{"title":"Controlling the Shape of Soft Robots Using the Koopman Operator","authors":"A. Singh, Jiefeng Sun, Jianguo Zhao","doi":"10.23919/ACC55779.2023.10156145","DOIUrl":"https://doi.org/10.23919/ACC55779.2023.10156145","url":null,"abstract":"In nature, animals with soft body parts demonstrate remarkable control over their shape, such as an elephant trunk wrapping around a tree branch to pick it up. However, most research on robotic manipulators focuses on controlling the end effector, partly because the manipulator’s arm is rigidly articulated. With recent advances in soft robotics research, controlling a soft manipulator into many different shapes will significantly improve the robot’s functionality, such as medical robots morphing their shape to navigate the digestive system and deliver drugs to specific locations. However, controlling the shape of soft robots is challenging due to their highly nonlinear dynamics that are computationally intensive. In this paper, we leverage a physics-informed, data-driven approach using the Koopman operator to realize the shape control of soft robots. We simulate the dynamics of a soft manipulator using a physics-based simulator (PyElastica) to generate the input-output data, which is then used to identify an approximated linear model based on the Koopman operator. We then formulate the shapecontrol problem as a convex optimization problem that is computationally efficient. Our linear model is over 12 times faster than the physics-based model in simulating the manipulator’s motion. Further, we can control a soft manipulator into different shapes using model predictive control. We envision that the proposed method can be effectively used to control the shapes of soft robots to interact with uncertain environments or enable shape-morphing robots to fulfill diverse tasks. This paper is complemented with a video.","PeriodicalId":397401,"journal":{"name":"2023 American Control Conference (ACC)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115136450","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}
{"title":"Safe and Constrained Rendezvous, Proximity Operations, and Docking","authors":"C. Petersen, R. Caverly, S. Phillips, A. Weiss","doi":"10.23919/ACC55779.2023.10155826","DOIUrl":"https://doi.org/10.23919/ACC55779.2023.10155826","url":null,"abstract":"This tutorial paper discusses the rising need for safe and constrained spacecraft Rendezvous, Proximity Operations, and Docking (RPOD). This class of problems brings with it i) a unique set of equations of motion, ii) a variety of constraints and objectives that are specialized to RPOD, and iii) a number of traditional and current Guidance, Navigation, and Control (GNC) considerations. There are strong connections between the work done in RPOD and a variety of other research domains that have synergistically aided in pushing forward the state-of-the-art. This tutorial paper discusses the above, provides an entry point into the field of spacecraft RPOD, and highlights a selection of open problems that still exist in the field.","PeriodicalId":397401,"journal":{"name":"2023 American Control Conference (ACC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123088192","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}
{"title":"Learning Adaptive Optimal Controllers for Linear Time-Delay Systems *","authors":"Leilei Cui, Bo Pang, Zhong-Ping Jiang","doi":"10.23919/ACC55779.2023.10156108","DOIUrl":"https://doi.org/10.23919/ACC55779.2023.10156108","url":null,"abstract":"This paper studies the learning-based optimal control for a class of infinite-dimensional linear time-delay systems. The aim is to fill the gap of adaptive dynamic programming (ADP) where adaptive optimal control of infinite-dimensional systems is not addressed. A key strategy is to combine the classical model-based linear quadratic (LQ) optimal control of time-delay systems with the state-of-art reinforcement learning (RL) technique. Both the model-based and data-driven policy iteration (PI) approaches are proposed to solve the corresponding algebraic Riccati equation (ARE) with guaranteed convergence. The proposed PI algorithm can be considered as a generalization of ADP to infinite-dimensional time-delay systems. The efficiency of the proposed algorithm is demonstrated by the practical application arising from autonomous driving in mixed traffic environments, where human drivers’ reaction delay is considered.","PeriodicalId":397401,"journal":{"name":"2023 American Control Conference (ACC)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116772411","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}
Y. Shirai, Devesh K. Jha, A. Raghunathan, D. Romeres
{"title":"Chance-Constrained Optimization in Contact-rich Systems","authors":"Y. Shirai, Devesh K. Jha, A. Raghunathan, D. Romeres","doi":"10.23919/ACC55779.2023.10156516","DOIUrl":"https://doi.org/10.23919/ACC55779.2023.10156516","url":null,"abstract":"This paper presents a chance-constrained formulation for robust trajectory optimization during manipulation. In particular, we present a chance-constrained optimization for Stochastic Discrete-time Linear Complementarity Systems (SDLCS). To solve the optimization problem, we formulate Mixed-Integer Quadratic Programming with Chance Constraints (MIQPCC). In our formulation, we explicitly consider joint chance constraints for complementarity as well as states to capture the stochastic evolution of dynamics. We evaluate robustness of our optimized trajectories in simulation on several systems. The proposed approach outperforms some recent approaches for robust trajectory optimization for SDLCS.","PeriodicalId":397401,"journal":{"name":"2023 American Control Conference (ACC)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126077655","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}
{"title":"H2 suboptimal containment control of multi-agent systems","authors":"Yuan Gao, Junjie Jiao, S. Hirche","doi":"10.23919/ACC55779.2023.10156193","DOIUrl":"https://doi.org/10.23919/ACC55779.2023.10156193","url":null,"abstract":"This paper deals with the distributed H2 suboptimal containment control problem by static state feedback for linear multi-agent systems. Given multiple autonomous leaders, a number of followers, and an H2 cost functional, we aim to design a distributed protocol that achieves containment control while the associated H2 cost is smaller than an a priori given upper bound. To that end, we first show that the H2 suboptimal containment control problem can be equivalently recast into the H2 suboptimal control problem of a set of independent systems. Based on this, a design method is provided to compute such a distributed protocol. The computation of the feedback gain involves a single Riccati inequality whose dimension is equal to the dimension of the states of the agents. The performance of the proposed protocol is illustrated by a simulation example.","PeriodicalId":397401,"journal":{"name":"2023 American Control Conference (ACC)","volume":"268 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123287166","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}
