Pub Date : 2025-01-09DOI: 10.1109/TCST.2024.3520438
Jacob B. Fine;Carson M. McGuire;Vinson O. Williams;Michael Jenkins;Hannah McDaniel;Maya Keele;Matthew Bryant;Ashok Gopalarathnam;Chris Vermillion
This work presents an experimentally validated dynamic model, control trajectory optimization methodology, and representative simulation results for a morphing underwater kite. Morphing, defined as real-time modification of the kite’s geometry to either curtail structural loading or enhance power generation, is motivated by the fact that the optimal design of an energy-harvesting kite is highly sensitive to flow speed and tether length, particularly in the presence of structural limitations that render load curtailment necessary at high flow speeds and short tether lengths. To achieve morphing behavior, an inboard Fowler flap (capable of modifying the chord and camber of an inboard wing section) was employed in tandem with a symmetric aileron bias, enabling simultaneous control over both the wing’s overall lift coefficient and center of lift without requiring the mechanical complexity associated with span morphing. The effects of these morphing parameters were integrated into an existing dynamic simulation framework, and experiments were conducted using a customized scaled tow testing setup to refine and experimentally validate the simulation model. Following the refinement of this model, a morphing trajectory optimizer was designed to optimize the morphing input trajectories over a spooling cycle using flow data from the previous cycle. Finally, using the refined simulation model and multicycle controller, simulations of large-scale kites operating in a realistic flow environment were conducted. In these simulations, a kite capable of morphing was shown to generate between 8.1% and 25.3% more energy than non-morphing kite designs.
{"title":"Optimal Cyclic Control of a Structurally Constrained Morphing Energy-Harvesting Kite Using an Experimentally Validated Simulation Model","authors":"Jacob B. Fine;Carson M. McGuire;Vinson O. Williams;Michael Jenkins;Hannah McDaniel;Maya Keele;Matthew Bryant;Ashok Gopalarathnam;Chris Vermillion","doi":"10.1109/TCST.2024.3520438","DOIUrl":"https://doi.org/10.1109/TCST.2024.3520438","url":null,"abstract":"This work presents an experimentally validated dynamic model, control trajectory optimization methodology, and representative simulation results for a morphing underwater kite. Morphing, defined as real-time modification of the kite’s geometry to either curtail structural loading or enhance power generation, is motivated by the fact that the optimal design of an energy-harvesting kite is highly sensitive to flow speed and tether length, particularly in the presence of structural limitations that render load curtailment necessary at high flow speeds and short tether lengths. To achieve morphing behavior, an inboard Fowler flap (capable of modifying the chord and camber of an inboard wing section) was employed in tandem with a symmetric aileron bias, enabling simultaneous control over both the wing’s overall lift coefficient and center of lift without requiring the mechanical complexity associated with span morphing. The effects of these morphing parameters were integrated into an existing dynamic simulation framework, and experiments were conducted using a customized scaled tow testing setup to refine and experimentally validate the simulation model. Following the refinement of this model, a morphing trajectory optimizer was designed to optimize the morphing input trajectories over a spooling cycle using flow data from the previous cycle. Finally, using the refined simulation model and multicycle controller, simulations of large-scale kites operating in a realistic flow environment were conducted. In these simulations, a kite capable of morphing was shown to generate between 8.1% and 25.3% more energy than non-morphing kite designs.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 2","pages":"744-759"},"PeriodicalIF":4.9,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-06DOI: 10.1109/TCST.2024.3522793
Qianhong Zhao;Gang Tao
This article presents a study of the Koopman operator theory and its application to optimal control of a multiple-mobile-robot system. The operator, while operating on a set of observation functions of the state vector of a nonlinear system, produces a set of dynamic equations that, through a dynamic transformation, form a new dynamic system. The Koopman system technique is then applied to the development of a linear or bilinear model approximation of nonlinear utility functions for optimal control of a system of multiple (mobile) robots, by selecting the utility functions as the Koopman system state variables and expressing the set of Koopman variables as the state variables of a linear or bilinear system whose parameters are determined through optimization. An iterative algorithm is developed to estimate the parameters adaptively. Finally, the optimal control problems based on a linear or bilinear approximation model are formulated, by transforming the nonlinear programming problem to a linear programming problem. The above models are simulated on a three-mobile-robot system to verify their performance in both centralized and decentralized versions. From the simulation results, the bilinear model has more capacity to approximate the nonlinear utility functions. Both the centralized and decentralized bilinear approximation model-based control signals can achieve the control objective and are generated fast enough for real-time control.
