Pub Date : 2025-01-01Epub Date: 2025-12-22DOI: 10.1016/j.ifacol.2025.12.275
Rasoul Milasi
This paper presents the design and control of a solar photovoltaic (PV) converter system connected to a three-phase AC grid. The system operates in parallel with a local load, enabling power sharing between the PV system and the utility grid. An advanced Maximum Power Point Tracking (MPPT) algorithm, incorporating an emotional controller, ensures efficient power extraction and adaptive response to varying solar irradiance and load conditions. The proposed control strategy ensures maximum extraction of active power from the solar PV panel while simultaneously compensating for the reactive power demand of the AC load. This enables the three-phase AC source to operate at unity power factor. A Hardware-in-the-Loop (HIL) implementation validates the controller’s robust performance under varying load conditions and fluctuating solar irradiance.
{"title":"Emotion-Inspired Control for MPPT and Power Factor Correction in a Photovoltaic Converter System Connected to a Three-Phase AC Grid","authors":"Rasoul Milasi","doi":"10.1016/j.ifacol.2025.12.275","DOIUrl":"10.1016/j.ifacol.2025.12.275","url":null,"abstract":"<div><div>This paper presents the design and control of a solar photovoltaic (PV) converter system connected to a three-phase AC grid. The system operates in parallel with a local load, enabling power sharing between the PV system and the utility grid. An advanced Maximum Power Point Tracking (MPPT) algorithm, incorporating an emotional controller, ensures efficient power extraction and adaptive response to varying solar irradiance and load conditions. The proposed control strategy ensures maximum extraction of active power from the solar PV panel while simultaneously compensating for the reactive power demand of the AC load. This enables the three-phase AC source to operate at unity power factor. A Hardware-in-the-Loop (HIL) implementation validates the controller’s robust performance under varying load conditions and fluctuating solar irradiance.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 30","pages":"Pages 431-436"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145801973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2025-12-22DOI: 10.1016/j.ifacol.2025.12.226
Steven Evangelos , Nicholas Rubino , Victor H. Duenas
Hamstring spasticity in people with spinal cord injury (SCI) causes severe lower limb dysfunction. Static stretching is a primary treatment for spasticity. Active-Isolated stretching (AIS) is an effective strategy in rehabilitation and exercise science for reducing tension in overactive (i.e., spastic) muscles and improving range of motion (ROM). AIS is a more efficient lengthening technique due to its ability to bypass the myotactic (i.e., shortening) reflex that static stretching is subject to. Motivated to expand the scope of care for people with SCI, this paper develops novel closed-loop nonlinear controllers to facilitate AIS of the hamstrings. A robust electric motor kinematic controller is developed to move the leg from its initial supine position to the target end ROM by maintaining the leg within a kinematic range. When the leg reaches the end ROM, functional electrical stimulation (FES) is applied to the quadriceps to reciprocally inhibit (i.e., lengthen) the hamstrings, per AIS protocol. A custom FES controller tracks a desired active muscle torque using feedback from a load cell. An experiment was conducted to demonstrate safe control of AIS. A Lyapunov-based stability analysis is developed to guarantee exponential tracking of the motor and FES control loops. An output strictly passive condition is used to examine the influence of the FES-induced torque on the motor closed-loop error system when the leg is at the end ROM and both controllers are active.
