Pub Date : 2022-06-08DOI: 10.23919/ACC53348.2022.9867264
Angelo D. Bonzanini, A. Mesbah, S. D. Cairano
Perception-aware Chance-constrained Model Predictive Control (PAC-MPC) accounts for the interdependence between perception and control for systems operating in uncertain environments. The environment is discovered by perception, which imposes chance constraints on system operation. PAC-MPC can handle a perception quality that depends on the system states and/or inputs, thus affecting uncertainty quantification in the chance constraints. In this paper, we extend PAC-MPC by introducing a scenario-based prediction for future measurements, so that the resulting multi-stage PAC-MPC does not require a conservative estimate of the measurement prediction error covariance. We demonstrate PAC-MPC for automated vehicle control when obstacles and road boundaries are uncertain and perceived by variable precision sensors subject to an overall sensing budget and when the scenarios are generated based on possible obstacle behaviors.
{"title":"Multi-stage Perception-aware Chance-constrained MPC with Applications to Automated Driving","authors":"Angelo D. Bonzanini, A. Mesbah, S. D. Cairano","doi":"10.23919/ACC53348.2022.9867264","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867264","url":null,"abstract":"Perception-aware Chance-constrained Model Predictive Control (PAC-MPC) accounts for the interdependence between perception and control for systems operating in uncertain environments. The environment is discovered by perception, which imposes chance constraints on system operation. PAC-MPC can handle a perception quality that depends on the system states and/or inputs, thus affecting uncertainty quantification in the chance constraints. In this paper, we extend PAC-MPC by introducing a scenario-based prediction for future measurements, so that the resulting multi-stage PAC-MPC does not require a conservative estimate of the measurement prediction error covariance. We demonstrate PAC-MPC for automated vehicle control when obstacles and road boundaries are uncertain and perceived by variable precision sensors subject to an overall sensing budget and when the scenarios are generated based on possible obstacle behaviors.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"26 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":"132733052","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/ACC53348.2022.9867594
Niklas Schmid, J. Gruner, H. S. Abbas, P. Rostalski
Gaussian Process (GP) regressions have proven to be a valuable tool to predict disturbances and model mismatches and incorporate this information into a Model Predictive Control (MPC) prediction. Unfortunately, the computational complexity of inference and learning on classical GPs scales cubically, which is intractable for real-time applications. Thus GPs are commonly trained offline, which is not suited for learning disturbances as their dynamics may vary with time. Recently, state-space formulation of GPs has been introduced, allowing inference and learning with linear computational complexity. This paper presents a framework that enables online learning of disturbance dynamics on quadcopters, which can be executed within milliseconds using a state-space formulation of GPs. The obtained disturbance predictions are combined with MPC leading to a significant performance increase in simulations with jMAVSim. The computational burden is evaluated on a Raspberry Pi 4 B to prove the real-time applicability.