Efrain Gonzalez, L. Avazpour, R. Kamalapurkar, Joel A. Rosenfeld
{"title":"Modeling Partially Unknown Dynamics with Continuous Time DMD*","authors":"Efrain Gonzalez, L. Avazpour, R. Kamalapurkar, Joel A. Rosenfeld","doi":"10.23919/ACC55779.2023.10156424","DOIUrl":"https://doi.org/10.23919/ACC55779.2023.10156424","url":null,"abstract":"This manuscript addresses the problem of the data driven modeling of a dynamical system in the presence of partially known dynamics using an operator theoretic dynamic mode decomposition (DMD) approach. The method relies on the linearity of the Liouville operator with respect to the dynamics together with established relations between Liouville operators and occupation kernels, which embed trajectory data as a function within a reproducing kernel Hilbert space. The linearity allows for the known portion of the dynamics to be subtracted from the overall dynamics, and the Liouville operator corresponding to the unknown dynamics may thus be isolated. A model for the unknown portion of the dynamical systems may then be obtained from observed trajectory data, and this model may then be utilized for predicting future states.","PeriodicalId":397401,"journal":{"name":"2023 American Control Conference (ACC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125266628","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}
{"title":"A novel control design for high-speed Atomic Force Microscopy*","authors":"R. S. Gorugantu, S. Salapaka","doi":"10.23919/ACC55779.2023.10156581","DOIUrl":"https://doi.org/10.23919/ACC55779.2023.10156581","url":null,"abstract":"In this paper, we propose a novel transform-based imaging mode for Atomic Force Microscopy (AFM), where the cantilever oscillations are made to track the output of a mockmodel system. The states of the resulting tracking error dynamics is appended by another set of suitably designed states, which enable a specific time-varying coordinate transformation, which in turn results in dynamic models that are very conducive to linear control synthesis design methods. The proposed imaging mode enables higher throughput in AFM imaging without the need for significantly high resonant frequency AFM cantilever probes. This method overcomes the fundamental limitation of nonlinear input-output relationship in Amplitude Modulation AFM (AM-AFM) imaging mode by applying an appropriately chosen real-time transform on the output signal. In combination with model-based reference generation, the proposed real-time transform yields linear dynamical input-output characteristics. Simulations on detailed AFM models with ${mathcal{H}_infty }$ optimal control designs show the efficacy of the proposed imaging mode for feature bandwidths higher than 5% of the cantilever resonant frequency.","PeriodicalId":397401,"journal":{"name":"2023 American Control Conference (ACC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126915912","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}
{"title":"Dynamic Component-Based Design Optimization of Multirotor Aircraft*","authors":"Philip M. Renkert, A. Alleyne","doi":"10.23919/ACC55779.2023.10156147","DOIUrl":"https://doi.org/10.23919/ACC55779.2023.10156147","url":null,"abstract":"Rising complexity of engineered systems, increasing needs for coordination among specialized design teams, and the emergence of component-based design modalities have created a need for component-based design optimization tools. Further, many modern systems are dynamic, and dynamic performance is a core component of the design objective. This work builds on a hybrid methodology for component-based design optimization by expanding the formulation to dynamic systems. The method is applied to select a planar quadrotor’s components and input trajectory to minimize the to complete a dynamic mission.","PeriodicalId":397401,"journal":{"name":"2023 American Control Conference (ACC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115044660","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}
Janine Matschek, M. D. Berliner, A. Himmel, R. D. Braatz, R. Findeisen
{"title":"Necessary Optimality Conditions for Fast Lithium-ion Battery Charging via Hybrid Simulations","authors":"Janine Matschek, M. D. Berliner, A. Himmel, R. D. Braatz, R. Findeisen","doi":"10.23919/ACC55779.2023.10155949","DOIUrl":"https://doi.org/10.23919/ACC55779.2023.10155949","url":null,"abstract":"Fast yet health-conscious and safe optimal charging for lithium-ion batteries is essential for resource efficiency, increased battery lifetime, and overall usability. While quick and resource-efficient charging can be formulated as an optimal control problem, it often cannot be solved in real-time on computationally limited, embedded systems. We build upon a mathematical reformulation of the constrained optimal control problem into hybrid simulations, which allows for computationally efficient solutions and provides operation modes beyond existing charging profiles. We analyze under which conditions this mathematical reformulation can lead to the optimality of the resulting charging protocols. Physics-based battery models are analyzed from a systems theory perspective. Using controllability and flatness properties, necessary optimality conditions for the charging protocols are established.","PeriodicalId":397401,"journal":{"name":"2023 American Control Conference (ACC)","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116461452","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}
Alexander Krolicki, Dakota Rufino, D. Tellez-Castro
{"title":"Finite Time Nonlinear Optimal Control using Koopman Eigenfunctions","authors":"Alexander Krolicki, Dakota Rufino, D. Tellez-Castro","doi":"10.23919/ACC55779.2023.10156481","DOIUrl":"https://doi.org/10.23919/ACC55779.2023.10156481","url":null,"abstract":"We propose a computational method for the finite-time nonlinear optimal control problem. We compute the solutions by first performing a coordinate transformation using the principle Koopman eigenfunctions. Then, we synthesize past and present techniques for obtaining a general explicit solution to the resulting differential Riccati equation. We demonstrate our method on a numerical example, for which the analytic Koopman eigenfunctions are known.","PeriodicalId":397401,"journal":{"name":"2023 American Control Conference (ACC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122845780","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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