Joel Paulson, Farshud Sorourifar, Christopher Laughman, Ankush Chakrabarty
Abstract Self-optimizing efficiency of vapor compression cycles (VCCs) involves assigning multiple decision variables simultaneously in order to minimize power consumption while maintaining safe operating conditions. Due to the modeling complexity associated with cycle dynamics (and other smart building energy systems), online self-optimization requires algorithms that can safely and efficiently explore the search space in a derivative-free and model-agnostic manner. This makes Bayesian optimization (BO) a strong candidate for self-optimization. Unfortunately, classical BO algorithms ignore the relationship between consecutive optimizer candidates, resulting in jumps in the search space that can lead to fail-safe mechanisms being triggered, or undesired transient dynamics that violate operational constraints. To this end, we propose safe LSR-BO, a global optimization methodology that builds on the BO framework while enforcing two types of safety constraints including black-box constraints on the output and local search region (LSR) constraints on the input. We provide theoretical guarantees that under standard assumptions on the performance and constraint functions, LSR-BO guarantees constraints will be satisfied at all iterations with high probability. Furthermore, in the presence of only input LSR constraints, we show the method will converge to the true (unknown) globally optimal solution. We demonstrate the potential of our proposed LSR-BO method on a high-fidelity simulation model of a commercial vapor compression system with both LSR constraints on expansion valve positions and fan speeds, in addition to other safety constraints on discharge and evaporator temperatures.
{"title":"Self-Optimizing Vapor Compression Cycles Online With Bayesian Optimization Under Local Search Region Constraints","authors":"Joel Paulson, Farshud Sorourifar, Christopher Laughman, Ankush Chakrabarty","doi":"10.1115/1.4064027","DOIUrl":"https://doi.org/10.1115/1.4064027","url":null,"abstract":"Abstract Self-optimizing efficiency of vapor compression cycles (VCCs) involves assigning multiple decision variables simultaneously in order to minimize power consumption while maintaining safe operating conditions. Due to the modeling complexity associated with cycle dynamics (and other smart building energy systems), online self-optimization requires algorithms that can safely and efficiently explore the search space in a derivative-free and model-agnostic manner. This makes Bayesian optimization (BO) a strong candidate for self-optimization. Unfortunately, classical BO algorithms ignore the relationship between consecutive optimizer candidates, resulting in jumps in the search space that can lead to fail-safe mechanisms being triggered, or undesired transient dynamics that violate operational constraints. To this end, we propose safe LSR-BO, a global optimization methodology that builds on the BO framework while enforcing two types of safety constraints including black-box constraints on the output and local search region (LSR) constraints on the input. We provide theoretical guarantees that under standard assumptions on the performance and constraint functions, LSR-BO guarantees constraints will be satisfied at all iterations with high probability. Furthermore, in the presence of only input LSR constraints, we show the method will converge to the true (unknown) globally optimal solution. We demonstrate the potential of our proposed LSR-BO method on a high-fidelity simulation model of a commercial vapor compression system with both LSR constraints on expansion valve positions and fan speeds, in addition to other safety constraints on discharge and evaporator temperatures.","PeriodicalId":54846,"journal":{"name":"Journal of Dynamic Systems Measurement and Control-Transactions of the Asme","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135286566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Andrew Weng, Hamidreza Movahedi, Clement Wong, Jason B. Siegel, Anna G. Stefanopoulou
Abstract This paper proposes an analytical framework describing how initial capacity and resistance variability in parallel-connected battery cells may inflict additional variability or reduce variability while the cells age. We derive closed-form equations for current and SOC imbalance dynamics within any given charge or discharge cycle. These dynamics are represented by a first-order equivalent circuit model with a linear open circuit voltage behavior and validated against experimental data. To demonstrate how current and SOC imbalance leads to cell degradation, we developed a simplified, incremental degradation update scheme based on the solid electrolyte interphase growth mechanism. We propose a scheme in which the inter-cycle imbalance dynamics update the intra-cycle degradation dynamics, and vice versa. Using this framework, we demonstrate that current imbalance can cause convergent degradation trajectories, consistent with previous reports. However, we also demonstrate that different degradation assumptions, such as those associated with SOC imbalance, may cause divergent degradation in some cases. We finally highlight the role of different cell chemistries, including different OCV function nonlinearities, on system behavior, and derive analytical bounds on the SOC imbalance using Lyapunov analysis.
