gain function is employed. Then, by integrating the RBF-NN model and Nussbaum function into the command filtered backstepping (CFB) approach, the proposed resilient control scheme is designed. Compared with the existing works, the proposed control eliminates the “explosion of complexity” problem in the conventional backstepping approach, removes the trial and error in choosing time constant of the first order filters in the dynamics surface control (DSC) approach, compensates the filtering error and deals with both additive and multiplicative cyber-attacks in “controller to actuator” channel, simultaneously. Also, it mitigates the effects of the cyber-attack without requiring separate attack estimation unit, controller reconfiguration or readjustment algorithm. Simulation results on the robotic arm under different cyber-attacks verify effective resilient performance of the proposed control scheme.
Research Articles
Optimization
Mahdi Alinaghizadeh Ardestani; Parham Parham Haji Ali Mohamadi
Abstract
Low back pain and spinal disorders are widespread issues affecting individuals globally, often requiring effective rehabilitation methods. This paper proposes a cable-driven parallel robot designed to assist in rehabilitation by moving patients' legs along frontal and sagittal axes. A novel Current Iterative ...
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Low back pain and spinal disorders are widespread issues affecting individuals globally, often requiring effective rehabilitation methods. This paper proposes a cable-driven parallel robot designed to assist in rehabilitation by moving patients' legs along frontal and sagittal axes. A novel Current Iterative Learning Control (CILC) algorithm is introduced to enhance the system's precision and reliability. The CILC ensures the convergence of system states and outputs to desired trajectories, maintaining bounded tracking errors even under disturbances, noise, or initial condition inaccuracies. Simulations demonstrate the controller's effectiveness when applied to the robotic structure, highlighting its potential for accurate and robust rehabilitation applications. By addressing challenges such as system nonlinearity and external uncertainties, the proposed solution offers a promising advancement in electromechanical rehabilitation equipment. This innovation not only improves patient outcomes but also provides a cost-effective and adaptable tool for diverse therapeutic needs. The integration of advanced control strategies with robotic systems marks a significant step forward in spinal rehabilitation technology.
Research Articles
Control
Valiollah Ghaffari
Abstract
The pursuit of time-optimal performance is a fundamental objective for high-speed robotic and mechatronic systems, where minimizing settling-time directly enhances throughput and operational efficiency. Despite its importance, deriving exact solutions for practical systems remains a formidable challenge. ...
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The pursuit of time-optimal performance is a fundamental objective for high-speed robotic and mechatronic systems, where minimizing settling-time directly enhances throughput and operational efficiency. Despite its importance, deriving exact solutions for practical systems remains a formidable challenge. Existing methodologies, which predominantly rely on numerical optimization or simplified plant models, often yield suboptimal results; they fail to provide guarantees of global optimality and frequently prove inadequate when confronting realistic system constraints such as actuator saturation. To address this critical gap, this paper introduces an analytical framework for synthesizing time-optimal control laws for a class of second-order systems. The proposed method is based on phase portrait analysis, a powerful geometric approach that facilitates the direct derivation of the exact switching curve. This curve is the critical element that defines the globally optimal, bang-bang controller. The final control law is presented in a closed-form expression, thereby enabling computationally efficient and straightforward implementation. Simulation results validate the theoretical framework, demonstrating that the proposed controller consistently achieves the theoretical performance by tracking the optimal trajectory perfectly.
Research Articles
Power systems
Ali Beiranvand; MahmoudReza Shakarami; Meysam Doostizadeh
Abstract
Today, power system operators face two major challenges: (1) determining the flexibility region for generators due to the increasing penetration of renewable generation in power systems, and (2) the rise in ambient temperature. Structural limitations in a power network and changes in its topology significantly ...
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Today, power system operators face two major challenges: (1) determining the flexibility region for generators due to the increasing penetration of renewable generation in power systems, and (2) the rise in ambient temperature. Structural limitations in a power network and changes in its topology significantly affect the generator's flexibility region. In this paper, in addition to describing the structure of the flexibility region, the variability of these regions due to changes in the network topology is thoroughly demonstrated. For this purpose, the problem of determining the flexibility region of the generators (GFR) is integrated with the Optimal Transmission Switching (OTS) and Dynamic Line Rating (DLR) problems to develop the flexibility region and maximize the use of renewable power generation. To reduce the computational time and increase the effectiveness of the proposed method, an innovative method (referred to as Radar Scanning) is used to categorize the data related to the changes in the output power of renewable generation units. To evaluate the proposed method, the IEEE 30-bus and 118-bus power systems are employed, and the numerical results obtained from the simulation show that: first, OTS and DLR can enhance and improve the flexibility region of the generators in a power system; and second, by employing the proposed algorithm, the flexibility region of 30- and 118-bus power systems are determined by examining only a limited number of data (less than 30%), resulting in a 92% and 89% reduction in computational time, respectively.