{"title":"Closed-Loop Active-Isolated Hamstring Stretching with an Electric Motor and FES","authors":"Steven Evangelos , Nicholas Rubino , Victor H. Duenas","doi":"10.1016/j.ifacol.2025.12.226","DOIUrl":"10.1016/j.ifacol.2025.12.226","url":null,"abstract":"<div><div>Hamstring spasticity in people with spinal cord injury (SCI) causes severe lower limb dysfunction. Static stretching is a primary treatment for spasticity. Active-Isolated stretching (AIS) is an effective strategy in rehabilitation and exercise science for reducing tension in overactive (i.e., spastic) muscles and improving range of motion (ROM). AIS is a more efficient lengthening technique due to its ability to bypass the myotactic (i.e., shortening) reflex that static stretching is subject to. Motivated to expand the scope of care for people with SCI, this paper develops novel closed-loop nonlinear controllers to facilitate AIS of the hamstrings. A robust electric motor kinematic controller is developed to move the leg from its initial supine position to the target end ROM by maintaining the leg within a kinematic range. When the leg reaches the end ROM, functional electrical stimulation (FES) is applied to the quadriceps to reciprocally inhibit (i.e., lengthen) the hamstrings, per AIS protocol. A custom FES controller tracks a desired active muscle torque using feedback from a load cell. An experiment was conducted to demonstrate safe control of AIS. A Lyapunov-based stability analysis is developed to guarantee exponential tracking of the motor and FES control loops. An output strictly passive condition is used to examine the influence of the FES-induced torque on the motor closed-loop error system when the leg is at the end ROM and both controllers are active.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 30","pages":"Pages 138-143"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145801910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2025-12-22DOI: 10.1016/j.ifacol.2025.12.220
Satoshi Tsuruhara , Kazuhisa Ito
In recent years, adaptive identification methods that can achieve the true value convergence of parameters without requiring persistent excitation (PE) have been widely studied, and concurrent learning has been intensively studied. However, the parameter convergence rate is limited for the gradient-based method owing to small parameter update gain, and even the introduction of forgetting factors does not work sufficiently. To address this problem, this study proposes a novel discrete-time recursive least squares method under finite excitation (FE) conditions using two forgetting factors (inner and outer) and an augmented regressor matrix comprising a sum of regressor vectors. The proposed method ensures the PE condition of the augmented regressor matrix under FE conditions of the regressor vector and allows the properly design of the forgetting factor without estimator windup and/or destabilization of the system. Numerical simulations demonstrate its effectiveness by comparing it with several conventional methods.
{"title":"Discrete-time Two-Layered Forgetting RLS Identification under Finite Excitation⁎","authors":"Satoshi Tsuruhara , Kazuhisa Ito","doi":"10.1016/j.ifacol.2025.12.220","DOIUrl":"10.1016/j.ifacol.2025.12.220","url":null,"abstract":"<div><div>In recent years, adaptive identification methods that can achieve the true value convergence of parameters without requiring persistent excitation (PE) have been widely studied, and concurrent learning has been intensively studied. However, the parameter convergence rate is limited for the gradient-based method owing to small parameter update gain, and even the introduction of forgetting factors does not work sufficiently. To address this problem, this study proposes a novel discrete-time recursive least squares method under finite excitation (FE) conditions using two forgetting factors (inner and outer) and an augmented regressor matrix comprising a sum of regressor vectors. The proposed method ensures the PE condition of the augmented regressor matrix under FE conditions of the regressor vector and allows the properly design of the forgetting factor without estimator windup and/or destabilization of the system. Numerical simulations demonstrate its effectiveness by comparing it with several conventional methods.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 30","pages":"Pages 102-107"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145802178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2025-07-29DOI: 10.1016/j.ifacol.2025.07.069
Hongyu Liu , Hao Xia
This study constructs a multilayer network framework that captures both within and across market interactions, to measure the volatility spillovers among stocks listed across both Hong Kong and Mainland Chinese exchanges. We find that: (i) From a system-level perspective, the volatility network’s total connectedness reacts notably to major events, while the leader for cross-market spillovers is not constant but shifts with time. (ii) From an individual-level perspective, cross-listed stock pairs display varying spillover influence across and within markets; however, the bidirectional spillovers between them are generally symmetric and tend to be strengthened by the introduction of inter-market connectivity policies. This research offers an innovative approach for a deeper understanding of the connectedness characteristics of cross-listed stocks and for optimizing risk management decisions.