{"title":"Current Imbalance in Dissimilar Parallel-Connected Batteries and the Fate of Degradation Convergence","authors":"Andrew Weng, Hamidreza Movahedi, Clement Wong, Jason B. Siegel, Anna G. Stefanopoulou","doi":"10.1115/1.4064028","DOIUrl":"https://doi.org/10.1115/1.4064028","url":null,"abstract":"Abstract This paper proposes an analytical framework describing how initial capacity and resistance variability in parallel-connected battery cells may inflict additional variability or reduce variability while the cells age. We derive closed-form equations for current and SOC imbalance dynamics within any given charge or discharge cycle. These dynamics are represented by a first-order equivalent circuit model with a linear open circuit voltage behavior and validated against experimental data. To demonstrate how current and SOC imbalance leads to cell degradation, we developed a simplified, incremental degradation update scheme based on the solid electrolyte interphase growth mechanism. We propose a scheme in which the inter-cycle imbalance dynamics update the intra-cycle degradation dynamics, and vice versa. Using this framework, we demonstrate that current imbalance can cause convergent degradation trajectories, consistent with previous reports. However, we also demonstrate that different degradation assumptions, such as those associated with SOC imbalance, may cause divergent degradation in some cases. We finally highlight the role of different cell chemistries, including different OCV function nonlinearities, on system behavior, and derive analytical bounds on the SOC imbalance using Lyapunov analysis.","PeriodicalId":54846,"journal":{"name":"Journal of Dynamic Systems Measurement and Control-Transactions of the Asme","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135240977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract For the mismatched uncertain system, a novel spiking-free disturbance observer (DO)-based sliding-mode control (SMC) scheme is developed. First, a DO is proposed to estimate the mismatched uncertainty. A new sliding-mode surface is developed by using the system states, the estimation of mismatched uncertainty, and a spike suppression term. Then, an SMC method is proposed by using the developed sliding-mode surface. Compared with existing SMC schemes, the proposed spiking-free DO-based SMC has two advantages: (1) the mismatched uncertainty can be effectively suppressed; (2) the harmful spike problem of extended disturbance observer (EDO)-based and extended state observer (ESO)-based SMC schemes when the observer gain is high can be avoided. Finally, the simulation is presented to show the effectiveness of the proposed scheme by comparing the proposed and existing SMC schemes.
{"title":"Spiking-Free Disturbance Observer-Based Sliding-Mode Control for Mismatched Uncertain System","authors":"Yang Wang, Yan Guan, Huanyun Li","doi":"10.1115/1.4063609","DOIUrl":"https://doi.org/10.1115/1.4063609","url":null,"abstract":"Abstract For the mismatched uncertain system, a novel spiking-free disturbance observer (DO)-based sliding-mode control (SMC) scheme is developed. First, a DO is proposed to estimate the mismatched uncertainty. A new sliding-mode surface is developed by using the system states, the estimation of mismatched uncertainty, and a spike suppression term. Then, an SMC method is proposed by using the developed sliding-mode surface. Compared with existing SMC schemes, the proposed spiking-free DO-based SMC has two advantages: (1) the mismatched uncertainty can be effectively suppressed; (2) the harmful spike problem of extended disturbance observer (EDO)-based and extended state observer (ESO)-based SMC schemes when the observer gain is high can be avoided. Finally, the simulation is presented to show the effectiveness of the proposed scheme by comparing the proposed and existing SMC schemes.","PeriodicalId":54846,"journal":{"name":"Journal of Dynamic Systems Measurement and Control-Transactions of the Asme","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135191001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Additive friction stir deposition is a recent innovation in additive manufacturing allowing the deposition of metallic alloys onto a metallic deposit bed, creating a purely mechanical metallic bond. The deposition can be done in a layer-by-layer manner, and the purely mechanical process eliminates the need for high energy consumption and can be deposited at a much higher rate than beam-based welding. The mechanical nature of the process allows the bonding of dissimilar alloys and a reduction in size of the heat affected zone. The additive friction stir deposition process is difficult to model and existing literature has focused on numerical analysis, which is not amenable to online closed-loop control. In this work, a form of reservoir computing called an echo state network is used to model the additive friction stir deposition process from online process data, and validation is performed on a reserved data set. Subsequently, a model free controller using Lyapunov-derived combination of the robust integral of the sign error, and a single hidden layer neural network design is developed to control the additive friction stir deposition process. Control efficacy is given by way of a Lyapunov analysis which shows the system is globally exponentially stable, and simulation results with the echo state networks. Stability proof shows that under one assumption, the controller can be extrapolated to the real system. The mean squared error of the tracking result using the controller and echo state network simulation is 2.05 degrees Celsius.