高斯过程(GP)回归已被证明是预测干扰和模型不匹配的有价值的工具，并将这些信息纳入模型预测控制(MPC)预测。不幸的是，经典GPs的推理和学习的计算复杂度是立方的，这对于实时应用来说是难以解决的。因此，全科医生通常是离线训练，这并不适合学习障碍，因为它们的动态可能随时间而变化。最近，引入了GPs的状态空间公式，允许线性计算复杂度的推理和学习。本文提出了一个能够在线学习四轴飞行器扰动动力学的框架，该框架可以使用GPs的状态空间公式在毫秒内执行。得到的干扰预测与MPC相结合，导致jMAVSim模拟的性能显著提高。在Raspberry Pi 4b上对计算负担进行了评估，以证明该算法的实时性。
{"title":"A real-time GP based MPC for quadcopters with unknown disturbances","authors":"Niklas Schmid, J. Gruner, H. S. Abbas, P. Rostalski","doi":"10.23919/ACC53348.2022.9867594","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867594","url":null,"abstract":"Gaussian Process (GP) regressions have proven to be a valuable tool to predict disturbances and model mismatches and incorporate this information into a Model Predictive Control (MPC) prediction. Unfortunately, the computational complexity of inference and learning on classical GPs scales cubically, which is intractable for real-time applications. Thus GPs are commonly trained offline, which is not suited for learning disturbances as their dynamics may vary with time. Recently, state-space formulation of GPs has been introduced, allowing inference and learning with linear computational complexity. This paper presents a framework that enables online learning of disturbance dynamics on quadcopters, which can be executed within milliseconds using a state-space formulation of GPs. The obtained disturbance predictions are combined with MPC leading to a significant performance increase in simulations with jMAVSim. The computational burden is evaluated on a Raspberry Pi 4 B to prove the real-time applicability.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"31 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":"133116859","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/ACC53348.2022.9867270
Harish Bezawada, Cole Woods, V. Vikas
In recent years, there has been significant interest in use of soft and continuum manipulators in diverse fields including surgical and agricultural robotics. Consequently, researchers have designed open-loop and feedback control algorithms for such systems. Here, the knowledge of the manipulator shape is critical for effective control. The estimation of the manipulator shape is challenging due to their highly deformable and non-linear nature. Researchers have explored inductive, magnetic and optical sensing techniques to deduce the manipulator shape. However, they are intrusive and economically expensive. Alternate non-contact sensing approaches may involve use of vision or inertial measurement units (IMUs) that are placed at known intervals along the manipulator. Here, the camera provides position of the marker, while the slope (rotation matrix or direction cosines) can be determined using IMUs. In this paper, we mathematically model the manipulator shape using multiple piecewise continuous quintic Pythogorean-Hodograph (PH) curves. A PH-curve has continuous slope and is a convenient parametric model for curves with constant length. We investigate the use of multiple piecewise continuous-curvature PH curves to estimate the shape of a soft continuum manipulator. The curves model manipulator segments of constant lengths while the slopes at the knots are assumed to be known. For N curve segments with (4N + 8) unknowns, the shape estimation is formulated as a constrained optimization problem that minimizes the curve bending energy. The algorithm imposes (4N + 3) nonlinear constraints corresponding to continuity, slope and segment length. Unlike traditional cubic splines, the optimization problem is nonlinear and sensitive to initial guesses and has potential to provide incorrect estimates. We investigate the robustness of the algorithm by adding variation to the direction cosines, and compare the output shapes. The simulation results on a five-segment manipulator illustrate the robustness of the algorithm. While the experimental results on a soft tensegrity-spine manipulator validate the effectiveness of the approach. Here estimation error of the end-effector position normalized to the manipulator length are 6.53% and 6.2% for the two experimental poses.
{"title":"Shape Estimation of Soft Manipulators using Piecewise Continuous Pythagorean-Hodograph Curves","authors":"Harish Bezawada, Cole Woods, V. Vikas","doi":"10.23919/ACC53348.2022.9867270","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867270","url":null,"abstract":"In recent years, there has been significant interest in use of soft and continuum manipulators in diverse fields including surgical and agricultural robotics. Consequently, researchers have designed open-loop and feedback control algorithms for such systems. Here, the knowledge of the manipulator shape is critical for effective control. The estimation of the manipulator shape is challenging due to their highly deformable and non-linear nature. Researchers have explored inductive, magnetic and optical sensing techniques to deduce the manipulator shape. However, they are intrusive and economically expensive. Alternate non-contact sensing approaches may involve use of vision or inertial measurement units (IMUs) that are placed at known intervals along the manipulator. Here, the camera provides position of the marker, while the slope (rotation matrix or direction