{"title":"Koopman System Approximation-Based Optimal Control of Multiple Mobile Robots","authors":"Qianhong Zhao;Gang Tao","doi":"10.1109/TCST.2024.3522793","DOIUrl":"https://doi.org/10.1109/TCST.2024.3522793","url":null,"abstract":"This article presents a study of the Koopman operator theory and its application to optimal control of a multiple-mobile-robot system. The operator, while operating on a set of observation functions of the state vector of a nonlinear system, produces a set of dynamic equations that, through a dynamic transformation, form a new dynamic system. The Koopman system technique is then applied to the development of a linear or bilinear model approximation of nonlinear utility functions for optimal control of a system of multiple (mobile) robots, by selecting the utility functions as the Koopman system state variables and expressing the set of Koopman variables as the state variables of a linear or bilinear system whose parameters are determined through optimization. An iterative algorithm is developed to estimate the parameters adaptively. Finally, the optimal control problems based on a linear or bilinear approximation model are formulated, by transforming the nonlinear programming problem to a linear programming problem. The above models are simulated on a three-mobile-robot system to verify their performance in both centralized and decentralized versions. From the simulation results, the bilinear model has more capacity to approximate the nonlinear utility functions. Both the centralized and decentralized bilinear approximation model-based control signals can achieve the control objective and are generated fast enough for real-time control.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 3","pages":"963-979"},"PeriodicalIF":4.9,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Quadrotors that can operate predictably in the presence of imperfect model knowledge and external disturbances are crucial in safety-critical applications. We present $boldsymbol {mathcal {L}}_{1}$ Quad, a control architecture that ensures uniformly bounded transient response of the quadrotor’s uncertain dynamics on the special Euclidean group SE(3). By leveraging the geometric controller and the $boldsymbol {mathcal {L}}_{1}$ adaptive controller, the $boldsymbol {mathcal {L}}_{1}$ Quad architecture provides a theoretically justified framework for the design and analysis of quadrotor’s tracking controller in the presence of nonlinear (time- and state-dependent) uncertainties on both the translational and rotational dynamics. In addition, we validate the performance of the $boldsymbol {mathcal {L}}_{1}$ Quad architecture through extensive experiments for 11 types of uncertainties across various trajectories. The results demonstrate that the $boldsymbol {mathcal {L}}_{1}$ Quad can achieve consistently small tracking errors despite the uncertainties and disturbances and significantly outperforms existing state-of-the-art controllers.
{"title":"L1Quad: L1 Adaptive Augmentation of Geometric Control for Agile Quadrotors With Performance Guarantees","authors":"Zhuohuan Wu;Sheng Cheng;Pan Zhao;Aditya Gahlawat;Kasey A. Ackerman;Arun Lakshmanan;Chengyu Yang;Jiahao Yu;Naira Hovakimyan","doi":"10.1109/TCST.2024.3521182","DOIUrl":"https://doi.org/10.1109/TCST.2024.3521182","url":null,"abstract":"Quadrotors that can operate predictably in the presence of imperfect model knowledge and external disturbances are crucial in safety-critical applications. We present <inline-formula> <tex-math>$boldsymbol {mathcal {L}}_{1}$ </tex-math></inline-formula>Quad, a control architecture that ensures uniformly bounded transient response of the quadrotor’s uncertain dynamics on the special Euclidean group SE(3). By leveraging the geometric controller and the <inline-formula> <tex-math>$boldsymbol {mathcal {L}}_{1}$ </tex-math></inline-formula> adaptive controller, the <inline-formula> <tex-math>$boldsymbol {mathcal {L}}_{1}$ </tex-math></inline-formula>Quad architecture provides a theoretically justified framework for the design and analysis of quadrotor’s tracking controller in the presence of nonlinear (time- and state-dependent) uncertainties on both the translational and rotational dynamics. In addition, we validate the performance of the <inline-formula> <tex-math>$boldsymbol {mathcal {L}}_{1}$ </tex-math></inline-formula>Quad architecture through extensive experiments for 11 types of uncertainties across various trajectories. The results demonstrate that the <inline-formula> <tex-math>$boldsymbol {mathcal {L}}_{1}$ </tex-math></inline-formula>Quad can achieve consistently small tracking errors despite the uncertainties and disturbances and significantly outperforms existing state-of-the-art controllers.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 2","pages":"597-612"},"PeriodicalIF":4.9,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10820973","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-31DOI: 10.1109/TCST.2024.3521322
Thomas L. Dearing;John Hauser;Xudong Chen;Marco M. Nicotra;Christopher Petersen
In this work, an optimal spacecraft maneuver planner is developed for rest-to-rest attitude transfers using single gimbal control moment gyroscopes (CMGs). In contrast to conventional optimization approaches developed using simplified dynamical models, this work examines the optimal performance and unique control strategies available to a variable speed CMG array under comprehensive physical models for its dynamics and power consumption. This formulation employs a dynamical model which preserves the array’s (conservative) momentum exchange dynamics, a power model directly tracking the usage of the individual CMG motors, and typical operational safety constraints on input saturation, angular velocity, and camera exclusion cones. On average, the optimal control strategies produced under this comprehensive formulation present a 35% reduction in mean required electrical energy and a 44% reduction in maneuver time over the classic singularity robust (SR) control law. These improvements are observed to correlate with several specific control behaviors. To extend these improvements to practical spacecraft design restrictions, suggestions on how to reproduce these behaviors using existing feedback control methods are provided.
