Pub Date : 2025-07-29DOI: 10.1109/TCSI.2025.3588369
{"title":"IEEE Transactions on Circuits and Systems--I: Regular Papers Publication Information","authors":"","doi":"10.1109/TCSI.2025.3588369","DOIUrl":"https://doi.org/10.1109/TCSI.2025.3588369","url":null,"abstract":"","PeriodicalId":13039,"journal":{"name":"IEEE Transactions on Circuits and Systems I: Regular Papers","volume":"72 8","pages":"C2-C2"},"PeriodicalIF":5.2,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11099068","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144725223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-29DOI: 10.1109/TCSI.2025.3588371
{"title":"IEEE Transactions on Circuits and Systems--I: Regular Papers Information for Authors","authors":"","doi":"10.1109/TCSI.2025.3588371","DOIUrl":"https://doi.org/10.1109/TCSI.2025.3588371","url":null,"abstract":"","PeriodicalId":13039,"journal":{"name":"IEEE Transactions on Circuits and Systems I: Regular Papers","volume":"72 8","pages":"4403-4403"},"PeriodicalIF":5.2,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11099067","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144725268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes a trust-evaluation-based distributed model predictive control (DMPC) strategy for safe and scalable consensus of heterogeneous multi-agent systems (MASs) in a zero-trust environment. Dempster-Shafer theory (DST) is employed to quantify inter-agent trustworthiness and derive a global trust metric, thereby mitigating the impact of network threats and false testimonies during the trust evaluation process. Based on the computed trust, artificial reference trajectories are constructed to define the safe and scalable consensus state that each agent tracks, enabling adaptive regulation of reliance on neighbor information through real-time trust weights. The consensus problem is then reformulated as a trust-aware DMPC tracking problem that depends solely on locally received information, supporting distributed decision-making under zero-trust communication conditions. Sufficient conditions are derived to ensure recursive feasibility of the optimization problem, exponential stability of the closed-loop system, and the achievement of safe and scalable consensus. The effectiveness of the proposed strategy is validated via numerical simulations and vehicle platoon experiments.
{"title":"Distributed MPC for Safe and Scalable Consensus of Heterogeneous Multi-Agent Systems in a Zero-Trust Environment","authors":"Xiaotian Zhang;Defeng He;Xiulan Song;Haiping Du;Darong Huang","doi":"10.1109/TCSI.2025.3590249","DOIUrl":"https://doi.org/10.1109/TCSI.2025.3590249","url":null,"abstract":"This paper proposes a trust-evaluation-based distributed model predictive control (DMPC) strategy for safe and scalable consensus of heterogeneous multi-agent systems (MASs) in a zero-trust environment. Dempster-Shafer theory (DST) is employed to quantify inter-agent trustworthiness and derive a global trust metric, thereby mitigating the impact of network threats and false testimonies during the trust evaluation process. Based on the computed trust, artificial reference trajectories are constructed to define the safe and scalable consensus state that each agent tracks, enabling adaptive regulation of reliance on neighbor information through real-time trust weights. The consensus problem is then reformulated as a trust-aware DMPC tracking problem that depends solely on locally received information, supporting distributed decision-making under zero-trust communication conditions. Sufficient conditions are derived to ensure recursive feasibility of the optimization problem, exponential stability of the closed-loop system, and the achievement of safe and scalable consensus. The effectiveness of the proposed strategy is validated via numerical simulations and vehicle platoon experiments.","PeriodicalId":13039,"journal":{"name":"IEEE Transactions on Circuits and Systems I: Regular Papers","volume":"73 2","pages":"1367-1379"},"PeriodicalIF":5.2,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-28DOI: 10.1109/TCSI.2025.3590009
Feng Zhao;Zhikang Shuai;Wei Wang;Yuqing Liang;Lili He;Daming Wang
More and more distribution generators (DGs) are integrated into renewable energy systems, which increases the transient instability risk of systems. The Takagi-Sugeno (TS) fuzzy multimodeling method is usually applied in transient stability analysis of power electronic systems. However, the traditional TS fuzzy multimodeling method is based on the linear matrix inequalities (LMIs) calculations and attraction domains search with the fixed search directions and step size, which severely increases conservation and calculation burden while evaluating the stability of high-order systems. To fill this gap, a large signal stability analysis method based on attraction domains directionless search is proposed in this paper. Firstly, the drawbacks of the traditional TS fuzzy multimodeling method is analyzed. Secondly, an active attraction-domain-search-based (AADSB) Lyapunov function construction method is proposed. The attraction domain index (ADI) is defined in this paper. The proposed method constructs Lyapunov function by searching the maximum ADI of studied systems. The position of the operating point which is corresponded to the maximum ADI is used to calculate the candidate energy function of studied systems. Thirdly, simulations and experiments are proposed to verified the effectiveness of the proposed method. Compared with the traditional TS fuzzy multimodeling method, the proposed method is less conservative and has less parameter selection restrictions.
