Pub Date : 2022-06-08DOI: 10.23919/ACC53348.2022.9867576
Benjamin Decardi-Nelson, Jinfeng Liu
In this work, we present a distributed framework based on the graph algorithm for computing control invariant set for nonlinear cascade systems. The proposed algorithm exploits the structure of the interconnections within a process network. First, the overall system is decomposed into several subsystems with overlapping states. Second, the control invariant set for the subsystems are computed in a distributed manner. Finally, an approximation of the control invariant set for the overall system is reconstructed from the subsystem solutions and validated. We demonstrate the efficacy and convergence of the proposed method to the centralized graph-based algorithm using a nonlinear example.
{"title":"A distributed control invariant set computing algorithm for nonlinear cascade systems","authors":"Benjamin Decardi-Nelson, Jinfeng Liu","doi":"10.23919/ACC53348.2022.9867576","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867576","url":null,"abstract":"In this work, we present a distributed framework based on the graph algorithm for computing control invariant set for nonlinear cascade systems. The proposed algorithm exploits the structure of the interconnections within a process network. First, the overall system is decomposed into several subsystems with overlapping states. Second, the control invariant set for the subsystems are computed in a distributed manner. Finally, an approximation of the control invariant set for the overall system is reconstructed from the subsystem solutions and validated. We demonstrate the efficacy and convergence of the proposed method to the centralized graph-based algorithm using a nonlinear example.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128128100","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 : 2022-06-08DOI: 10.23919/ACC53348.2022.9867617
Yashodhara Pandit, Amitalok J. Budkuley
In this work, we study the problem of distributed sampling for the recovery of a remote source under information mismatch at the estimator. In particular, a centralized estimator seeks to estimate a remote Gaussian random signal, where unlike in the ‘classical’ estimation setup, we assume that the estimator has a fixed, unknown mismatch vis-à-vis source statistics, in particular, the source covariance matrix. Such a mismatched estimator deploys multiple samplers in the field, where each sampler observes an independently noise corrupted version of the remote source and then forwards its sampled version to the estimator. The estimator has a fixed limit on the number of samples it can concurrently process; given such a total sampling budget, it seeks to distribute these samples optimally among samplers so as to obtain a reasonably high fidelity sampled noisy observation of the remote source via the samplers. Using this sampled data, the mismatched estimator then outputs a source estimate which minimizes distortion (i.e., the overall mean squared error).Our principal goal in this work is to understand the distortion-versus-sampling rate trade-off for the mismatched Gaussian source estimation problem under general distributed configurations. In the high-rate sampling regime, where the estimator has a ‘large’ sampling budget and essentially every sampler can operate at ‘high’ sampling rate, we show the interesting result that for a wide range of parameters, the optimal distributed sampling strategy is a uniform sampling strategy but one which, interestingly, does not depend on the mismatch at the estimator. We also characterize the optimal distortion, which we show does indeed depend on the degree of mismatch. Our results also bring to the fore an interesting phenomenon where the optimal distortion behaves asymmetrically w.r.t. the nature of mismatch, i.e., even for identical mismatch magnitude, the distortion is significantly different depending on the sign of the mismatch.