{"title":"Multilayer Information Spillover Networks: Application to Stocks Cross-listed in Mainland China and Hong Kong","authors":"Hongyu Liu , Hao Xia","doi":"10.1016/j.ifacol.2025.07.069","DOIUrl":"10.1016/j.ifacol.2025.07.069","url":null,"abstract":"<div><div>This study constructs a multilayer network framework that captures both within and across market interactions, to measure the volatility spillovers among stocks listed across both Hong Kong and Mainland Chinese exchanges. We find that: (i) From a system-level perspective, the volatility network’s total connectedness reacts notably to major events, while the leader for cross-market spillovers is not constant but shifts with time. (ii) From an individual-level perspective, cross-listed stock pairs display varying spillover influence across and within markets; however, the bidirectional spillovers between them are generally symmetric and tend to be strengthened by the introduction of inter-market connectivity policies. This research offers an innovative approach for a deeper understanding of the connectedness characteristics of cross-listed stocks and for optimizing risk management decisions.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 4","pages":"Pages 205-209"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144724135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2025-07-29DOI: 10.1016/j.ifacol.2025.07.067
A. Mohamed Messilem , Guido Carnevale , Ruggero Carli
In this paper, we consider a network of agents that jointly aim to minimize the sum of local functions subject to coupling constraints involving all local variables. To solve this problem, we propose a novel solution based on a primal-dual architecture. The algorithm is derived starting from an alternative definition of the Lagrangian function, and its convergence to the optimal solution is proved using recent advanced results in the theory of timescale separation in nonlinear systems. The rate of convergence is shown to be linear under standard assumptions on the local cost functions. Interestingly, the algorithm is amenable to a direct implementation to deal with asynchronous communication scenarios that may be corrupted by other non-idealities such as packet loss. We numerically test the validity of our approach on a real-world application related to the provision of ancillary services in three-phase low-voltage microgrids.
{"title":"Distributed Constraint-Coupled Optimization: Harnessing ADMM-consensus for robustness","authors":"A. Mohamed Messilem , Guido Carnevale , Ruggero Carli","doi":"10.1016/j.ifacol.2025.07.067","DOIUrl":"10.1016/j.ifacol.2025.07.067","url":null,"abstract":"<div><div>In this paper, we consider a network of agents that jointly aim to minimize the sum of local functions subject to coupling constraints involving all local variables. To solve this problem, we propose a novel solution based on a primal-dual architecture. The algorithm is derived starting from an alternative definition of the Lagrangian function, and its convergence to the optimal solution is proved using recent advanced results in the theory of timescale separation in nonlinear systems. The rate of convergence is shown to be linear under standard assumptions on the local cost functions. Interestingly, the algorithm is amenable to a direct implementation to deal with asynchronous communication scenarios that may be corrupted by other non-idealities such as packet loss. We numerically test the validity of our approach on a real-world application related to the provision of ancillary services in three-phase low-voltage microgrids.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 4","pages":"Pages 193-198"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144724133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we present a preliminary analysis of the convergence rate of the relaxed ADMM-based algorithm recently introduced in the literature for distributed optimization problems. It is well known that the relaxed ADMM is a more general algorithm than the classical ADMM. Indeed, while the performance of the latter typically depends on one parameter, say p, the performance of the former depends on two parameters, say p and a, and the relaxed ADMM reduces to the classical ADMM when α = 1/2. Interestingly, from a computational point of view, the presence of the additional parameter a does not significantly complicate the updating steps of the algorithm. Nevertheless, the relaxed ADMM is much less considered in distributed optimization problems than the classical ADMM. In this paper, we show that by restricting to quadratic functions with the same convexity and to communication graphs that are regular connected graphs, it is possible to analytically compute the eigenvalues of the matrix that governs the dynamics of the algorithm based on the relaxed ADMM. Based on these eigenvalues, it is possible to design an efficient numerical procedure to evaluate the rate of convergence and to optimise it with respect to both α and p. Our results show that, by properly tuning the a parameter, the relaxed ADMM can can achieve superior convergence rates compared to its classical ADMM counterpart; in particular, for the family of graphs we considered, the optimal a typically tends to 1 as the number of agents increases. The analysis we present is preliminary, but suggests that the use of the relaxed ADMM can significantly improve the performance of the classical ADMM when applied to distributed optimization problems, at the price of a slight increase in computational complexity.