{"title":"An Active Attraction-Domain-Search-Based Lyapunov Construction Method in Power Electronic Systems","authors":"Feng Zhao;Zhikang Shuai;Wei Wang;Yuqing Liang;Lili He;Daming Wang","doi":"10.1109/TCSI.2025.3590009","DOIUrl":"https://doi.org/10.1109/TCSI.2025.3590009","url":null,"abstract":"More and more distribution generators (DGs) are integrated into renewable energy systems, which increases the transient instability risk of systems. The Takagi-Sugeno (TS) fuzzy multimodeling method is usually applied in transient stability analysis of power electronic systems. However, the traditional TS fuzzy multimodeling method is based on the linear matrix inequalities (LMIs) calculations and attraction domains search with the fixed search directions and step size, which severely increases conservation and calculation burden while evaluating the stability of high-order systems. To fill this gap, a large signal stability analysis method based on attraction domains directionless search is proposed in this paper. Firstly, the drawbacks of the traditional TS fuzzy multimodeling method is analyzed. Secondly, an active attraction-domain-search-based (AADSB) Lyapunov function construction method is proposed. The attraction domain index (ADI) is defined in this paper. The proposed method constructs Lyapunov function by searching the maximum ADI of studied systems. The position of the operating point which is corresponded to the maximum ADI is used to calculate the candidate energy function of studied systems. Thirdly, simulations and experiments are proposed to verified the effectiveness of the proposed method. Compared with the traditional TS fuzzy multimodeling method, the proposed method is less conservative and has less parameter selection restrictions.","PeriodicalId":13039,"journal":{"name":"IEEE Transactions on Circuits and Systems I: Regular Papers","volume":"73 3","pages":"2181-2194"},"PeriodicalIF":5.2,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147288187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-28DOI: 10.1109/TCSI.2025.3591558
Lisha Yan;Zhen Wang;Xia Huang;Hao Shen
This paper investigates the exponential synchronization of reaction-diffusion systems with communication delays and non-collocated sensor-actuator deployments. To mitigate the communication resource wastage inherent in traditional continuous-time control, a dwell-time-based event-triggered scheme is proposed, which effectively reduces the frequency of control signal updates. A limited number of sensors are spatially deployed to measure local average signals, and only valuable data are transmitted through the proposed event-triggered mechanism. To address communication delays induced by bandwidth constraints and locally distributed actuators, a spatiotemporal-dependent switching system is constructed. Subsequently, sufficient conditions for ensuring exponential synchronization are derived by constructing appropriate Lyapunov functionals that accommodate both collocated and non-collocated sensor-actuator configurations. Finally, numerical simulations and an image encryption application validate the effectiveness of the proposed approach. The results demonstrate that the method improves delay tolerance by allowing a larger sensor distribution ratio without modifying actuator placement, offering practical insights for the design of efficient and robust networked control systems.
