Pub Date : 2024-12-17DOI: 10.1109/LCSYS.2024.3519435
Kartik A. Pant;Shiraz Khan;Inseok Hwang
The design of sensor spoofing attacks for cyber-physical systems (CPSs) has received considerable attention in the literature, as it can reveal the underlying vulnerabilities of the CPS. We present a dynamic output feedback approach for designing stealthy sensor spoofing attacks against CPSs. Unlike the existing works, we consider the case where the attacker has limited knowledge of the victim CPS’s dynamical model, characterized by polytopic uncertainty. It is shown that despite the limited knowledge of the attacker, the proposed stealthy sensor spoofing attack method can provably avoid detection by the onboard detection mechanism, even in the presence of model uncertainties, measurement noises, and disturbances. Furthermore, we show that the resulting attack design is recursively feasible, i.e., the designed attack at the current time step ensures persistent detection constraint satisfaction throughout the attack. Finally, we demonstrate the effectiveness of our approach through an illustrative numerical simulation of a sensor spoofing attack on a quadrotor.
{"title":"Adversarial Sensor Attacks Against Uncertain Cyber-Physical Systems: A Dynamic Output Feedback Approach","authors":"Kartik A. Pant;Shiraz Khan;Inseok Hwang","doi":"10.1109/LCSYS.2024.3519435","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3519435","url":null,"abstract":"The design of sensor spoofing attacks for cyber-physical systems (CPSs) has received considerable attention in the literature, as it can reveal the underlying vulnerabilities of the CPS. We present a dynamic output feedback approach for designing stealthy sensor spoofing attacks against CPSs. Unlike the existing works, we consider the case where the attacker has limited knowledge of the victim CPS’s dynamical model, characterized by polytopic uncertainty. It is shown that despite the limited knowledge of the attacker, the proposed stealthy sensor spoofing attack method can provably avoid detection by the onboard detection mechanism, even in the presence of model uncertainties, measurement noises, and disturbances. Furthermore, we show that the resulting attack design is recursively feasible, i.e., the designed attack at the current time step ensures persistent detection constraint satisfaction throughout the attack. Finally, we demonstrate the effectiveness of our approach through an illustrative numerical simulation of a sensor spoofing attack on a quadrotor.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2997-3002"},"PeriodicalIF":2.4,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142962892","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 : 2024-12-17DOI: 10.1109/LCSYS.2024.3519301
Mikihisa Yuasa;Huy T. Tran;Ramavarapu S. Sreenivas
Explaining reinforcement learning policies is important for deploying them in real-world scenarios. We introduce a set of linear temporal logic formulae designed to provide such explanations, and an algorithm for searching through those formulae for the one that best explains a given policy. Our key idea is to compare action distributions from the target policy with those from policies optimized for candidate explanations. This comparison provides more insight into the target policy than existing methods and avoids inference of “catch-all” explanations. We demonstrate our method in a simulated game of capture-the-flag, a car-parking environment, and a robot navigation task.
{"title":"On Generating Explanations for Reinforcement Learning Policies: An Empirical Study","authors":"Mikihisa Yuasa;Huy T. Tran;Ramavarapu S. Sreenivas","doi":"10.1109/LCSYS.2024.3519301","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3519301","url":null,"abstract":"Explaining reinforcement learning policies is important for deploying them in real-world scenarios. We introduce a set of linear temporal logic formulae designed to provide such explanations, and an algorithm for searching through those formulae for the one that best explains a given policy. Our key idea is to compare action distributions from the target policy with those from policies optimized for candidate explanations. This comparison provides more insight into the target policy than existing methods and avoids inference of “catch-all” explanations. We demonstrate our method in a simulated game of capture-the-flag, a car-parking environment, and a robot navigation task.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3027-3032"},"PeriodicalIF":2.4,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142962832","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 : 2024-12-17DOI: 10.1109/LCSYS.2024.3519379
Ahmet Kaan Aydin;Md Zulfiqur Haider;Ahmet Özkan Özer
This letter extends a Finite Difference model reduction method to the Euler-Bernoulli beam equation with fully clamped boundary conditions. The corresponding partial differential equation (PDE) is exactly observable in the energy space with a single boundary observer in arbitrarily short observation times. However, standard Finite Difference spatial discretization fails to achieve uniform exact observability as the mesh parameter approaches zero, with minimal observation time potentially depending on the filtering parameter. To address this, we propose a Finite Difference algorithm incorporating an averaging operator and discrete multipliers, leveraging Haraux’s theorem on the spectral gap to ensure uniform observability. This approach eliminates the need for artificial viscosity or Fourier filtering. Our method achieves uniform observability for arbitrarily small times with dual observers-the tip moment and average tip velocity-mirroring results from mixed Finite Elements applied to the wave equation with homogeneous Dirichlet boundary conditions, where dual controllers converge to the single controller of the PDE model [Castro, Micu-Numerische Mathematik’06]. Our reduced model is applicable to more complex systems involving Euler-Bernoulli beam equations.