{"title":"Energy-Optimal Attitude Control Strategies With Control Moment Gyroscopes","authors":"Thomas L. Dearing;John Hauser;Xudong Chen;Marco M. Nicotra;Christopher Petersen","doi":"10.1109/TCST.2024.3521322","DOIUrl":"https://doi.org/10.1109/TCST.2024.3521322","url":null,"abstract":"In this work, an optimal spacecraft maneuver planner is developed for rest-to-rest attitude transfers using single gimbal control moment gyroscopes (CMGs). In contrast to conventional optimization approaches developed using simplified dynamical models, this work examines the optimal performance and unique control strategies available to a variable speed CMG array under comprehensive physical models for its dynamics and power consumption. This formulation employs a dynamical model which preserves the array’s (conservative) momentum exchange dynamics, a power model directly tracking the usage of the individual CMG motors, and typical operational safety constraints on input saturation, angular velocity, and camera exclusion cones. On average, the optimal control strategies produced under this comprehensive formulation present a 35% reduction in mean required electrical energy and a 44% reduction in maneuver time over the classic singularity robust (SR) control law. These improvements are observed to correlate with several specific control behaviors. To extend these improvements to practical spacecraft design restrictions, suggestions on how to reproduce these behaviors using existing feedback control methods are provided.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 3","pages":"1093-1100"},"PeriodicalIF":4.9,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
For safe navigation in dynamic uncertain environments, robotic systems rely on the perception and prediction of other agents. Particularly, in occluded areas, where cameras and light detection and ranging (LiDAR) give no data, the robot must be able to reason about the potential movements of invisible dynamic agents. This work presents a provably safe motion planning scheme for real-time navigation in an a priori unmapped environment, where occluded dynamic agents are present. Safety guarantees are provided based on the reachability analysis. Forward reachable sets associated with potential occluded agents, such as pedestrians, are computed and incorporated into planning. An iterative optimization-based planner is presented that alternates between two optimizations: nonlinear model predictive control (NMPC) and collision avoidance. The recursive feasibility of the MPC is guaranteed by introducing a terminal stopping constraint. The effectiveness of the proposed algorithm is demonstrated through simulation studies and hardware experiments with a TurtleBot robot equipped with a LiDAR system. The video of experimental results is also available at: https://youtu.be/OUnkB5Feyuk.