{"title":"On the rate of convergence of distributed relaxed-ADMM algorithms in distributed optimization","authors":"Leonardo Armijos-Bacuilima , A. Mohamed Messilem , Luca Schenato , Ruggero Carli","doi":"10.1016/j.ifacol.2025.07.066","DOIUrl":"10.1016/j.ifacol.2025.07.066","url":null,"abstract":"<div><div>In this paper, we present a preliminary analysis of the convergence rate of the relaxed ADMM-based algorithm recently introduced in the literature for distributed optimization problems. It is well known that the relaxed ADMM is a more general algorithm than the classical ADMM. Indeed, while the performance of the latter typically depends on one parameter, say <em>p,</em> the performance of the former depends on two parameters, say <em>p</em> and a, and the relaxed ADMM reduces to the classical ADMM when <em>α =</em> 1/2. Interestingly, from a computational point of view, the presence of the additional parameter <em>a</em> does not significantly complicate the updating steps of the algorithm. Nevertheless, the relaxed ADMM is much less considered in distributed optimization problems than the classical ADMM. In this paper, we show that by restricting to quadratic functions with the same convexity and to communication graphs that are regular connected graphs, it is possible to analytically compute the eigenvalues of the matrix that governs the dynamics of the algorithm based on the relaxed ADMM. Based on these eigenvalues, it is possible to design an efficient numerical procedure to evaluate the rate of convergence and to optimise it with respect to both <em>α</em> and <em>p.</em> Our results show that, by properly tuning the <em>a</em> parameter, the relaxed ADMM can can achieve superior convergence rates compared to its classical ADMM counterpart; in particular, for the family of graphs we considered, the optimal <em>a</em> typically tends to 1 as the number of agents increases. The analysis we present is preliminary, but suggests that the use of the relaxed ADMM can significantly improve the performance of the classical ADMM when applied to distributed optimization problems, at the price of a slight increase in computational complexity.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 4","pages":"Pages 187-192"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144724132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2025-07-29DOI: 10.1016/j.ifacol.2025.07.059
Rajul Kumar, Ningshi Yao
We propose a novel bias-based consensus framework for nonlinear opinion dynamics. Due to the observable and malleable nature of bias in human-robot interactions, we utilize it as a control parameter to achieve consensus. First, we analyze the Lyapunov–Schmidt reduced system near equilibrium under small bias assumptions. Through constrained cusp bifurcation, we show that increasing individual biases beyond identified thresholds—and relative biases beyond saddle-node limit points ensures consensus with a unique stable equilibrium. For large biases, we conduct a global phase-plane analysis. By establishing strong monotonicity and applying the Poincaré–Bendixson theorem, we eliminate the possibility of limit cycles and guarantee consensus with a unique stable attractor as equilibrium. Finally, along with numerical simulations for the two-agent, two-option case, we show that the proposed bias control approach extends seamlessly to decentralized multi-agent opinion consensus.
{"title":"From Dissensus to Consensus: Bias-Controlled Transition in Nonlinear Opinion Dynamics⁎","authors":"Rajul Kumar, Ningshi Yao","doi":"10.1016/j.ifacol.2025.07.059","DOIUrl":"10.1016/j.ifacol.2025.07.059","url":null,"abstract":"<div><div>We propose a novel bias-based consensus framework for nonlinear opinion dynamics. Due to the observable and malleable nature of bias in human-robot interactions, we utilize it as a control parameter to achieve consensus. First, we analyze the Lyapunov–Schmidt reduced system near equilibrium under small bias assumptions. Through constrained cusp bifurcation, we show that increasing individual biases beyond identified thresholds—and relative biases beyond saddle-node limit points ensures consensus with a unique stable equilibrium. For large biases, we conduct a global phase-plane analysis. By establishing strong monotonicity and applying the Poincaré–Bendixson theorem, we eliminate the possibility of limit cycles and guarantee consensus with a unique stable attractor as equilibrium. Finally, along with numerical simulations for the two-agent, two-option case, we show that the proposed bias control approach extends seamlessly to decentralized multi-agent opinion consensus.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 4","pages":"Pages 145-150"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144724125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2025-07-29DOI: 10.1016/j.ifacol.2025.07.052
Iori Takaki , Ahmet Cetinkaya , Hideaki Ishii
In this paper, we consider a remote control problem based on data-driven control with an emphasis on communication constraints. Specifically, we propose a direct data-driven stabilization method with quantization in input and state data for unknown discrete-time linear systems. Moreover, the controller is designed taking account of the effects of quantization in the feedback data. Logarithmic type quantization is employed, and we show the inherent trade-off in the quantization coarseness for data-driven design and feedback control. We illustrate the effectiveness of the method through numerical simulations.