{"title":"Dwell-Time-Based Event-Triggered Control for Reaction-Diffusion Systems With Non-Collocated Sensors and Actuators Over Delayed Networks","authors":"Lisha Yan;Zhen Wang;Xia Huang;Hao Shen","doi":"10.1109/TCSI.2025.3591558","DOIUrl":"https://doi.org/10.1109/TCSI.2025.3591558","url":null,"abstract":"This paper investigates the exponential synchronization of reaction-diffusion systems with communication delays and non-collocated sensor-actuator deployments. To mitigate the communication resource wastage inherent in traditional continuous-time control, a dwell-time-based event-triggered scheme is proposed, which effectively reduces the frequency of control signal updates. A limited number of sensors are spatially deployed to measure local average signals, and only valuable data are transmitted through the proposed event-triggered mechanism. To address communication delays induced by bandwidth constraints and locally distributed actuators, a spatiotemporal-dependent switching system is constructed. Subsequently, sufficient conditions for ensuring exponential synchronization are derived by constructing appropriate Lyapunov functionals that accommodate both collocated and non-collocated sensor-actuator configurations. Finally, numerical simulations and an image encryption application validate the effectiveness of the proposed approach. The results demonstrate that the method improves delay tolerance by allowing a larger sensor distribution ratio without modifying actuator placement, offering practical insights for the design of efficient and robust networked control systems.","PeriodicalId":13039,"journal":{"name":"IEEE Transactions on Circuits and Systems I: Regular Papers","volume":"73 3","pages":"2156-2169"},"PeriodicalIF":5.2,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147288191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study proposes a novel synchronization framework for memristive chaotic systems (MCSs) through an enhanced deep reinforcement learning (DRL) approach, featuring an improved proximal policy optimization (PPO) algorithm. Distinguished from traditional linear/nonlinear control paradigms that necessitate precise mathematical modeling, our DRL-based methodology operates without prior knowledge of system dynamics or analytical model requirements. The developed data-driven control strategy demonstrates significant advantages by reducing the required control forces from four to three dimensions, thereby substantially decreasing control complexity and operational costs compared to conventional item-by-item control methods. Through systematic optimization of the reward function architecture in classical PPO algorithms, we achieve accelerated synchronization convergence rates for MCSs, in which an optimal exponential parameter is obtained accordingly. Finally, the practical efficacy of our DRL-driven synchronization framework is successfully validated in image encryption applications. Comprehensive numerical simulations and comparative analyses demonstrate that the proposed methodology not only maintains robust performance under Gaussian noise perturbations but also achieves synchronization efficiency improvements.
{"title":"A Deep Reinforcement Learning Approach for Synchronization Between Two Memristor Chaotic Systems and Application for Image Encryption","authors":"Shitao Jin;Jie Chen;Jie Wu;Xiaofeng Wang;Xiaoli Luan;Junjie Fu;Guanghui Wen","doi":"10.1109/TCSI.2025.3592187","DOIUrl":"https://doi.org/10.1109/TCSI.2025.3592187","url":null,"abstract":"This study proposes a novel synchronization framework for memristive chaotic systems (MCSs) through an enhanced deep reinforcement learning (DRL) approach, featuring an improved proximal policy optimization (PPO) algorithm. Distinguished from traditional linear/nonlinear control paradigms that necessitate precise mathematical modeling, our DRL-based methodology operates without prior knowledge of system dynamics or analytical model requirements. The developed data-driven control strategy demonstrates significant advantages by reducing the required control forces from four to three dimensions, thereby substantially decreasing control complexity and operational costs compared to conventional item-by-item control methods. Through systematic optimization of the reward function architecture in classical PPO algorithms, we achieve accelerated synchronization convergence rates for MCSs, in which an optimal exponential parameter is obtained accordingly. Finally, the practical efficacy of our DRL-driven synchronization framework is successfully validated in image encryption applications. Comprehensive numerical simulations and comparative analyses demonstrate that the proposed methodology not only maintains robust performance under Gaussian noise perturbations but also achieves synchronization efficiency improvements.","PeriodicalId":13039,"journal":{"name":"IEEE Transactions on Circuits and Systems I: Regular Papers","volume":"73 2","pages":"1380-1393"},"PeriodicalIF":5.2,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-22DOI: 10.1109/TCSI.2025.3590605
Xiaozeng Xu;Yanzheng Zhu;Rongni Yang;Wei Xing Zheng;Jose de Jesus Rubio
In this paper, a new data-based Q-learning algorithm is proposed to address the optimal control issue for a class of discrete-time switched affine systems (SASs). The algorithm shifts the emphasis onto learning the optimal switching law directly from system input-output data, employing a neural-network-approximated Q-function as the key learning element. Firstly, the optimal control issue is transformed into solving the corresponding Bellman’s optimality equation based on the Q-function. Then, a new Q-learning algorithm is developed to find the optimal solution of system switching based entirely on the system input-output data, and a fully connected neural network is borrowed as the Q-function approximator. Moreover, considering the affine properties of SASs, the sequence of Q-functions generated remains bounded in proximity to the precise optimal solution. Finally, both the advantage and effectiveness of the proposed Q-learning based optimal control approach are verified by three examples, including a case study of DC-DC buck-boost converter.