{"title":"A New Semi-Discretization of the Fully Clamped Euler-Bernoulli Beam Preserving Boundary Observability Uniformly","authors":"Ahmet Kaan Aydin;Md Zulfiqur Haider;Ahmet Özkan Özer","doi":"10.1109/LCSYS.2024.3519379","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3519379","url":null,"abstract":"This letter extends a Finite Difference model reduction method to the Euler-Bernoulli beam equation with fully clamped boundary conditions. The corresponding partial differential equation (PDE) is exactly observable in the energy space with a single boundary observer in arbitrarily short observation times. However, standard Finite Difference spatial discretization fails to achieve uniform exact observability as the mesh parameter approaches zero, with minimal observation time potentially depending on the filtering parameter. To address this, we propose a Finite Difference algorithm incorporating an averaging operator and discrete multipliers, leveraging Haraux’s theorem on the spectral gap to ensure uniform observability. This approach eliminates the need for artificial viscosity or Fourier filtering. Our method achieves uniform observability for arbitrarily small times with dual observers-the tip moment and average tip velocity-mirroring results from mixed Finite Elements applied to the wave equation with homogeneous Dirichlet boundary conditions, where dual controllers converge to the single controller of the PDE model [Castro, Micu-Numerische Mathematik’06]. Our reduced model is applicable to more complex systems involving Euler-Bernoulli beam equations.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2955-2960"},"PeriodicalIF":2.4,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142975846","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 : 2024-12-16DOI: 10.1109/LCSYS.2024.3519013
Amit Dutta;Thinh T. Doan
We revisit the so-called distributed two-time-scale stochastic gradient method for solving a strongly convex optimization problem over a network of agents in a bandwidth-limited regime. In this setting, the agents can only exchange the quantized values of their local variables using a limited number of communication bits. Due to quantization errors, the existing best-known convergence results of this method can only achieve a suboptimal rate $mathcal {O}$ ($1/sqrt {k}$ ), while the optimal rate is $mathcal {O}$ ($1/k$ ) under no quantization, where k is the time iteration. The main contribution of this letter is to address this theoretical gap, where we study a sufficient condition and develop an innovative analysis and step-size selection to achieve the optimal convergence rate $mathcal {O}$ ($1/k$ ) for the distributed gradient methods given any number of quantization bits. We provide numerical simulations to illustrate the effectiveness of our theoretical results.