{"title":"Nonlinear Temperature Control of Additive Friction Stir Deposition Evaluated On an Echo State Network","authors":"Glen Merritt, Christian Cousin, Hwan-Sik Yoon","doi":"10.1115/1.4064000","DOIUrl":"https://doi.org/10.1115/1.4064000","url":null,"abstract":"Abstract Additive friction stir deposition is a recent innovation in additive manufacturing allowing the deposition of metallic alloys onto a metallic deposit bed, creating a purely mechanical metallic bond. The deposition can be done in a layer-by-layer manner, and the purely mechanical process eliminates the need for high energy consumption and can be deposited at a much higher rate than beam-based welding. The mechanical nature of the process allows the bonding of dissimilar alloys and a reduction in size of the heat affected zone. The additive friction stir deposition process is difficult to model and existing literature has focused on numerical analysis, which is not amenable to online closed-loop control. In this work, a form of reservoir computing called an echo state network is used to model the additive friction stir deposition process from online process data, and validation is performed on a reserved data set. Subsequently, a model free controller using Lyapunov-derived combination of the robust integral of the sign error, and a single hidden layer neural network design is developed to control the additive friction stir deposition process. Control efficacy is given by way of a Lyapunov analysis which shows the system is globally exponentially stable, and simulation results with the echo state networks. Stability proof shows that under one assumption, the controller can be extrapolated to the real system. The mean squared error of the tracking result using the controller and echo state network simulation is 2.05 degrees Celsius.","PeriodicalId":54846,"journal":{"name":"Journal of Dynamic Systems Measurement and Control-Transactions of the Asme","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135342076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract This article investigates the problem of optimal periodic cycling for maximizing the identifiability of the unknown parameters of a Lithium-Sulfur (Li-S) battery model, including estimates of the initial values of species masses. This research is motivated by the need for more accurate Li-S battery modeling and diagnostics. Li-S batteries offer higher energy density levels compared to more traditional lithium-ion batteries, making them an attractive option for energy storage applications. However, the monitoring and control of Li-S batteries is challenging because of the complexity of the underlying multi-step reaction chain. The existing literature addresses poor battery parameter identifiability through a variety of tools including optimal input shaping for Fisher information maximization. However, this literature's focus is predominantly on the identifiability of lithium-ion battery model parameters. The main purpose of this study is to optimize Li-S battery Fisher identifiability through optimal input shaping. The study shows that such optimal input shaping indeed improves the accuracy of Li-S parameter estimation significantly. This outcome is demonstrated in simulation. Moreover, an experimental study is conducted showing that the underlying battery model fits laboratory experimental cycling data reasonably well when the optimized test cycle is employed.
{"title":"Periodic Optimal Input Shaping for Maximizing Lithium-Sulfur Battery Parameter Identifiability","authors":"Mahsa Doosthosseini, Chu Xu, Hosam Fathy","doi":"10.1115/1.4064024","DOIUrl":"https://doi.org/10.1115/1.4064024","url":null,"abstract":"Abstract This article investigates the problem of optimal periodic cycling for maximizing the identifiability of the unknown parameters of a Lithium-Sulfur (Li-S) battery model, including estimates of the initial values of species masses. This research is motivated by the need for more accurate Li-S battery modeling and diagnostics. Li-S batteries offer higher energy density levels compared to more traditional lithium-ion batteries, making them an attractive option for energy storage applications. However, the monitoring and control of Li-S batteries is challenging because of the complexity of the underlying multi-step reaction chain. The existing literature addresses poor battery parameter identifiability through a variety of tools including optimal input shaping for Fisher information maximization. However, this literature's focus is predominantly on the identifiability of lithium-ion battery model parameters. The main purpose of this study is to optimize Li-S battery Fisher identifiability through optimal input shaping. The study shows that such optimal input shaping indeed improves the accuracy of Li-S parameter estimation significantly. This outcome is demonstrated in simulation. Moreover, an experimental study is conducted showing that the underlying battery model fits laboratory experimental cycling data reasonably well when the optimized test cycle is employed.","PeriodicalId":54846,"journal":{"name":"Journal of Dynamic Systems Measurement and Control-Transactions of the Asme","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135391250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Renato Rodriguez, Omidreza Ahmadzadeh, Yan Wang, Damoon Soudbakhsh