Research Articles
Power systems
Hamid Reza Sezavar
Abstract
This paper presents a comparative study on the application of artificial intelligence for optimizing External Lightning Protection Systems (ELPS) in photovoltaic power (PV) plants. The research addresses the critical need for advanced protection systems in solar installations, which are particularly ...
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This paper presents a comparative study on the application of artificial intelligence for optimizing External Lightning Protection Systems (ELPS) in photovoltaic power (PV) plants. The research addresses the critical need for advanced protection systems in solar installations, which are particularly vulnerable to lightning strikes due to their expansive outdoor configurations. Through a detailed comparative analysis, the study evaluates multiple AI approaches, including metaheuristic algorithms and machine learning models. The investigation reveals that metaheuristic algorithms often have lower accuracy compared to modern AI techniques. All comparisons are based on a multi-level optimization framework, systematically addressing air termination design, grounding system configuration, and overall system integration. The results show superiority in sensitivity analysis in the transformer model. Compared to other models, the random forest (RF) model, along with the artificial neural network (ANN) model, has a higher speed in data analysis. However, physics-informed neural networks (PINN) achieve remarkable improvements, delivering 93% protection coverage with only 3.2% grounding error while significantly reducing design convergence times.
Research Articles
Computer science
Farzaneh Jahanshahi Javaran; Somayyeh Jafarali Jassbi; Hossein Khademolhosseini; Razieh Farazkish
Abstract
As the field of nanotechnology rapidly advances and the need for faster processing in smaller dimensions grows, as does the integration of Very Large-Scale Integration (VLSI) technology. These difficulties include things like large-scale area needs, high power consumption, and low operating speeds, which ...
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As the field of nanotechnology rapidly advances and the need for faster processing in smaller dimensions grows, as does the integration of Very Large-Scale Integration (VLSI) technology. These difficulties include things like large-scale area needs, high power consumption, and low operating speeds, which call for new approaches to lessen these constraints. Developed and implemented at the nano-based, Quantum-Dot Cellular Automata (QCA) technology presents itself as a viable way around these obstacles. The advent of QCA technology heralds our entry into the nano-scale realm, where the advantages of enhanced processing speeds, reduced dimensions, and minimal power consumption become manifest. This article focuses on the 7-input majority gate, a fundamental component in QCA technology, distinguished by its fault-tolerant characteristics. The primary objective is to present the design and simulation of this key gate within the context of QCA technology. Noteworthy among the merits of the 7-input majority gate is its capacity to implement logic gates with a greater number of inputs, consolidating multiple functionalities within a single gate. QCADesigner and QCAPro software have simulated the given gate, and the results demonstrate the exact and correct operation of the gate. This gate generates the output signal every 0.25 clock cycles and is built with 66 quantum cells within a 0.03 µm2. The simulation results demonstrate the precision of the circuit's operation. Additionally, basic fault-tolerant gates such as 4-input AND, and 4-input OR using a 7-input fault-tolerant majority gate have been suggested in order to illustrate the proper operation of the new gate.
Research Articles
Computer science
Shahin Pourbahrami
Abstract
In many data mining problems, leveraging structural and local connectivity information can significantly improve clustering performance. This paper presents a novel semi-supervised clustering framework that integrates weighted feature information, Delaunay-based graph construction, and pairwise constraints. ...
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In many data mining problems, leveraging structural and local connectivity information can significantly improve clustering performance. This paper presents a novel semi-supervised clustering framework that integrates weighted feature information, Delaunay-based graph construction, and pairwise constraints. First, feature weights are computed based on within-class pairwise variability, emphasizing dimensions that contribute most to local cluster structure. Weighted distances between samples are then calculated, and a Delaunay graph is constructed and filtered using an influence radius, preserving meaningful local geometric relationships while removing redundant edges. To capture higher-order neighborhood information, a GraphSAGE-style embedding propagates feature information through the graph, generating enriched low-dimensional representations of the data. Pairwise constraints are incorporated into the similarity matrix to encode prior knowledge about sample relationships, guiding the clustering process. Finally, semi-supervised clustering is performed using constraint-based spectral clustering. Experiments on benchmark datasets demonstrate that the combination of structural graph information, feature weighting, and pairwise constraints substantially improves clustering accuracy. The proposed framework is flexible and can be effectively applied across diverse data domains.