{"title":"On the O(1/k) Convergence of Distributed Gradient Methods Under Random Quantization","authors":"Amit Dutta;Thinh T. Doan","doi":"10.1109/LCSYS.2024.3519013","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3519013","url":null,"abstract":"We revisit the so-called distributed two-time-scale stochastic gradient method for solving a strongly convex optimization problem over a network of agents in a bandwidth-limited regime. In this setting, the agents can only exchange the quantized values of their local variables using a limited number of communication bits. Due to quantization errors, the existing best-known convergence results of this method can only achieve a suboptimal rate <inline-formula> <tex-math>$mathcal {O}$ </tex-math></inline-formula>(<inline-formula> <tex-math>$1/sqrt {k}$ </tex-math></inline-formula>), while the optimal rate is <inline-formula> <tex-math>$mathcal {O}$ </tex-math></inline-formula>(<inline-formula> <tex-math>$1/k$ </tex-math></inline-formula>) under no quantization, where k is the time iteration. The main contribution of this letter is to address this theoretical gap, where we study a sufficient condition and develop an innovative analysis and step-size selection to achieve the optimal convergence rate <inline-formula> <tex-math>$mathcal {O}$ </tex-math></inline-formula>(<inline-formula> <tex-math>$1/k$ </tex-math></inline-formula>) for the distributed gradient methods given any number of quantization bits. We provide numerical simulations to illustrate the effectiveness of our theoretical results.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2967-2972"},"PeriodicalIF":2.4,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142975932","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 : 2024-12-16DOI: 10.1109/LCSYS.2024.3518396
Pengfei Wang;Emilia Fridman
This letter investigates the finite-dimensional observer-based boundary control for 1D linear parabolic-elliptic systems via the modal decomposition method. To address the potential multiple eigenvalues arising from the elliptic equation, we implement bilateral actuations (one Dirichlet and one Neumann) on the boundary of the parabolic equation with two point measurements. When the eigenvalues are simple, one boundary actuation and one point measurement are sufficient, but the second input and output may reduce the observer dimension. We present efficient LMI conditions for finding observer dimension, as well as controller and observer gains, ensuring the ${mathrm { H}}^{1}$ exponential stability with any desirable decay rate. We show that the LMIs are always feasible for large enough values of the observer dimension. Numerical examples demonstrate the efficiency of the method.
{"title":"Finite-Dimensional Observer-Based Boundary Control of 1-D Linear Parabolic-Elliptic Systems","authors":"Pengfei Wang;Emilia Fridman","doi":"10.1109/LCSYS.2024.3518396","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3518396","url":null,"abstract":"This letter investigates the finite-dimensional observer-based boundary control for 1D linear parabolic-elliptic systems via the modal decomposition method. To address the potential multiple eigenvalues arising from the elliptic equation, we implement bilateral actuations (one Dirichlet and one Neumann) on the boundary of the parabolic equation with two point measurements. When the eigenvalues are simple, one boundary actuation and one point measurement are sufficient, but the second input and output may reduce the observer dimension. We present efficient LMI conditions for finding observer dimension, as well as controller and observer gains, ensuring the <inline-formula> <tex-math>${mathrm { H}}^{1}$ </tex-math></inline-formula> exponential stability with any desirable decay rate. We show that the LMIs are always feasible for large enough values of the observer dimension. Numerical examples demonstrate the efficiency of the method.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2943-2948"},"PeriodicalIF":2.4,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142975929","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 : 2024-12-16DOI: 10.1109/LCSYS.2024.3519014
Nilay Kant;Ranjan Mukherjee
The dynamics of an oscillator, which exhibits a stable equilibrium and a stable limit cycle, is investigated. We refer to it as a bistable oscillator unit (BOU) and show that two coupled BOUs (CBOUs) exhibit dynamics analogous to neural activity patterns in epilepsy, including healthy, localized, and fully spread epileptic states. By treating each CBOU as an independent system influenced by the state of the other, we establish local input-to-state stability near the equilibrium and the limit cycle, and estimate the ultimate bounds of the trajectories. Our analysis identifies the domain of the initial conditions and estimates the coupling parameter critical in determining the epileptic behavior of the CBOUs. The actual value of the coupling parameter, above which the dynamics transition from localized to fully spread epileptic states, is determined through simulations; the results closely match the derived analytical estimate.