{"title":"Trade-off in Quantization Between Data-driven Design and Control Inputs⁎","authors":"Iori Takaki , Ahmet Cetinkaya , Hideaki Ishii","doi":"10.1016/j.ifacol.2025.07.052","DOIUrl":"10.1016/j.ifacol.2025.07.052","url":null,"abstract":"<div><div>In this paper, we consider a remote control problem based on data-driven control with an emphasis on communication constraints. Specifically, we propose a direct data-driven stabilization method with quantization in input and state data for unknown discrete-time linear systems. Moreover, the controller is designed taking account of the effects of quantization in the feedback data. Logarithmic type quantization is employed, and we show the inherent trade-off in the quantization coarseness for data-driven design and feedback control. We illustrate the effectiveness of the method through numerical simulations.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 4","pages":"Pages 103-108"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144724123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2025-07-29DOI: 10.1016/j.ifacol.2025.07.054
Liwei Yuan , Hideaki Ishii
We study resilient distributed parameter estimation in multi-agent systems where some agents may malfunction. The objective is for each nonfaulty agent to locally estimate its parameter while it may interact with adversaries. To this end, we develop an algorithm using multi-hop relaying to achieve the goal in multi-agent networks with directed topologies. With multi-hop relays, agents can access more information of remote agents even though they communicate with only direct neighbors. We characterize a necessary and sufficient graph condition for our algorithm to succeed, which is denoted by the notion of robust following graphs. We prove that our condition with multi-hop relays is more relaxed than the one with one-hop case, and hence, our approach can tolerate more adversaries in the same network when multi-hop relays are applied. Lastly, numerical examples verify the efficacy of our algorithm.
{"title":"Distributed Parameter Estimation with Adversaries via Multi-Hop Relays⁎","authors":"Liwei Yuan , Hideaki Ishii","doi":"10.1016/j.ifacol.2025.07.054","DOIUrl":"10.1016/j.ifacol.2025.07.054","url":null,"abstract":"<div><div>We study resilient distributed parameter estimation in multi-agent systems where some agents may malfunction. The objective is for each nonfaulty agent to locally estimate its parameter while it may interact with adversaries. To this end, we develop an algorithm using multi-hop relaying to achieve the goal in multi-agent networks with directed topologies. With multi-hop relays, agents can access more information of remote agents even though they communicate with only direct neighbors. We characterize a necessary and sufficient graph condition for our algorithm to succeed, which is denoted by the notion of robust following graphs. We prove that our condition with multi-hop relays is more relaxed than the one with one-hop case, and hence, our approach can tolerate more adversaries in the same network when multi-hop relays are applied. Lastly, numerical examples verify the efficacy of our algorithm.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 4","pages":"Pages 115-120"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144724120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2025-07-29DOI: 10.1016/j.ifacol.2025.07.044
Junkai Wang , Ziqiao Zhang , Fumin Zhang
In this paper, we develop a neural network-based method to study opinion behaviors under a covariance-based dissensus algorithm. Driven by this dissensus algorithm, the opinions are updated based on relative interactions and gradually converge to dissensus on the sphere. This proposed neural network-based method samples data and trains a neural network to ensure the Lyapunov conditions, which significantly simplifies the Lyapunov function design for stability analysis. The regions of attraction for different dissensus equilibria can also be estimated under opinion dynamics on a unit sphere by training a neural network to approximate the solution of Zubov’s equation. Simulations demonstrate the performance of the proposed method.
{"title":"Neural Network-based Stability Guarantee for Dissensus Opinion Behaviors on the Sphere⁎","authors":"Junkai Wang , Ziqiao Zhang , Fumin Zhang","doi":"10.1016/j.ifacol.2025.07.044","DOIUrl":"10.1016/j.ifacol.2025.07.044","url":null,"abstract":"<div><div>In this paper, we develop a neural network-based method to study opinion behaviors under a covariance-based dissensus algorithm. Driven by this dissensus algorithm, the opinions are updated based on relative interactions and gradually converge to dissensus on the sphere. This proposed neural network-based method samples data and trains a neural network to ensure the Lyapunov conditions, which significantly simplifies the Lyapunov function design for stability analysis. The regions of attraction for different dissensus equilibria can also be estimated under opinion dynamics on a unit sphere by training a neural network to approximate the solution of Zubov’s equation. Simulations demonstrate the performance of the proposed method.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 4","pages":"Pages 55-60"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144724568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号