{"title":"On Distributed Sampling for Mismatched Estimation of Remote Sources","authors":"Yashodhara Pandit, Amitalok J. Budkuley","doi":"10.23919/ACC53348.2022.9867617","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867617","url":null,"abstract":"In this work, we study the problem of distributed sampling for the recovery of a remote source under information mismatch at the estimator. In particular, a centralized estimator seeks to estimate a remote Gaussian random signal, where unlike in the ‘classical’ estimation setup, we assume that the estimator has a fixed, unknown mismatch vis-à-vis source statistics, in particular, the source covariance matrix. Such a mismatched estimator deploys multiple samplers in the field, where each sampler observes an independently noise corrupted version of the remote source and then forwards its sampled version to the estimator. The estimator has a fixed limit on the number of samples it can concurrently process; given such a total sampling budget, it seeks to distribute these samples optimally among samplers so as to obtain a reasonably high fidelity sampled noisy observation of the remote source via the samplers. Using this sampled data, the mismatched estimator then outputs a source estimate which minimizes distortion (i.e., the overall mean squared error).Our principal goal in this work is to understand the distortion-versus-sampling rate trade-off for the mismatched Gaussian source estimation problem under general distributed configurations. In the high-rate sampling regime, where the estimator has a ‘large’ sampling budget and essentially every sampler can operate at ‘high’ sampling rate, we show the interesting result that for a wide range of parameters, the optimal distributed sampling strategy is a uniform sampling strategy but one which, interestingly, does not depend on the mismatch at the estimator. We also characterize the optimal distortion, which we show does indeed depend on the degree of mismatch. Our results also bring to the fore an interesting phenomenon where the optimal distortion behaves asymmetrically w.r.t. the nature of mismatch, i.e., even for identical mismatch magnitude, the distortion is significantly different depending on the sign of the mismatch.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"258 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115801350","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 : 2022-06-08DOI: 10.23919/ACC53348.2022.9867874
Jie Wang, G. Chiu
Accurate and consistent drop volume and high drop placement accuracy are important performance factors for drop-on-demand inkjet printing.
准确一致的滴量和高滴位精度是按需喷墨打印的重要性能因素。
{"title":"Kalman Estimation Based One-Step Look Ahead Control of Data-driven Model with Random Parameters","authors":"Jie Wang, G. Chiu","doi":"10.23919/ACC53348.2022.9867874","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867874","url":null,"abstract":"Accurate and consistent drop volume and high drop placement accuracy are important performance factors for drop-on-demand inkjet printing.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132111259","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 : 2022-06-08DOI: 10.23919/ACC53348.2022.9867292
Shaopan Guo, Xiangyu Meng, M. Farasat
A longitudinal control of autonomous electric vehicle platoons is proposed for improved energy efficiency and battery management. The proposed control scheme consists of two phases: the resequencing phase and the platooning phase. The introduction of the resequencing phase overcomes the issue that the leader vehicle’s battery charge diminishes excessively fast in the traditional platoon control schemes, where the platoon is fixed, thereby extending the driving distance per charge cycle. A Monte Carlo reinforcement learning approach is used to find the optimal sequence of all vehicles. The platooning control is realized by a multi-agent formation control algorithm.
{"title":"Energy Efficient and Battery SOC-aware Coordinated Control of Connected and Autonomous Electric Vehicles","authors":"Shaopan Guo, Xiangyu Meng, M. Farasat","doi":"10.23919/ACC53348.2022.9867292","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867292","url":null,"abstract":"A longitudinal control of autonomous electric vehicle platoons is proposed for improved energy efficiency and battery management. The proposed control scheme consists of two phases: the resequencing phase and the platooning phase. The introduction of the resequencing phase overcomes the issue that the leader vehicle’s battery charge diminishes excessively fast in the traditional platoon control schemes, where the platoon is fixed, thereby extending the driving distance per charge cycle. A Monte Carlo reinforcement learning approach is used to find the optimal sequence of all vehicles. The platooning control is realized by a multi-agent formation control algorithm.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132543734","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 : 2022-06-08DOI: 10.23919/ACC53348.2022.9867853
Trevor J. Bird, Neera Jain, H. Pangborn, Justin P. Koeln
This paper presents a closed-form solution to the exact reachable sets of closed-loop systems under linear model predictive control (MPC) using the hybrid zonotope, a new mixed-integer set representation. This is accomplished by directly embedding the Karush Kuhn Tucker conditions of a parametric quadratic program within the hybrid zonotope set definition as mixed-integer constraints, and thus representing the set of all optimizers over a set of parameters. Using the set of explicit MPC solutions, it is shown how the plant’s closed-loop dynamics may be propagated through an identity that is calculated algebraically and does not require solving any optimization programs or taking set approximations. The proposed approach captures the worst-case exponential growth in the number of convex sets required to represent the exact reachable set, but incurs only linear growth in the number of variables used in the hybrid zonotope set representation. Beyond reachability analysis, it is shown that the set of optimizers represented by a hybrid zonotope may be decomposed to give the explicit solution of general quadratic multi-parametric programs as a collection of constrained zonotopes.