{"title":"OA-MPC: Occlusion-Aware MPC for Guaranteed Safe Robot Navigation With Unseen Dynamic Obstacles","authors":"Roya Firoozi;Alexandre Mir;Gadiel Sznaier Camps;Mac Schwager","doi":"10.1109/TCST.2024.3520462","DOIUrl":"https://doi.org/10.1109/TCST.2024.3520462","url":null,"abstract":"For safe navigation in dynamic uncertain environments, robotic systems rely on the perception and prediction of other agents. Particularly, in occluded areas, where cameras and light detection and ranging (LiDAR) give no data, the robot must be able to reason about the potential movements of invisible dynamic agents. This work presents a provably safe motion planning scheme for real-time navigation in an a priori unmapped environment, where occluded dynamic agents are present. Safety guarantees are provided based on the reachability analysis. Forward reachable sets associated with potential occluded agents, such as pedestrians, are computed and incorporated into planning. An iterative optimization-based planner is presented that alternates between two optimizations: nonlinear model predictive control (NMPC) and collision avoidance. The recursive feasibility of the MPC is guaranteed by introducing a terminal stopping constraint. The effectiveness of the proposed algorithm is demonstrated through simulation studies and hardware experiments with a TurtleBot robot equipped with a LiDAR system. The video of experimental results is also available at: <uri>https://youtu.be/OUnkB5Feyuk</uri>.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 3","pages":"940-951"},"PeriodicalIF":4.9,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-27DOI: 10.1109/TCST.2024.3520461
Zhaowu Ping;Junyi Zhang;Hongwei Zhang
The cooperative output regulation problem of nonlinear multiagent systems has been intensively investigated in the control community under a standard assumption that the exact solution of regulator equations associated with each agent is available. However, this assumption is not realistic for many practical applications. To remove such restriction, this brief further studies a cooperative approximate output regulation problem of nonlinear multiagent systems. A novel distributed neural network (NN) control law is proposed, which integrates a distributed observer and NN controller. In contrast with existing results, the proposed algorithm leads to the solution of the cooperative output regulation problem for a general class of nonlinear multiagent systems, where the exact solution of regulator equations associated with each agent is unavailable. The proposed algorithm is applied to a benchmark problem of cooperative control of multiple inverted pendulum on a cart (IPC) systems and is verified by both simulation and experimental results.
{"title":"Cooperative Approximate Output Regulation of Nonlinear Multiagent Systems: Theory and Application","authors":"Zhaowu Ping;Junyi Zhang;Hongwei Zhang","doi":"10.1109/TCST.2024.3520461","DOIUrl":"https://doi.org/10.1109/TCST.2024.3520461","url":null,"abstract":"The cooperative output regulation problem of nonlinear multiagent systems has been intensively investigated in the control community under a standard assumption that the exact solution of regulator equations associated with each agent is available. However, this assumption is not realistic for many practical applications. To remove such restriction, this brief further studies a cooperative approximate output regulation problem of nonlinear multiagent systems. A novel distributed neural network (NN) control law is proposed, which integrates a distributed observer and NN controller. In contrast with existing results, the proposed algorithm leads to the solution of the cooperative output regulation problem for a general class of nonlinear multiagent systems, where the exact solution of regulator equations associated with each agent is unavailable. The proposed algorithm is applied to a benchmark problem of cooperative control of multiple inverted pendulum on a cart (IPC) systems and is verified by both simulation and experimental results.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 3","pages":"1085-1092"},"PeriodicalIF":4.9,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-27DOI: 10.1109/TCST.2024.3508580
Jiakun Lei;Tao Meng;Dongyu Li;Kun Wang;Weijia Wang;Zhonghe Jin
This article addresses the challenge of achieving spacecraft attitude control with guaranteed performance while significantly reducing actuator activation frequency. To tackle this issue, we propose the concept of switched hybrid control and further integrate it with a modified prescribed-performance control (PPC) scheme. To enhance the robustness of the PPC control, we introduce the concept of a zeroing barrier function (ZBF). Coupled with a projection-operator-based modification dynamics, this approach assesses and adjusts the envelope in response to the risk of violating performance envelope constraints. Subsequently, a control mode switching strategy, considering the safety of the performance envelope and the system’s motion velocity, is proposed. This strategy automatically switches between intermittent and continuous control modes to select an appropriate control command execution strategy, thereby reducing actuator activation frequency under proper circumstances. Furthermore, we demonstrate the boundedness of the closed-loop system for different control modes and establish a uniform upper bound of the Lyapunov certificate throughout the entire time domain, thereby proving the overall uniformly ultimately bounded (UUB) of the system. Finally, numerical simulation results are presented to validate the effectiveness of the proposed control scheme.