cosines) can be determined using IMUs. In this paper, we mathematically model the manipulator shape using multiple piecewise continuous quintic Pythogorean-Hodograph (PH) curves. A PH-curve has continuous slope and is a convenient parametric model for curves with constant length. We investigate the use of multiple piecewise continuous-curvature PH curves to estimate the shape of a soft continuum manipulator. The curves model manipulator segments of constant lengths while the slopes at the knots are assumed to be known. For N curve segments with (4N + 8) unknowns, the shape estimation is formulated as a constrained optimization problem that minimizes the curve bending energy. The algorithm imposes (4N + 3) nonlinear constraints corresponding to continuity, slope and segment length. Unlike traditional cubic splines, the optimization problem is nonlinear and sensitive to initial guesses and has potential to provide incorrect estimates. We investigate the robustness of the algorithm by adding variation to the direction cosines, and compare the output shapes. The simulation results on a five-segment manipulator illustrate the robustness of the algorithm. While the experimental results on a soft tensegrity-spine manipulator validate the effectiveness of the approach. Here estimation error of the end-effector position normalized to the manipulator length are 6.53% and 6.2% for the two experimental poses.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"7 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":"115572899","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/ACC53348.2022.9867857
Zhongjun Hu, Xu Jin
In this work, we propose a novel adaptive formation control architecture for a group of quadrotor systems, under line-of-sight (LOS) distance and relative distance constraints, where the constraint requirements can be both asymmetric and time-varying in nature. Universal barrier functions are adopted in the controller design and analysis, which is a generic framework that can address system with different types of constraints in a unified controller architecture. Furthermore, each quadrotor’s mass is unknown, and the system dynamics are subjected to time-varying external disturbance. Through rigorous analysis, an exponential convergence rate can be guaranteed on the distance tracking errors, while the constraints are satisfied during the operation. A simulation example further demonstrates the efficacy of the proposed control framework.
{"title":"An Adaptive Formation Control Architecture for A Team of Quadrotors with Performance and Safety Constraints","authors":"Zhongjun Hu, Xu Jin","doi":"10.23919/ACC53348.2022.9867857","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867857","url":null,"abstract":"In this work, we propose a novel adaptive formation control architecture for a group of quadrotor systems, under line-of-sight (LOS) distance and relative distance constraints, where the constraint requirements can be both asymmetric and time-varying in nature. Universal barrier functions are adopted in the controller design and analysis, which is a generic framework that can address system with different types of constraints in a unified controller architecture. Furthermore, each quadrotor’s mass is unknown, and the system dynamics are subjected to time-varying external disturbance. Through rigorous analysis, an exponential convergence rate can be guaranteed on the distance tracking errors, while the constraints are satisfied during the operation. A simulation example further demonstrates the efficacy of the proposed control framework.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"106 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":"124104587","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/ACC53348.2022.9867344
Steven J. Kuntz, J. Rawlings
The performance of industrially successful model predictive control (MPC) and offset-free MPC is reliant on identifying an adequate linear state-space model using plant data. While the models for MPC can be identified using one of many subspace identification methods, there are no methods for identifying the linear disturbance models used in offset-free MPC. Here we formulate a series of maximum likelihood estimation (MLE) problems for identifying linear disturbance models. To formulate the first problem, the state is estimated as a linear combination of past inputs and outputs, and the state-space model is then written as a linear estimation problem. The second problem is formulated as a linear estimation problem relating the long-range prediction error sequence to the disturbance and noise sequences. The last problem is simply a covariance estimation problem for the noises in the linear disturbance model. Each MLE problem has a closed-form solution. While size of the second MLE problem makes it computationally demanding, it can be simplified considerably in the case where the system has no integrators. Hardware experiments (TCLab, an Arduino-based heat transport laboratory) demonstrate that the proposed method generates offset-free performance under realistic conditions on systems without integrators. Numerical simulation experiments demonstrate that the results also generalize to systems with integrators.