{"title":"Set-Based Reachability and the Explicit Solution of Linear MPC using Hybrid Zonotopes *","authors":"Trevor J. Bird, Neera Jain, H. Pangborn, Justin P. Koeln","doi":"10.23919/ACC53348.2022.9867853","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867853","url":null,"abstract":"This paper presents a closed-form solution to the exact reachable sets of closed-loop systems under linear model predictive control (MPC) using the hybrid zonotope, a new mixed-integer set representation. This is accomplished by directly embedding the Karush Kuhn Tucker conditions of a parametric quadratic program within the hybrid zonotope set definition as mixed-integer constraints, and thus representing the set of all optimizers over a set of parameters. Using the set of explicit MPC solutions, it is shown how the plant’s closed-loop dynamics may be propagated through an identity that is calculated algebraically and does not require solving any optimization programs or taking set approximations. The proposed approach captures the worst-case exponential growth in the number of convex sets required to represent the exact reachable set, but incurs only linear growth in the number of variables used in the hybrid zonotope set representation. Beyond reachability analysis, it is shown that the set of optimizers represented by a hybrid zonotope may be decomposed to give the explicit solution of general quadratic multi-parametric programs as a collection of constrained zonotopes.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130252661","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 : 2022-06-08DOI: 10.23919/ACC53348.2022.9867890
Guoqing Hu, F. You
This paper proposes a hybrid approach that utilizes the knowledge of the disjunctive uncertainty sets and incorporates them into the model predictive control (MPC). This approach targets multi-zone building control to the thermal comfort, and it’s robust to the uncertain weather forecast errors. The control objective is to maintain each zone’s temperature and relative humidity within the specified ranges by leveraging the minimum cost of energy of the underlying heating system. The hybrid model is constructed by using a physics-based and regression method for the temperature and relative humidity of each zone in the building. The uncertainty space is on the basis of historical weather forecast error data, which are captured by a group of disjunctive uncertainty sets using a k-means clustering algorithm. Machine learning approaches based on principal component analysis and kernel density estimation are used to construct each basic uncertainty set and reduce the conservatism of resulting robust control action under disturbances. Based on the proposed hybrid model and data-driven disjunctive uncertainty set, a robust MPC framework is further developed. An affine disturbance feedback rule is employed to obtain a tractable approximation of the robust MPC problem. Besides, the feasibility and stability of the proposed hybrid approach are ensured and discussed in detail. A case study of controlling temperature and relative humidity of a multi-zone building in Ithaca, New York, USA, is presented. The results demonstrate that the proposed hybrid approach can reduce 9.8% to 17.9% of total energy consumption compared to conventional robust MPC approaches. Moreover, the proposed hybrid approach can essentially satisfy the thermal constraints that certainty equivalent MPC and robust MPC largely violate.