{"title":"Q-Learning-Based Control for Discrete-Time Switched Affine Systems and Its Application to DC–DC Converter","authors":"Xiaozeng Xu;Yanzheng Zhu;Rongni Yang;Wei Xing Zheng;Jose de Jesus Rubio","doi":"10.1109/TCSI.2025.3590605","DOIUrl":"https://doi.org/10.1109/TCSI.2025.3590605","url":null,"abstract":"In this paper, a new data-based Q-learning algorithm is proposed to address the optimal control issue for a class of discrete-time switched affine systems (SASs). The algorithm shifts the emphasis onto learning the optimal switching law directly from system input-output data, employing a neural-network-approximated Q-function as the key learning element. Firstly, the optimal control issue is transformed into solving the corresponding Bellman’s optimality equation based on the Q-function. Then, a new Q-learning algorithm is developed to find the optimal solution of system switching based entirely on the system input-output data, and a fully connected neural network is borrowed as the Q-function approximator. Moreover, considering the affine properties of SASs, the sequence of Q-functions generated remains bounded in proximity to the precise optimal solution. Finally, both the advantage and effectiveness of the proposed Q-learning based optimal control approach are verified by three examples, including a case study of DC-DC buck-boost converter.","PeriodicalId":13039,"journal":{"name":"IEEE Transactions on Circuits and Systems I: Regular Papers","volume":"73 3","pages":"2206-2215"},"PeriodicalIF":5.2,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147288181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-22DOI: 10.1109/TCSI.2025.3588383
Yan Zhao;Min Meng;Xiuxian Li
This paper investigates multi-coalition games (MCGs) involving players with high-order dynamics, where their decisions satisfy both hard local set constraints and nonlinear coupled inequality constraints. The hard set constraints require that players’ decisions always satisfy the prescribed local set constraints. The coexistence of constraints and high-order dynamics leads to significant challenges for algorithm design and analysis, as the system inertia caused by high-order dynamics prevents direct control of the decisions of players through their inputs. To control the constrained high-order players to perform MCG tasks autonomously and distributedly, based on state feedback, leader-following consensus, dynamic average consensus, projection, and adaptive control methods, a fully distributed algorithm is proposed, where the decisions of all players are ensured to satisfy the set constraints all the time. It allows players to adjust their own parameters by using their procurable feedback errors without requiring direct access to others’ decisions or gradients, eliminating the need for global information and parameters tuning. Furthermore, the convergence of this algorithm is rigorously proven by nonsmooth analysis and Lyapunov stability theory. Finally, the algorithm is applied to the electricity market games (EMGs) of smart grids to demonstrate its effectiveness.