{"title":"Maximum Likelihood Estimation of Linear Disturbance Models for Offset-free Model Predictive Control","authors":"Steven J. Kuntz, J. Rawlings","doi":"10.23919/ACC53348.2022.9867344","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867344","url":null,"abstract":"The performance of industrially successful model predictive control (MPC) and offset-free MPC is reliant on identifying an adequate linear state-space model using plant data. While the models for MPC can be identified using one of many subspace identification methods, there are no methods for identifying the linear disturbance models used in offset-free MPC. Here we formulate a series of maximum likelihood estimation (MLE) problems for identifying linear disturbance models. To formulate the first problem, the state is estimated as a linear combination of past inputs and outputs, and the state-space model is then written as a linear estimation problem. The second problem is formulated as a linear estimation problem relating the long-range prediction error sequence to the disturbance and noise sequences. The last problem is simply a covariance estimation problem for the noises in the linear disturbance model. Each MLE problem has a closed-form solution. While size of the second MLE problem makes it computationally demanding, it can be simplified considerably in the case where the system has no integrators. Hardware experiments (TCLab, an Arduino-based heat transport laboratory) demonstrate that the proposed method generates offset-free performance under realistic conditions on systems without integrators. Numerical simulation experiments demonstrate that the results also generalize to systems with integrators.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"42 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":"114833249","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/ACC53348.2022.9867389
Tao Liu, Jie Huang
This paper studies the output regulation problem for discrete-time linear systems subject to an uncertain exosystem. The design involves two online estimation algorithms. The first one is to estimate the unknown parameters of the exosystem, and the second one is to estimate the solution to the regulator equations based on the estimated system matrix of the exosystem. Combining these two algorithms gives rise to an iterative solution that converges exponentially to some exact solution to the regulator equations. Finally, by integrating these two algorithms with the feedforward control approach, both dynamic state feedback and dynamic output feedback control laws are synthesized to solve the problem.
{"title":"Adaptive Output Regulation for Discrete-time Linear Systems with an Uncertain Exosystem","authors":"Tao Liu, Jie Huang","doi":"10.23919/ACC53348.2022.9867389","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867389","url":null,"abstract":"This paper studies the output regulation problem for discrete-time linear systems subject to an uncertain exosystem. The design involves two online estimation algorithms. The first one is to estimate the unknown parameters of the exosystem, and the second one is to estimate the solution to the regulator equations based on the estimated system matrix of the exosystem. Combining these two algorithms gives rise to an iterative solution that converges exponentially to some exact solution to the regulator equations. Finally, by integrating these two algorithms with the feedforward control approach, both dynamic state feedback and dynamic output feedback control laws are synthesized to solve the problem.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"9 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":"117286860","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/ACC53348.2022.9867713
Vinay Kenny, Sixiong You, Chaoying Pei, R. Dai
The paper develops a feature learning-based method to solve optimal control problems using B-splines to approximate the optimal solutions. The feature learning-based optimal control method can quickly generate near-optimal trajectories for general optimal control problems subject to system dynamics and constraints. The steps in the proposed method are as follows: Firstly, by representing the state and control variables with B-spline functions, the optimal control problem is converted into an approximate nonlinear programming (NLP) problem, where parameters of the B-splines are identified as features of the optimal solution. Secondly, for a specific problem with designated inputs, a dataset of the optimal trajectories under varying inputs is generated by solving the corresponding NLP problem offline. Finally, the neural network is applied to map the relationship between the designated inputs and identified features, represented by the parameters of B-splines and time variables. To show the effectiveness and efficiency of the proposed method for solving the optimal control problems, extensive simulation cases are presented and analyzed.