{"title":"Hybrid Physics-based and Data-driven Model Predictive Control for Multi-Zone Building’s Thermal Comfort Under Disjunctive Uncertainty","authors":"Guoqing Hu, F. You","doi":"10.23919/ACC53348.2022.9867890","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867890","url":null,"abstract":"This paper proposes a hybrid approach that utilizes the knowledge of the disjunctive uncertainty sets and incorporates them into the model predictive control (MPC). This approach targets multi-zone building control to the thermal comfort, and it’s robust to the uncertain weather forecast errors. The control objective is to maintain each zone’s temperature and relative humidity within the specified ranges by leveraging the minimum cost of energy of the underlying heating system. The hybrid model is constructed by using a physics-based and regression method for the temperature and relative humidity of each zone in the building. The uncertainty space is on the basis of historical weather forecast error data, which are captured by a group of disjunctive uncertainty sets using a k-means clustering algorithm. Machine learning approaches based on principal component analysis and kernel density estimation are used to construct each basic uncertainty set and reduce the conservatism of resulting robust control action under disturbances. Based on the proposed hybrid model and data-driven disjunctive uncertainty set, a robust MPC framework is further developed. An affine disturbance feedback rule is employed to obtain a tractable approximation of the robust MPC problem. Besides, the feasibility and stability of the proposed hybrid approach are ensured and discussed in detail. A case study of controlling temperature and relative humidity of a multi-zone building in Ithaca, New York, USA, is presented. The results demonstrate that the proposed hybrid approach can reduce 9.8% to 17.9% of total energy consumption compared to conventional robust MPC approaches. Moreover, the proposed hybrid approach can essentially satisfy the thermal constraints that certainty equivalent MPC and robust MPC largely violate.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134148589","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 : 2022-06-08DOI: 10.23919/ACC53348.2022.9867348
Mengsen Jia, A. Köhler, R. Fritz, Ping Zhang
This paper considers the tracking control of Petri nets, namely finding the optimal firing sequence that leads the Petri net from an initial marking to a destination marking. Value neural networks (VNN) and policy neural networks (PNN) are used to improve the Monte-Carlo Tree Search (MCTS) based tracking control approach proposed recently in [1]. It is shown how to integrate the VNN and PNN, respectively, with the simulation and expansion step of the MCTS algorithm, so that the search space is significantly reduced. By introducing the neural networks, the dependence of the performance of the MCTS algorithm on parameter selection is also strongly reduced. Compared with the existing tracking control approaches, the proposed approaches can handle large PNs and have a very high probability of finding the optimal firing sequence within a prespecified time. The PNN based MCTS approach needs less online calculation, while the VNN based MCTS approach requires less offline training time. An example is given to illustrate the proposed approaches and show the advantage of the proposed approaches over other approaches.
{"title":"Monte-Carlo Tree Search with Neural Networks for Petri Nets","authors":"Mengsen Jia, A. Köhler, R. Fritz, Ping Zhang","doi":"10.23919/ACC53348.2022.9867348","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867348","url":null,"abstract":"This paper considers the tracking control of Petri nets, namely finding the optimal firing sequence that leads the Petri net from an initial marking to a destination marking. Value neural networks (VNN) and policy neural networks (PNN) are used to improve the Monte-Carlo Tree Search (MCTS) based tracking control approach proposed recently in [1]. It is shown how to integrate the VNN and PNN, respectively, with the simulation and expansion step of the MCTS algorithm, so that the search space is significantly reduced. By introducing the neural networks, the dependence of the performance of the MCTS algorithm on parameter selection is also strongly reduced. Compared with the existing tracking control approaches, the proposed approaches can handle large PNs and have a very high probability of finding the optimal firing sequence within a prespecified time. The PNN based MCTS approach needs less online calculation, while the VNN based MCTS approach requires less offline training time. An example is given to illustrate the proposed approaches and show the advantage of the proposed approaches over other approaches.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134443979","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 : 2022-06-08DOI: 10.23919/ACC53348.2022.9867447
H. Haeri, Behrad Soleimani, Kshitij Jerath
Moving averages are widely used to estimate time-varying parameters, especially when the underlying dynamic model is unknown or uncertain. However, the selection of the optimal window length over which to evaluate the moving averages remains an unresolved issue in the field. In this paper, we demonstrate the use of Allan variance to identify the characteristic timescales of a noisy random walk from historical measurements. Further, we provide a closed-form, analytical result to show that the Allan variance-informed averaging window length is indeed the optimal averaging window length in the context of moving average estimation of noisy random walks. We complement the analytical proof with numerical results that support the solution, which is also reflected in the authors’ related works. This systematic methodology for selecting the optimal averaging window length using Allan variance is expected to widely benefit practitioners in a diverse array of fields that utilize the moving average estimation technique for noisy random walk signals.