{"title":"Investigating Bistable Dynamics of Coupled Oscillators With Similarities to Neural Activity in Epilepsy","authors":"Nilay Kant;Ranjan Mukherjee","doi":"10.1109/LCSYS.2024.3519014","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3519014","url":null,"abstract":"The dynamics of an oscillator, which exhibits a stable equilibrium and a stable limit cycle, is investigated. We refer to it as a bistable oscillator unit (BOU) and show that two coupled BOUs (CBOUs) exhibit dynamics analogous to neural activity patterns in epilepsy, including healthy, localized, and fully spread epileptic states. By treating each CBOU as an independent system influenced by the state of the other, we establish local input-to-state stability near the equilibrium and the limit cycle, and estimate the ultimate bounds of the trajectories. Our analysis identifies the domain of the initial conditions and estimates the coupling parameter critical in determining the epileptic behavior of the CBOUs. The actual value of the coupling parameter, above which the dynamics transition from localized to fully spread epileptic states, is determined through simulations; the results closely match the derived analytical estimate.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2919-2924"},"PeriodicalIF":2.4,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976173","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}
This letter is concerned with developing a data-driven approach for learning control barrier certificates (CBCs) and associated safety controllers for discrete-time input-affine nonlinear systems with polynomial dynamics with (partially) unknown mathematical models, guaranteeing system safety over an infinite time horizon. The proposed approach leverages measured data acquired through an input-state observation, referred to as a single trajectory, collected over a specified time horizon. By fulfilling a certain rank condition, which ensures the unknown system is persistently excited by the collected data, we design a CBC and its corresponding safety controller directly from the finite-length observed data, without explicitly identifying the unknown dynamical system. This is achieved through proposing a data-based sum-of-squares optimization (SOS) program to systematically design CBCs and their safety controllers. We validate our data-driven approach over two physical case studies including a jet engine and a Lorenz system, demonstrating the efficacy of our proposed method.
{"title":"From a Single Trajectory to Safety Controller Synthesis of Discrete-Time Nonlinear Polynomial Systems","authors":"Behrad Samari;Omid Akbarzadeh;Mahdieh Zaker;Abolfazl Lavaei","doi":"10.1109/LCSYS.2024.3519017","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3519017","url":null,"abstract":"This letter is concerned with developing a data-driven approach for learning control barrier certificates (CBCs) and associated safety controllers for discrete-time input-affine nonlinear systems with polynomial dynamics with (partially) unknown mathematical models, guaranteeing system safety over an infinite time horizon. The proposed approach leverages measured data acquired through an input-state observation, referred to as a single trajectory, collected over a specified time horizon. By fulfilling a certain rank condition, which ensures the unknown system is persistently excited by the collected data, we design a CBC and its corresponding safety controller directly from the finite-length observed data, without explicitly identifying the unknown dynamical system. This is achieved through proposing a data-based sum-of-squares optimization (SOS) program to systematically design CBCs and their safety controllers. We validate our data-driven approach over two physical case studies including a jet engine and a Lorenz system, demonstrating the efficacy of our proposed method.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3123-3128"},"PeriodicalIF":2.4,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142962890","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 : 2024-12-16DOI: 10.1109/LCSYS.2024.3518912
Nicholas Rober;Jonathan P. How
This letter presents a method to efficiently reduce conservativeness in reachable set over approximations (RSOAs) to verify safety for neural feedback loops (NFLs), i.e., systems that have neural networks in their control pipelines. While generating RSOAs is a tractable alternative to calculating exact reachable sets, RSOAs can be overly conservative, especially when generated over long time horizons or for highly nonlinear NN control policies. Refinement strategies such as partitioning or symbolic propagation are typically used to limit the conservativeness of RSOAs, but these approaches come with a high computational cost and often can only be used to verify safety for simple reachability problems. This letter presents Constraint-Aware Refinement for Verification (CARV): an efficient refinement strategy that reduces the conservativeness of RSOAs by explicitly using the safety constraints on the NFL. Unlike existing approaches that seek to refine RSOAs over the entire time horizon, CARV limits the computational cost of refinement by refining RSOAs only where necessary to verify safety. We demonstrate that CARV can verify the safety of an NFL where other approaches either fail or take more than $60times $ longer and $40times $ the memory.