{"title":"Feature Learning for Optimal Control with B-spline Representations","authors":"Vinay Kenny, Sixiong You, Chaoying Pei, R. Dai","doi":"10.23919/ACC53348.2022.9867713","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867713","url":null,"abstract":"The paper develops a feature learning-based method to solve optimal control problems using B-splines to approximate the optimal solutions. The feature learning-based optimal control method can quickly generate near-optimal trajectories for general optimal control problems subject to system dynamics and constraints. The steps in the proposed method are as follows: Firstly, by representing the state and control variables with B-spline functions, the optimal control problem is converted into an approximate nonlinear programming (NLP) problem, where parameters of the B-splines are identified as features of the optimal solution. Secondly, for a specific problem with designated inputs, a dataset of the optimal trajectories under varying inputs is generated by solving the corresponding NLP problem offline. Finally, the neural network is applied to map the relationship between the designated inputs and identified features, represented by the parameters of B-splines and time variables. To show the effectiveness and efficiency of the proposed method for solving the optimal control problems, extensive simulation cases are presented and analyzed.","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":"116002441","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/ACC53348.2022.9867683
Mahdi Imani, M. Imani, S. F. Ghoreishi
Gene regulatory networks (GRNs) are large and complex dynamical systems often monitored through RNA sequencing or microarray technologies. Genomics studies often focus on a small subset of genes and analyze only these genes due to the huge cost and time-limit constraints. Therefore, selecting a small subset of genes that carries the highest information about the underlying process of these complex systems is highly desired. The existing biomarker selection techniques rely on unrealistic assumptions such as direct observability of genes’ states as well as the availability of perfect knowledge about the modeling process. To address the aforementioned issues, this paper models GRNs with uncertain regulatory models with the signal model of partially-observed Boolean dynamical systems (POBDS) and derives the optimal Bayesian biomarker selection framework given the noisy available gene-expression data. The proposed framework is built on the multiple-model adaptive estimation (MMAE) framework and the optimal minimum mean-square error (MMSE) state estimator for POBDS, called Boolean Kalman smoother (BKS). The proposed framework is an optimal solution relative to the uncertainty class, and its high performance is demonstrated using the mammalian cell-cycle Boolean network model and the p53-MDM2 negative feedback loop observed through gene-expression data.
{"title":"Optimal Bayesian Biomarker Selection for Gene Regulatory Networks under Regulatory Model Uncertainty","authors":"Mahdi Imani, M. Imani, S. F. Ghoreishi","doi":"10.23919/ACC53348.2022.9867683","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867683","url":null,"abstract":"Gene regulatory networks (GRNs) are large and complex dynamical systems often monitored through RNA sequencing or microarray technologies. Genomics studies often focus on a small subset of genes and analyze only these genes due to the huge cost and time-limit constraints. Therefore, selecting a small subset of genes that carries the highest information about the underlying process of these complex systems is highly desired. The existing biomarker selection techniques rely on unrealistic assumptions such as direct observability of genes’ states as well as the availability of perfect knowledge about the modeling process. To address the aforementioned issues, this paper models GRNs with uncertain regulatory models with the signal model of partially-observed Boolean dynamical systems (POBDS) and derives the optimal Bayesian biomarker selection framework given the noisy available gene-expression data. The proposed framework is built on the multiple-model adaptive estimation (MMAE) framework and the optimal minimum mean-square error (MMSE) state estimator for POBDS, called Boolean Kalman smoother (BKS). The proposed framework is an optimal solution relative to the uncertainty class, and its high performance is demonstrated using the mammalian cell-cycle Boolean network model and the p53-MDM2 negative feedback loop observed through gene-expression data.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"20 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":"123608195","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/ACC53348.2022.9867532
Alejandro Murillo-González, J. Poveda
We study the problem of target stabilization with robust obstacle avoidance in robots and vehicles that have access only to vision-based sensors for the purpose of real-time localization. This problem is particularly challenging due to the topological obstructions induced by the obstacle, which preclude the existence of smooth feedback controllers able to achieve simultaneous stabilization and robust obstacle avoidance. To overcome this issue, we develop a vision-based hybrid controller that switches between two different feedback laws depending on the current position of the vehicle using a hysteresis mechanism and a data-assisted supervisor. The main innovation of the paper is the incorporation of suitable perception maps into the hybrid controller. These maps can be learned from data obtained from cameras in the vehicles and trained via convolutional neural networks (CNN). Under suitable assumptions on this perception map, we establish theoretical guarantees for the trajectories of the vehicle in terms of convergence and obstacle avoidance. Moreover, the proposed vision-based hybrid controller is numerically tested under different scenarios, including noisy data, sensors with failures, and cameras with occlusions.