{"title":"Optimal Moving Average Estimation of Noisy Random Walks using Allan Variance-informed Window Length","authors":"H. Haeri, Behrad Soleimani, Kshitij Jerath","doi":"10.23919/ACC53348.2022.9867447","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867447","url":null,"abstract":"Moving averages are widely used to estimate time-varying parameters, especially when the underlying dynamic model is unknown or uncertain. However, the selection of the optimal window length over which to evaluate the moving averages remains an unresolved issue in the field. In this paper, we demonstrate the use of Allan variance to identify the characteristic timescales of a noisy random walk from historical measurements. Further, we provide a closed-form, analytical result to show that the Allan variance-informed averaging window length is indeed the optimal averaging window length in the context of moving average estimation of noisy random walks. We complement the analytical proof with numerical results that support the solution, which is also reflected in the authors’ related works. This systematic methodology for selecting the optimal averaging window length using Allan variance is expected to widely benefit practitioners in a diverse array of fields that utilize the moving average estimation technique for noisy random walk signals.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"7 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132624646","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 : 2022-06-08DOI: 10.23919/ACC53348.2022.9867549
In this paper, a computationally efficient two-path nonlinear optimal control allocation method is proposed to improve the yaw stability of four-wheel-independent-steering, four-wheel-drive vehicles. The virtual controller output is allocated using an optimization problem to compute each wheel's steering and traction commands at every controller time step. The optimization problem is solved by running a sequential quadratic programming (SQP) procedure, which may take some time to obtain satisfactory results. The proposed two-path control structure is derived from a more complex single-path allocation problem where torque allocation and steering correction optimal solutions are calculated concurrently. In this separated two-path control structure, computational load due to the complexity of the single block problem is reduced. In real applications, each problem can be run in parallel on different controllers of the vehicle controller network, which decreases the execution time with near-optimal results. The performance and speed comparisons of both approaches are studied using detailed vehicle simulations.
{"title":"A Computationally Efficient Control Allocation Method for Four-Wheel-Drive and Four-Wheel-Independent-Steering Electric Vehicles","authors":"","doi":"10.23919/ACC53348.2022.9867549","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867549","url":null,"abstract":"In this paper, a computationally efficient two-path nonlinear optimal control allocation method is proposed to improve the yaw stability of four-wheel-independent-steering, four-wheel-drive vehicles. The virtual controller output is allocated using an optimization problem to compute each wheel's steering and traction commands at every controller time step. The optimization problem is solved by running a sequential quadratic programming (SQP) procedure, which may take some time to obtain satisfactory results. The proposed two-path control structure is derived from a more complex single-path allocation problem where torque allocation and steering correction optimal solutions are calculated concurrently. In this separated two-path control structure, computational load due to the complexity of the single block problem is reduced. In real applications, each problem can be run in parallel on different controllers of the vehicle controller network, which decreases the execution time with near-optimal results. The performance and speed comparisons of both approaches are studied using detailed vehicle simulations.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129375823","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 : 2022-06-08DOI: 10.23919/ACC53348.2022.9867806
Tim Martin, F. Allgöwer
In the literature of data-driven dissipativity verification, many approaches are restricted to linear systems or require knowledge on the basis functions of the nonlinear system dynamics. To overcome these limitations, this work proposes based on Taylor approximation a novel polynomial representation of nonlinear systems which can be learned from noise-corrupted measurements. Due to the polynomial characterization and the inclusion of the approximation error into the analysis, we can determine dissipativity properties for nonlinear dynamical systems from noisy data with rigorous guarantees, without explicitly identifying a model, and using computationally tractable sum of squares optimization.
{"title":"Determining dissipativity for nonlinear systems from noisy data using Taylor polynomial approximation","authors":"Tim Martin, F. Allgöwer","doi":"10.23919/ACC53348.2022.9867806","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867806","url":null,"abstract":"In the literature of data-driven dissipativity verification, many approaches are restricted to linear systems or require knowledge on the basis functions of the nonlinear system dynamics. To overcome these limitations, this work proposes based on Taylor approximation a novel polynomial representation of nonlinear systems which can be learned from noise-corrupted measurements. Due to the polynomial characterization and the inclusion of the approximation error into the analysis, we can determine dissipativity properties for nonlinear dynamical systems from noisy data with rigorous guarantees, without explicitly identifying a model, and using computationally tractable sum of squares optimization.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126594999","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}
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