Abstract We present a physics-inspired input/output predictor of lithium-ion batteries (LiBs) for online state-of-charge (SOC) prediction. The complex electrochemical behavior of batteries results in nonlinear and high-dimensional dynamics. Accurate SOC prediction is paramount for increased performance, improved operational safety, and extended longevity of LiBs. The battery's internal parameters are cell-dependent and change with operating conditions and battery health variations. We present a data-driven solution to discover governing equations pertaining to SOC dynamics from battery operando measurements. Our approach relaxes the need for detailed knowledge of the battery's composition while maintaining prediction fidelity. The predictor consists of a library of candidate terms and a set of coefficients found via a sparsity-promoting algorithm. The library was enhanced with explicit physics-inspired terms to improve the predictor's interpretability and generalizability. Further, we developed a Monte Carlo search of additional nonlinear terms to efficiently explore the high-dimensional search space and improved the characterization of highly nonlinear behaviors. Additionally, we developed a hyperparameter autotuning approach for identifying optimal coefficients that balance accuracy and complexity. The resulting SOC predictor achieved high predictive performance scores (RMSE) of 2.2e-6 and 4.8e-4, respectively, for training and validation on experimental results corresponding to a stochastic drive cycle. Furthermore, the predictor achieved an RMSE of 8.5e-4 on unseen battery measurements corresponding to the standard US06 drive cycle, further showcasing the adaptability of the predictor and the enhanced modeling approach to new conditions.
{"title":"Data-Driven Discovery of Lithium-Ion Battery State of Charge Dynamics","authors":"Renato Rodriguez, Omidreza Ahmadzadeh, Yan Wang, Damoon Soudbakhsh","doi":"10.1115/1.4064026","DOIUrl":"https://doi.org/10.1115/1.4064026","url":null,"abstract":"Abstract We present a physics-inspired input/output predictor of lithium-ion batteries (LiBs) for online state-of-charge (SOC) prediction. The complex electrochemical behavior of batteries results in nonlinear and high-dimensional dynamics. Accurate SOC prediction is paramount for increased performance, improved operational safety, and extended longevity of LiBs. The battery's internal parameters are cell-dependent and change with operating conditions and battery health variations. We present a data-driven solution to discover governing equations pertaining to SOC dynamics from battery operando measurements. Our approach relaxes the need for detailed knowledge of the battery's composition while maintaining prediction fidelity. The predictor consists of a library of candidate terms and a set of coefficients found via a sparsity-promoting algorithm. The library was enhanced with explicit physics-inspired terms to improve the predictor's interpretability and generalizability. Further, we developed a Monte Carlo search of additional nonlinear terms to efficiently explore the high-dimensional search space and improved the characterization of highly nonlinear behaviors. Additionally, we developed a hyperparameter autotuning approach for identifying optimal coefficients that balance accuracy and complexity. The resulting SOC predictor achieved high predictive performance scores (RMSE) of 2.2e-6 and 4.8e-4, respectively, for training and validation on experimental results corresponding to a stochastic drive cycle. Furthermore, the predictor achieved an RMSE of 8.5e-4 on unseen battery measurements corresponding to the standard US06 drive cycle, further showcasing the adaptability of the predictor and the enhanced modeling approach to new conditions.","PeriodicalId":54846,"journal":{"name":"Journal of Dynamic Systems Measurement and Control-Transactions of the Asme","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135390746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Christopher Aksland, Daniel L. Clark, Christopher A. Lupp, Andrew G. Alleyne
Abstract Novel conceptual aircraft designs have been enabled by more electrified aircraft components providing enhanced capability and versatility. Through the advancement of multi-disciplinary design optimization, control co-design methods have become a popular approach for system design conceptualization wherein the plant and control action are designed simultaneously to account for the coupling between vehicle subsystems and power management systems. Many prior efforts have focused on open-loop control co-design that can later be adapted for a more realistic operating case. This work focuses on the development and scalability of closed-loop control co-design that would result in a physically realizable plant and closed-loop control law. The theoretical approach is demonstrated practically through the design of a hybrid electric unmanned air vehicle and two feedback controllers that operate the hybrid power split and propulsion system. The system is designed to complete a dynamic 7 phase mission consisting of multiple cruise, dash, engage, dive, and climb segments as quickly as possible. Given the scale of the dynamic design problem, a convergence study is introduced that facilitates accurate and computationally tractable design optimization studies. The study is conducted for independent, sequential, and simultaneous design approaches. The results indicate high-speed motors, high voltage batteries, and responsive control gains result in a fast vehicle with high thrust-to-weight ratio. The simultaneous design solution had the best closed-loop performance, outclassing a baseline system design by over 30%.
