IEEE open journal of control systems最新文献

{"title":"Abstraction-Based Planning for Uncertainty-Aware Legged Navigation","authors":"Jesse Jiang;Samuel Coogan;Ye Zhao","doi":"10.1109/OJCSYS.2023.3296000","DOIUrl":"https://doi.org/10.1109/OJCSYS.2023.3296000","url":null,"abstract":"This article addresses the problem of temporal-logic-based planning for bipedal robots in uncertain environments. We first propose an Interval Markov Decision Process abstraction of bipedal locomotion (IMDP-BL). Motion perturbations from multiple sources of uncertainty are incorporated into our model using stacked Gaussian process learning in order to achieve formal guarantees on the behavior of the system. We consider tasks which can be specified using Linear Temporal Logic (LTL). Through a product IMDP construction combining the IMDP-BL of the bipedal robot and a Deterministic Rabin Automaton (DRA) of the specifications, we synthesize control policies which allow the robot to safely traverse the environment, iteratively learning the unknown dynamics until the specifications can be satisfied with satisfactory probability. We demonstrate our methods with simulation case studies.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"2 ","pages":"221-234"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552933/9973428/10184473.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50375007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robustly Complete Finite-State Abstractions for Control Synthesis of Stochastic Systems","authors":"Yiming Meng;Jun Liu","doi":"10.1109/OJCSYS.2023.3294829","DOIUrl":"https://doi.org/10.1109/OJCSYS.2023.3294829","url":null,"abstract":"The essential step of abstraction-based control synthesis for nonlinear systems to satisfy a given specification is to obtain a finite-state abstraction of the original systems. The complexity of the abstraction is usually the dominating factor that determines the efficiency of the algorithm. For the control synthesis of discrete-time nonlinear stochastic systems modelled by nonlinear stochastic difference equations, recent literature has demonstrated the soundness of abstractions in preserving robust probabilistic satisfaction of \u0000<inline-formula><tex-math>$omega$</tex-math></inline-formula>\u0000-regular linear-time properties. However, unnecessary transitions exist within the abstractions, which are difficult to quantify, and the completeness of abstraction-based control synthesis in the stochastic setting remains an open theoretical question. In this article, we address this fundamental question from the topological view of metrizable space of probability measures, and propose constructive finite-state abstractions for control synthesis of probabilistic linear temporal specifications. Such abstractions are both sound and approximately complete. That is, given a concrete discrete-time stochastic system and an arbitrarily small \u0000<inline-formula><tex-math>$mathcal{L}^{1}$</tex-math></inline-formula>\u0000-perturbation of this system, there exists a family of finite-state controlled Markov chains that both abstracts the concrete system and is abstracted by the slightly perturbed system. In other words, given an arbitrarily small prescribed precision, an abstraction always exists to decide whether a control strategy exists for the concrete system to satisfy the probabilistic specification.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"2 ","pages":"235-248"},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552933/9973428/10179944.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50375008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Restructuring Dynamical Systems for Inductive Verification","authors":"Vishnu Murali;Ashutosh Trivedi;Majid Zamani","doi":"10.1109/OJCSYS.2023.3294098","DOIUrl":"https://doi.org/10.1109/OJCSYS.2023.3294098","url":null,"abstract":"Inductive approaches to deductive verification has gained widespread adoption in the control and verification of safety-critical dynamical systems. The practical success of barrier certificates attests to their effectiveness and ongoing theoretical and practical refinement. However, when verification conditions are non-inductive, various strategies are employed to address this issue. One strategy is to \u0000<italic>strengthen</i>\u0000 the property until they arrive at an inductive proof. However, it is not always obvious how one must strengthen a property. Notions of strenghtening are particularly non-obvious when the properties of interest are more expressive than safety or reachability. An alternative technique is to instead consider \u0000<italic>structural</i>\u0000 changes. These structural changes may either be to consider novel notions of induction such as \u0000<inline-formula><tex-math>$k$</tex-math></inline-formula>\u0000-induction, or to encode additional information similar to dimension lifting. We posit that reformulating or \u0000<italic>restructuring</i>\u0000 of the system is fundamental to inductive approaches. This position article provides an overview of barrier certificate based verification approaches and their connection to system restructuring. We discuss the opportunities, challenges, and open problems in this emerging field, paving the way for future research in the verification of safety-critical dynamical systems. The framework of restructuring of a system holds promise for advancing deductive verification, enhancing system safety, and promoting design insights.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"2 ","pages":"200-207"},"PeriodicalIF":0.0,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552933/9973428/10179178.