{"title":"Switched Hybrid Control for Spacecraft Attitude Control With Flexible and Guaranteed Performance","authors":"Jiakun Lei;Tao Meng;Dongyu Li;Kun Wang;Weijia Wang;Zhonghe Jin","doi":"10.1109/TCST.2024.3508580","DOIUrl":"https://doi.org/10.1109/TCST.2024.3508580","url":null,"abstract":"This article addresses the challenge of achieving spacecraft attitude control with guaranteed performance while significantly reducing actuator activation frequency. To tackle this issue, we propose the concept of switched hybrid control and further integrate it with a modified prescribed-performance control (PPC) scheme. To enhance the robustness of the PPC control, we introduce the concept of a zeroing barrier function (ZBF). Coupled with a projection-operator-based modification dynamics, this approach assesses and adjusts the envelope in response to the risk of violating performance envelope constraints. Subsequently, a control mode switching strategy, considering the safety of the performance envelope and the system’s motion velocity, is proposed. This strategy automatically switches between intermittent and continuous control modes to select an appropriate control command execution strategy, thereby reducing actuator activation frequency under proper circumstances. Furthermore, we demonstrate the boundedness of the closed-loop system for different control modes and establish a uniform upper bound of the Lyapunov certificate throughout the entire time domain, thereby proving the overall uniformly ultimately bounded (UUB) of the system. Finally, numerical simulation results are presented to validate the effectiveness of the proposed control scheme.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 2","pages":"582-596"},"PeriodicalIF":4.9,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-25DOI: 10.1109/TCST.2024.3518920
Fei Dong;Xinyu Wang;Qinglei Hu;Jianpeng Zhong;Keyou You
The intrinsic hysteresis nonlinearity of piezo-actuated stages (piezo stages) poses a significant challenge for precise trajectory tracking at high speeds. In response, we propose a deep parallel (dPara) model that effectively captures the dynamics of the piezo stage using historical voltage–displacement data over a concise time period. The dPara model, incorporating a parallel combination of a linear block and a feedforward neural network (FNN), exhibits exceptional performance with relative prediction errors ranging between 0.10% and 0.18% on sinusoidal trajectories at frequencies up to 72% of the resonance frequency of the piezo stage. By leveraging this parallel structure, we adapt the reference trajectory for a complex nonlinear model predictive control (MPC), leading to the development of the reference-adaptation MPC (RA-MPC). Furthermore, we design a coordinate ascent algorithm to solve the quadratic programming (QP) problem derived from the RA-MPC at a high frequency of 10 kHz. To assess the superiority of the proposed RA-MPC, comprehensive experiments are conducted under sinusoid, sawtooth, and staircase reference trajectories. Notably, it achieves maximum tracking errors (MTEs) ranging from 0.0263 to $0.7136 ; mu $ m for desired speeds spanning from 40 to $20,000 ; mu $ m/s.
压电驱动级（压电级）的固有迟滞非线性对高速下的精确轨迹跟踪提出了重大挑战。作为回应，我们提出了一种深度并行（dPara）模型，该模型使用简明时间段内的历史电压位移数据有效地捕获压电级的动态。dPara模型结合了线性块和前馈神经网络（FNN）的并行组合，在频率高达压电级共振频率的72%的正弦轨迹上表现出优异的性能，相对预测误差在0.10%到0.18%之间。利用这种并联结构，我们将参考轨迹调整为复杂非线性模型预测控制（MPC），从而导致参考自适应MPC （RA-MPC）的发展。此外，我们设计了一种坐标上升算法来解决由RA-MPC在10 kHz高频下导出的二次规划（QP）问题。为了评估所提出的RA-MPC的优越性，在正弦、锯齿和阶梯参考轨迹下进行了综合实验。值得注意的是，它实现了最大跟踪误差（mte）范围从0.0263到0.7136美元；$ mu $ m用于从$ 40到$ 20,000的期望速度；mu $ m/s。
{"title":"Reference-Adaptation Predictive Control Based on a Deep Parallel Model for Piezo-Actuated Stages","authors":"Fei Dong;Xinyu Wang;Qinglei Hu;Jianpeng Zhong;Keyou You","doi":"10.1109/TCST.2024.3518920","DOIUrl":"https://doi.org/10.1109/TCST.2024.3518920","url":null,"abstract":"The intrinsic hysteresis nonlinearity of piezo-actuated stages (piezo stages) poses a significant challenge for precise trajectory tracking at high speeds. In response, we propose a deep parallel (dPara) model that effectively captures the dynamics of the piezo stage using historical voltage–displacement data over a concise time period. The dPara model, incorporating a parallel combination of a linear block and a feedforward neural network (FNN), exhibits exceptional performance with relative prediction errors ranging between 0.10% and 0.18% on sinusoidal trajectories at frequencies up to 72% of the resonance frequency of the piezo stage. By leveraging this parallel structure, we adapt the reference trajectory for a complex nonlinear model predictive control (MPC), leading to the development of the reference-adaptation MPC (RA-MPC). Furthermore, we design a coordinate ascent algorithm to solve the quadratic programming (QP) problem derived from the RA-MPC at a high frequency of 10 kHz. To assess the superiority of the proposed RA-MPC, comprehensive experiments are conducted under sinusoid, sawtooth, and staircase reference trajectories. Notably, it achieves maximum tracking errors (MTEs) ranging from 0.0263 to <inline-formula> <tex-math>$0.7136 ; mu $ </tex-math></inline-formula>m for desired speeds spanning from 40 to <inline-formula> <tex-math>$20,000 ; mu $ </tex-math></inline-formula>m/s.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 3","pages":"915-927"},"PeriodicalIF":4.9,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-24DOI: 10.1109/TCST.2024.3516386