{"title":"Closed-Loop Control and Plant Co-Design of a Hybrid Electric Unmanned Air Vehicle","authors":"Christopher Aksland, Daniel L. Clark, Christopher A. Lupp, Andrew G. Alleyne","doi":"10.1115/1.4064025","DOIUrl":"https://doi.org/10.1115/1.4064025","url":null,"abstract":"Abstract Novel conceptual aircraft designs have been enabled by more electrified aircraft components providing enhanced capability and versatility. Through the advancement of multi-disciplinary design optimization, control co-design methods have become a popular approach for system design conceptualization wherein the plant and control action are designed simultaneously to account for the coupling between vehicle subsystems and power management systems. Many prior efforts have focused on open-loop control co-design that can later be adapted for a more realistic operating case. This work focuses on the development and scalability of closed-loop control co-design that would result in a physically realizable plant and closed-loop control law. The theoretical approach is demonstrated practically through the design of a hybrid electric unmanned air vehicle and two feedback controllers that operate the hybrid power split and propulsion system. The system is designed to complete a dynamic 7 phase mission consisting of multiple cruise, dash, engage, dive, and climb segments as quickly as possible. Given the scale of the dynamic design problem, a convergence study is introduced that facilitates accurate and computationally tractable design optimization studies. The study is conducted for independent, sequential, and simultaneous design approaches. The results indicate high-speed motors, high voltage batteries, and responsive control gains result in a fast vehicle with high thrust-to-weight ratio. The simultaneous design solution had the best closed-loop performance, outclassing a baseline system design by over 30%.","PeriodicalId":54846,"journal":{"name":"Journal of Dynamic Systems Measurement and Control-Transactions of the Asme","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135390412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kunal Sanjay Narkhede, Mohamad Shafiee Motahar, Sushant Veer, Ioannis Poulakakis
Abstract This paper presents a modular approach to motion planning with provable stability guarantees for robots that move through changing environments via periodic locomotion behaviors. We focus on dynamic walkers as a paradigm for such systems, although the tools developed in this paper can be used to support general compositional approaches to robot motion planning with Dynamic Movement Primitives (DMPs). By formulating the planning process as a Switching System with Multiple Equilibria (SSME) we prove that the system's evolution remains within explicitly characterized trapping regions in the state space under suitable constraints on the frequency of switching among the DMPs. These conditions encapsulate the low-level stability limitations in a form that can be easily communicated to the planner. Furthermore, we show how the available primitives can be safely composed online in a receding horizon manner to enable the robot to react to moving obstacles. The proposed framework can be applied in a wide class of 3D bipedal walking models, and offers a modular approach for integrating readily available low-level locomotion control and high-level planning methods.
{"title":"Reactive Gait Composition with Stability: Dynamic Walking Amidst Static and Moving Obstacles","authors":"Kunal Sanjay Narkhede, Mohamad Shafiee Motahar, Sushant Veer, Ioannis Poulakakis","doi":"10.1115/1.4063997","DOIUrl":"https://doi.org/10.1115/1.4063997","url":null,"abstract":"Abstract This paper presents a modular approach to motion planning with provable stability guarantees for robots that move through changing environments via periodic locomotion behaviors. We focus on dynamic walkers as a paradigm for such systems, although the tools developed in this paper can be used to support general compositional approaches to robot motion planning with Dynamic Movement Primitives (DMPs). By formulating the planning process as a Switching System with Multiple Equilibria (SSME) we prove that the system's evolution remains within explicitly characterized trapping regions in the state space under suitable constraints on the frequency of switching among the DMPs. These conditions encapsulate the low-level stability limitations in a form that can be easily communicated to the planner. Furthermore, we show how the available primitives can be safely composed online in a receding horizon manner to enable the robot to react to moving obstacles. The proposed framework can be applied in a wide class of 3D bipedal walking models, and offers a modular approach for integrating readily available low-level locomotion control and high-level planning methods.","PeriodicalId":54846,"journal":{"name":"Journal of Dynamic Systems Measurement and Control-Transactions of the Asme","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135476229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract In this paper, an improved model-free adaptive control strategy is proposed for the trajectory tracking problem of the wheeled mobile robot (WMR) with time-delay and bounded disturbance. Firstly, the original nonlinear time delay system is transformed into a data model by applying the full-form dynamic linearization method (FFDL). Secondly, the discrete-time extended state observer (DESO) is applied to estimate the unknown residual nonlinear time-varying term. A full-form dynamic linearization model-free adaptive control scheme based on discrete-time extended state observer (DESO-based FFDL MFAC) is proposed. In addition, a full-form dynamic linearization event-triggered model-free adaptive control based on discrete-time extended state observer (DESO based FFDL ET-MFAC) is established by designing an event-triggering condition to assure Lyapunov stability. The control input signal is updated only if the system indicator meets the provided event-triggering condition; otherwise, the control input remains unchanged which can address limited communication bandwidth effectively. Finally, the effectiveness of the proposed method is verified by simulation.