{"title":"Fully Distributed GNE Seeking for MCGs With High-Order Players and Hard Set Constraints","authors":"Yan Zhao;Min Meng;Xiuxian Li","doi":"10.1109/TCSI.2025.3588383","DOIUrl":"https://doi.org/10.1109/TCSI.2025.3588383","url":null,"abstract":"This paper investigates multi-coalition games (MCGs) involving players with high-order dynamics, where their decisions satisfy both hard local set constraints and nonlinear coupled inequality constraints. The hard set constraints require that players’ decisions always satisfy the prescribed local set constraints. The coexistence of constraints and high-order dynamics leads to significant challenges for algorithm design and analysis, as the system inertia caused by high-order dynamics prevents direct control of the decisions of players through their inputs. To control the constrained high-order players to perform MCG tasks autonomously and distributedly, based on state feedback, leader-following consensus, dynamic average consensus, projection, and adaptive control methods, a fully distributed algorithm is proposed, where the decisions of all players are ensured to satisfy the set constraints all the time. It allows players to adjust their own parameters by using their procurable feedback errors without requiring direct access to others’ decisions or gradients, eliminating the need for global information and parameters tuning. Furthermore, the convergence of this algorithm is rigorously proven by nonsmooth analysis and Lyapunov stability theory. Finally, the algorithm is applied to the electricity market games (EMGs) of smart grids to demonstrate its effectiveness.","PeriodicalId":13039,"journal":{"name":"IEEE Transactions on Circuits and Systems I: Regular Papers","volume":"73 1","pages":"606-617"},"PeriodicalIF":5.2,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-22DOI: 10.1109/TCSI.2025.3584951
Tse-Huang Lo;Shin-Hong Miao;Pei Yu Lai;Xiao Cheng Gao;Chan-Liang Wu;Chih-Wen Lu
This study proposes a micro-LED display driver with 1.15-transistor pixel circuits and current-driving mode pulse width modulation (PWM). A three-stage pass transistor pixel circuit was used to transmit a driving current to micro-LEDs to shorten their off-time and achieve an average of 1.15 transistors per pixel circuit. A 7,700-PPI $640 times 480$ micro-LED display driver operating in 8-bit current-driving mode PWM was designed and fabricated using standard 0.18-$mu $ m CMOS technology. A down counter with parallel load function was used in each channel of the column driver to generate PWM control signals. The results of the study indicate that the display driver chip has a die dimension of $3.64 times 4.82$ mm2 and a display area of $2.11times 1.58$ mm2. In addition, the maximum DNL and INL values are 0.73 and 0.97 least significant bits, respectively. Overall, the display driver consumed 92.9 mW of power at a frame rate of 240 Hz.
本研究提出一种采用1.15晶体管像素电路和电流驱动模式脉宽调制（PWM）的微型led显示驱动器。采用三级通型晶体管像素电路向微型led传输驱动电流，缩短了其关闭时间，实现了每像素电路平均1.15个晶体管。采用标准的0.18- $mu $ m CMOS技术，设计并制作了一个8位电流驱动PWM模式下的7700 ppi $640 × 480$微型led显示屏驱动器。在柱驱动器的每一个通道中都使用了一个具有并行负载功能的下行计数器来产生PWM控制信号。研究结果表明，该显示驱动芯片的芯片尺寸为3.64 × 4.82$ mm2，显示面积为2.11 × 1.58$ mm2。DNL的最大值为0.73位，INL的最大值为0.97位。总的来说，显示驱动器在240 Hz的帧速率下消耗了92.9 mW的功率。
{"title":"A 7700-PPI 640 × 480 Micro-LED Display Backplane Driver Chip With 1.15-Transistor Pixel Circuits by Using 0.18-μm CMOS Technology","authors":"Tse-Huang Lo;Shin-Hong Miao;Pei Yu Lai;Xiao Cheng Gao;Chan-Liang Wu;Chih-Wen Lu","doi":"10.1109/TCSI.2025.3584951","DOIUrl":"https://doi.org/10.1109/TCSI.2025.3584951","url":null,"abstract":"This study proposes a micro-LED display driver with 1.15-transistor pixel circuits and current-driving mode pulse width modulation (PWM). A three-stage pass transistor pixel circuit was used to transmit a driving current to micro-LEDs to shorten their off-time and achieve an average of 1.15 transistors per pixel circuit. A 7,700-PPI <inline-formula> <tex-math>$640 times 480$ </tex-math></inline-formula> micro-LED display driver operating in 8-bit current-driving mode PWM was designed and fabricated using standard 0.18-<inline-formula> <tex-math>$mu $ </tex-math></inline-formula>m CMOS technology. A down counter with parallel load function