Bo Yang;Zishuo Li;Jiayun Li;Yilin Mo;Jiaqi Yan
In this article, we address the model predictive control (MPC) problem for continuous-time linear time-invariant systems, with both state and input constraints. For computational efficiency, existing approaches typically discretize both dynamics and constraints, which potentially leads to constraint violations in between discrete-time instants. In contrast, to ensure strict constraint satisfaction, we equivalently replace the differential equations with linear mappings between state, input, and flat output, leveraging the differential flatness property of linear systems. By parameterizing the flat output with piecewise polynomials and employing Markov-Lukács theorem, the original MPC problem is then transformed into a semidefinite programming (SDP) problem, which guarantees the strict constraints satisfaction at all time. Furthermore, exploiting the fact that the proposed SDP contains numerous small-sized positive semidefinite (PSD) matrices as optimization variables, we propose a primal-dual hybrid gradient (PDHG) algorithm that can be efficiently parallelized, expediting the optimization procedure with GPU parallel computing. The simulation and experimental results demonstrate that our approach guarantees rigorous adherence to constraints at all time, and our solver exhibits superior computational speed compared to existing solvers for the proposed SDP problem.
{"title":"Continuous-Time-Constrained Model Predictive Control With a Parallel Solver","authors":"Bo Yang;Zishuo Li;Jiayun Li;Yilin Mo;Jiaqi Yan","doi":"10.1109/TCST.2024.3516386","DOIUrl":"https://doi.org/10.1109/TCST.2024.3516386","url":null,"abstract":"In this article, we address the model predictive control (MPC) problem for continuous-time linear time-invariant systems, with both state and input constraints. For computational efficiency, existing approaches typically discretize both dynamics and constraints, which potentially leads to constraint violations in between discrete-time instants. In contrast, to ensure strict constraint satisfaction, we equivalently replace the differential equations with linear mappings between state, input, and flat output, leveraging the differential flatness property of linear systems. By parameterizing the flat output with piecewise polynomials and employing Markov-Lukács theorem, the original MPC problem is then transformed into a semidefinite programming (SDP) problem, which guarantees the strict constraints satisfaction at all time. Furthermore, exploiting the fact that the proposed SDP contains numerous small-sized positive semidefinite (PSD) matrices as optimization variables, we propose a primal-dual hybrid gradient (PDHG) algorithm that can be efficiently parallelized, expediting the optimization procedure with GPU parallel computing. The simulation and experimental results demonstrate that our approach guarantees rigorous adherence to constraints at all time, and our solver exhibits superior computational speed compared to existing solvers for the proposed SDP problem.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 3","pages":"845-857"},"PeriodicalIF":4.9,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-24DOI: 10.1109/TCST.2024.3516364
Preston T. Abadie;Tania R. Jahan;Donald J. Docimo
This article studies parameter variations in battery packs and estimation of the imbalance propagated by such heterogeneity. Battery pack use has drastically increased in several areas, ranging from personal vehicles to utility-scale power distribution. However, manufacturing tolerances allow for slight variations between battery cells, which can cause uneven current distributions and hinder pack operation. Current work in the literature studies these parameter discrepancies by analyzing their effects or estimating the imbalances, but there are scarce efforts toward combining these tenets of addressing parameter mismatch. This article presents a modeling framework conducive to both analysis and estimation, allowing for investigation of battery dynamics due to unequal parameters, providing analytical representations of the impact of cell mismatch on state and output dynamics. Furthermore, the framework facilitates the development of an online state estimator with reduced computational cost. After parameterization of 66 lithium-ion cells, the framework is used to determine the contributions of multiple types of parameter heterogeneity on output imbalances. The proposed estimator is then validated experimentally, showing how the fewer required calculations benefit estimation runtime. The results show that this estimation scheme is capable of providing estimates within 0.6% state of charge (SOC) of a baseline estimator’s error while providing over a 60% reduction in computational cost.
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