{"title":"Data-Assisted Vision-Based Hybrid Control for Robust Stabilization with Obstacle Avoidance via Learning of Perception Maps","authors":"Alejandro Murillo-González, J. Poveda","doi":"10.23919/ACC53348.2022.9867532","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867532","url":null,"abstract":"We study the problem of target stabilization with robust obstacle avoidance in robots and vehicles that have access only to vision-based sensors for the purpose of real-time localization. This problem is particularly challenging due to the topological obstructions induced by the obstacle, which preclude the existence of smooth feedback controllers able to achieve simultaneous stabilization and robust obstacle avoidance. To overcome this issue, we develop a vision-based hybrid controller that switches between two different feedback laws depending on the current position of the vehicle using a hysteresis mechanism and a data-assisted supervisor. The main innovation of the paper is the incorporation of suitable perception maps into the hybrid controller. These maps can be learned from data obtained from cameras in the vehicles and trained via convolutional neural networks (CNN). Under suitable assumptions on this perception map, we establish theoretical guarantees for the trajectories of the vehicle in terms of convergence and obstacle avoidance. Moreover, the proposed vision-based hybrid controller is numerically tested under different scenarios, including noisy data, sensors with failures, and cameras with occlusions.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"101 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":"122054585","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/ACC53348.2022.9867180
Benjamin Edwards Farber, C. M. Richards
A novel parameter-dependent anti-windup compensator is developed to improve the performance of a constrained model reference adaptive controller. The combined control structure solves the input saturation and stability problem for inertia varying quadcopters. The control synthesis follows the conventional two-step anti-windup design paradigm where a nominal controller is designed without consideration of the input saturation, and the anti-windup compensator is designed to minimize deviations from nominal performance caused by saturated inputs. To account for varying inertia of the quadcopter during package retrieval/delivery routines, the inertia parameters of the vehicle/package are estimated with an online recursive identification technique. These estimates are used by the model reference adaptive controller to ensure stability of the nominal (unsaturated) system and to schedule the parameter-dependent anti-windup compensator. The performance and stability conditions of the parameter-dependent anti-windup compensator are formulated as a set of parameter-dependent linear matrix inequalities. When solved, the linear matrix inequalities yield a gain-scheduled anti-windup compensator that ensures stability and minimizes the deviation from nominal model reference adaptive control performance when saturation occurs. The effectiveness of the combined control scheme is demonstrated by simulations of an input constrained quadcopter lifting a payload of unknown mass.
{"title":"Adaptive Control and Parameter-Dependent Anti-windup Compensation for Inertia Varying Quadcopters*","authors":"Benjamin Edwards Farber, C. M. Richards","doi":"10.23919/ACC53348.2022.9867180","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867180","url":null,"abstract":"A novel parameter-dependent anti-windup compensator is developed to improve the performance of a constrained model reference adaptive controller. The combined control structure solves the input saturation and stability problem for inertia varying quadcopters. The control synthesis follows the conventional two-step anti-windup design paradigm where a nominal controller is designed without consideration of the input saturation, and the anti-windup compensator is designed to minimize deviations from nominal performance caused by saturated inputs. To account for varying inertia of the quadcopter during package retrieval/delivery routines, the inertia parameters of the vehicle/package are estimated with an online recursive identification technique. These estimates are used by the model reference adaptive controller to ensure stability of the nominal (unsaturated) system and to schedule the parameter-dependent anti-windup compensator. The performance and stability conditions of the parameter-dependent anti-windup compensator are formulated as a set of parameter-dependent linear matrix inequalities. When solved, the linear matrix inequalities yield a gain-scheduled anti-windup compensator that ensures stability and minimizes the deviation from nominal model reference adaptive control performance when saturation occurs. The effectiveness of the combined control scheme is demonstrated by simulations of an input constrained quadcopter lifting a payload of unknown mass.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"219 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":"122087553","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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