was used in each channel of the column driver to generate PWM control signals. The results of the study indicate that the display driver chip has a die dimension of <inline-formula> <tex-math>$3.64 times 4.82$ </tex-math></inline-formula> mm<sup>2</sup> and a display area of <inline-formula> <tex-math>$2.11times 1.58$ </tex-math></inline-formula> mm<sup>2</sup>. In addition, the maximum DNL and INL values are 0.73 and 0.97 least significant bits, respectively. Overall, the display driver consumed 92.9 mW of power at a frame rate of 240 Hz.","PeriodicalId":13039,"journal":{"name":"IEEE Transactions on Circuits and Systems I: Regular Papers","volume":"72 12","pages":"8523-8535"},"PeriodicalIF":5.2,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145600681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-18DOI: 10.1109/TCSI.2025.3588603
Jin Zhao;Mou Chen;Kenan Yong;Mihai Lungu
External disturbances in complex environments often have a serious impact on the control system performance. For the tracking control problem of an air-breathing hypersonic vehicle (AHV) longitudinal attitude system with unknown disturbances and multiple constraints, a dynamic event-triggered Lyapunov-based model predictive control (DET-LMPC) based on disturbance preview (DP) and control barrier function (CBF) scheme is proposed. To obtain the future disturbance information, DP is introduced to estimate the current and the future disturbances’ values, and used as the basis for designing predictive control technique. Then, a Lyapunov convergence constraint (LCC) imposed on the entire prediction horizon is given based on DP to ensure the stability of the closed-loop system. Moreover, to ensure that the AHV flies safely under disturbances, a dynamic predictive constraint for angle of attack is provided and realized by CBF added to the cost function. Furthermore, a dynamic event-triggered mechanism based on DP and LCC is taken into account to alleviate the calculation burden and maintain the anticipated tracking performance, simultaneously. The sufficient conditions ensuring the feasibility of the presented algorithm and the stability of the closed-loop system are analyzed. Finally, the simulation study demonstrates the effectiveness of the proposed DET-LMPC with DP and CBF scheme.
{"title":"Dynamic Event-Triggered Lyapunov-Based Model Predictive Control for AHV Under Disturbance and Multiple Constraints","authors":"Jin Zhao;Mou Chen;Kenan Yong;Mihai Lungu","doi":"10.1109/TCSI.2025.3588603","DOIUrl":"https://doi.org/10.1109/TCSI.2025.3588603","url":null,"abstract":"External disturbances in complex environments often have a serious impact on the control system performance. For the tracking control problem of an air-breathing hypersonic vehicle (AHV) longitudinal attitude system with unknown disturbances and multiple constraints, a dynamic event-triggered Lyapunov-based model predictive control (DET-LMPC) based on disturbance preview (DP) and control barrier function (CBF) scheme is proposed. To obtain the future disturbance information, DP is introduced to estimate the current and the future disturbances’ values, and used as the basis for designing predictive control technique. Then, a Lyapunov convergence constraint (LCC) imposed on the entire prediction horizon is given based on DP to ensure the stability of the closed-loop system. Moreover, to ensure that the AHV flies safely under disturbances, a dynamic predictive constraint for angle of attack is provided and realized by CBF added to the cost function. Furthermore, a dynamic event-triggered mechanism based on DP and LCC is taken into account to alleviate the calculation burden and maintain the anticipated tracking performance, simultaneously. The sufficient conditions ensuring the feasibility of the presented algorithm and the stability of the closed-loop system are analyzed. Finally, the simulation study demonstrates the effectiveness of the proposed DET-LMPC with DP and CBF scheme.","PeriodicalId":13039,"journal":{"name":"IEEE Transactions on Circuits and Systems I: Regular Papers","volume":"73 1","pages":"594-605"},"PeriodicalIF":5.2,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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