{"title":"Event-Triggered Model-Free Adaptive Control for Wheeled Mobile Robot with Time Delay and External Disturbance Based On Discrete-Time Extended State Observer","authors":"Jiahui Huang, Hua Chen, Chao Shen","doi":"10.1115/1.4063996","DOIUrl":"https://doi.org/10.1115/1.4063996","url":null,"abstract":"Abstract In this paper, an improved model-free adaptive control strategy is proposed for the trajectory tracking problem of the wheeled mobile robot (WMR) with time-delay and bounded disturbance. Firstly, the original nonlinear time delay system is transformed into a data model by applying the full-form dynamic linearization method (FFDL). Secondly, the discrete-time extended state observer (DESO) is applied to estimate the unknown residual nonlinear time-varying term. A full-form dynamic linearization model-free adaptive control scheme based on discrete-time extended state observer (DESO-based FFDL MFAC) is proposed. In addition, a full-form dynamic linearization event-triggered model-free adaptive control based on discrete-time extended state observer (DESO based FFDL ET-MFAC) is established by designing an event-triggering condition to assure Lyapunov stability. The control input signal is updated only if the system indicator meets the provided event-triggering condition; otherwise, the control input remains unchanged which can address limited communication bandwidth effectively. Finally, the effectiveness of the proposed method is verified by simulation.","PeriodicalId":54846,"journal":{"name":"Journal of Dynamic Systems Measurement and Control-Transactions of the Asme","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135479740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract An innovative deep-learning-based model, namely, deep anomaly detection with the same probability distribution (DADSPD) is proposed to improve the accuracy of anomaly detection (AD) of rolling bearings driven only by normal data. First, the main framework of feature extraction based on a residual network was established, and a three-layer encoder structure was used to extract multidimensional features. Second, a new loss function based on the same probability distribution is designed, and the function of its probability distribution is to complete the training of the model by calculating the similarity between the outputs. Subsequently, the vibration data were preprocessed using wavelet and envelope analysis, and the processed data are converted into two-dimensional image signals and used as the input of the DADSPD. Finally, the model is verified on three sets of run-to-failure experimental datasets of rolling bearing. The results demonstrate that the proposed DADSPD model reaches more than 99%, which indicates that the DADSPD model has a high fault early warning and AD capability.
{"title":"A Deep Anomaly Detection With Same Probability Distribution And Its Application In Rolling Bearing","authors":"Yuxiang Kang, Guo Chen, Wenping Pan, Hao Wang, Xunkai Wei","doi":"10.1115/1.4063608","DOIUrl":"https://doi.org/10.1115/1.4063608","url":null,"abstract":"Abstract An innovative deep-learning-based model, namely, deep anomaly detection with the same probability distribution (DADSPD) is proposed to improve the accuracy of anomaly detection (AD) of rolling bearings driven only by normal data. First, the main framework of feature extraction based on a residual network was established, and a three-layer encoder structure was used to extract multidimensional features. Second, a new loss function based on the same probability distribution is designed, and the function of its probability distribution is to complete the training of the model by calculating the similarity between the outputs. Subsequently, the vibration data were preprocessed using wavelet and envelope analysis, and the processed data are converted into two-dimensional image signals and used as the input of the DADSPD. Finally, the model is verified on three sets of run-to-failure experimental datasets of rolling bearing. The results demonstrate that the proposed DADSPD model reaches more than 99%, which indicates that the DADSPD model has a high fault early warning and AD capability.","PeriodicalId":54846,"journal":{"name":"Journal of Dynamic Systems Measurement and Control-Transactions of the Asme","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135012567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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