{"title":"Constraint-Aware Refinement for Safety Verification of Neural Feedback Loops","authors":"Nicholas Rober;Jonathan P. How","doi":"10.1109/LCSYS.2024.3518912","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3518912","url":null,"abstract":"This letter presents a method to efficiently reduce conservativeness in reachable set over approximations (RSOAs) to verify safety for neural feedback loops (NFLs), i.e., systems that have neural networks in their control pipelines. While generating RSOAs is a tractable alternative to calculating exact reachable sets, RSOAs can be overly conservative, especially when generated over long time horizons or for highly nonlinear NN control policies. Refinement strategies such as partitioning or symbolic propagation are typically used to limit the conservativeness of RSOAs, but these approaches come with a high computational cost and often can only be used to verify safety for simple reachability problems. This letter presents Constraint-Aware Refinement for Verification (CARV): an efficient refinement strategy that reduces the conservativeness of RSOAs by explicitly using the safety constraints on the NFL. Unlike existing approaches that seek to refine RSOAs over the entire time horizon, CARV limits the computational cost of refinement by refining RSOAs only where necessary to verify safety. We demonstrate that CARV can verify the safety of an NFL where other approaches either fail or take more than \u0000<inline-formula> <tex-math>$60times $ </tex-math></inline-formula>\u0000 longer and \u0000<inline-formula> <tex-math>$40times $ </tex-math></inline-formula>\u0000 the memory.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"3219-3224"},"PeriodicalIF":2.4,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938057","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 : 2024-12-16DOI: 10.1109/LCSYS.2024.3518393
Pio Ong;Max H. Cohen;Tamas G. Molnar;Aaron D. Ames
This letter presents a novel approach for synthesizing control barrier functions (CBFs) from high relative degree safety constraints: Rectified CBFs (ReCBFs). We begin by discussing the limitations of existing High-Order CBF approaches and how these can be overcome by incorporating an activation function into the CBF construction. We then provide a comparative analysis of our approach with related methods, such as CBF backstepping. Our results are presented first for safety constraints with relative degree two, then for mixed-input relative degree constraints, and finally for higher relative degrees. The theoretical developments are illustrated through simple running examples and an aircraft control problem.
{"title":"Rectified Control Barrier Functions for High-Order Safety Constraints","authors":"Pio Ong;Max H. Cohen;Tamas G. Molnar;Aaron D. Ames","doi":"10.1109/LCSYS.2024.3518393","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3518393","url":null,"abstract":"This letter presents a novel approach for synthesizing control barrier functions (CBFs) from high relative degree safety constraints: Rectified CBFs (ReCBFs). We begin by discussing the limitations of existing High-Order CBF approaches and how these can be overcome by incorporating an activation function into the CBF construction. We then provide a comparative analysis of our approach with related methods, such as CBF backstepping. Our results are presented first for safety constraints with relative degree two, then for mixed-input relative degree constraints, and finally for higher relative degrees. The theoretical developments are illustrated through simple running examples and an aircraft control problem.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2949-2954"},"PeriodicalIF":2.4,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976171","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 : 2024-12-16DOI: 10.1109/LCSYS.2024.3519033
Jinfeng Chen;Fan Zhang;Bin Hu;Qin Lin
Fault-tolerant control of a quadrotor in extreme conditions, such as rotor failure and strong winds, is exceptionally challenging due to its underactuated nature, strong mismatched disturbances, and highly nonlinear multi-input and multi-output properties. This letter proposes a reduced attitude control approach that combines a model-based extended state observer (MB-ESO) and mismatched disturbance decoupling to control a quadrotor under strong winds and complete loss of two opposing rotors. Our MB-ESO based control provides a new theoretical framework for more general nonlinear systems by utilizing all measurable outputs, thereby maximizing the use of all available information to design a robust controller. Testing in a high-fidelity simulator shows that our approach outperforms the state-of-the-art Incremental Nonlinear Dynamic Inversion method.
{"title":"Quadrotor Fault-Tolerant Control at High Speed: A Model-Based Extended State Observer for Mismatched Disturbance Rejection Approach","authors":"Jinfeng Chen;Fan Zhang;Bin Hu;Qin Lin","doi":"10.1109/LCSYS.2024.3519033","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3519033","url":null,"abstract":"Fault-tolerant control of a quadrotor in extreme conditions, such as rotor failure and strong winds, is exceptionally challenging due to its underactuated nature, strong mismatched disturbances, and highly nonlinear multi-input and multi-output properties. This letter proposes a reduced attitude control approach that combines a model-based extended state observer (MB-ESO) and mismatched disturbance decoupling to control a quadrotor under strong winds and complete loss of two opposing rotors. Our MB-ESO based control provides a new theoretical framework for more general nonlinear systems by utilizing all measurable outputs, thereby maximizing the use of all available information to design a robust controller. Testing in a high-fidelity simulator shows that our approach outperforms the state-of-the-art Incremental Nonlinear Dynamic Inversion method.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2895-2900"},"PeriodicalIF":2.4,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890151","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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