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50375004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-Driven Tube-Based Stochastic Predictive Control","authors":"SEBASTIAN Kerz;JOHANNES Teutsch;TIM Brüdigam;MARION Leibold;DIRK Wollherr","doi":"10.1109/OJCSYS.2023.3291596","DOIUrl":"https://doi.org/10.1109/OJCSYS.2023.3291596","url":null,"abstract":"A powerful result from behavioral systems theory known as the fundamental lemma allows for predictive control akin to Model Predictive Control (MPC) for linear time-invariant (LTI) systems with unknown dynamics purely from data. While most data-driven predictive control literature focuses on robustness with respect to measurement noise, only a few works consider exploiting probabilistic information of disturbances for performance-oriented control as in stochastic MPC. This work proposes a novel data-driven stochastic predictive control scheme for chance-constrained LTI systems subject to measurement noise and additive stochastic disturbances. In order to render the otherwise stochastic and intractable optimal control problem deterministic, our approach leverages ideas from tube-based MPC by decomposing the state into a deterministic nominal state driven by inputs and a stochastic error state affected by disturbances. Satisfaction of original chance constraints is guaranteed by tightening nominal constraints probabilistically with respect to additive disturbances and robustly with respect to measurement noise. The resulting data-driven receding horizon optimal control problem is lightweight, recursively feasible, and renders the closed loop input-to-state stable in the presence of both additive disturbances and measurement noise. We demonstrate the effectiveness of the proposed approach in a simulation example.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"2 ","pages":"185-199"},"PeriodicalIF":0.0,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552933/9973428/10171461.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50226359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Attack-Resilient Supervisory Control of Discrete-Event Systems: A Finite-State Transducer Approach","authors":"Yu Wang;Alper Kamil Bozkurt;Nathan Smith;Miroslav Pajic","doi":"10.1109/OJCSYS.2023.3290408","DOIUrl":"https://doi.org/10.1109/OJCSYS.2023.3290408","url":null,"abstract":"Resilience to sensor and actuator attacks is a major concern in the supervisory control of discrete events in cyber-physical systems (CPS). In this work, we propose a new framework to design supervisors for CPS under attacks using finite-state transducers (FSTs) to model the effects of the discrete events. FSTs can capture a general class of regular-rewriting attacks in which an attacker can nondeterministically rewrite sensing/actuation events according to a given regular relation. These include common insertion, deletion, event-wise replacement, and finite-memory replay attacks. We propose new theorems and algorithms with polynomial complexity to design resilient supervisors against these attacks. We also develop an open-source tool in Python based on the results and illustrate its applicability through a case study.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"2 ","pages":"208-220"},"PeriodicalIF":0.0,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552933/9973428/10167797.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50375005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Closed-Loop Kinematic and Indirect Force Control of a Cable-Driven Knee Exoskeleton: A Lyapunov-Based Switched Systems Approach","authors":"Chen-Hao Chang;Jonathan Casas;Victor H. Duenas","doi":"10.1109/OJCSYS.2023.3289771","DOIUrl":"https://doi.org/10.1109/OJCSYS.2023.3289771","url":null,"abstract":"Lower-limb exoskeletons can aid restoring mobility in people with movement disorders. Cable-driven exoskeletons can offload their actuators away from the human body to reduce the weight imposed on the user and enable precise control of joints. However, ensuring limb coordination through bidirectional motion control of joints using cables raise the technical challenge of preventing the occurrence of undesired cable slackness or counteracting forces between cables. Thus, motivation exists to develop a control design framework that integrates both a joint control loop to ensure suitable limb tracking and a cable control loop to maintain cable tension properly. In this article, a two-layer control structure consisting of high and low-level controllers are developed to ensure a knee-joint exoskeleton system follows the desired joint trajectories and adjusts the cable tension, respectively. A repetitive learning controller is designed for the high-level knee joint tracking objective motivated by the periodic nature of the desired leg swings (i.e., to achieve knee flexion and extension). Low-level robust controllers are developed for a pair of cables, each actuated by an electric motor, to track target motor trajectories composed of motor kinematics and offset angles to mitigate cable slackness. The offset angles are computed using admittance models that exploit measurements of the cable tensions as inputs. Each electric motor switches its role between tracking the knee joint trajectory (i.e., the motor acts as the leader motor to achieve flexion or extension) and implementing the low-level controller (i.e., the motor acts as the follower motor to reduce slackness). Hence, at any time, one motor is the leader and the other is the follower. A Lyapunov-based stability analysis is developed for the high-level joint controller to ensure global asymptotic tracking and the low-level follower controller to guarantee global exponential tracking. The designed controllers are implemented during leg swing experiments in six able-bodied individuals while wearing the knee joint cable-driven exoskeleton. A comparison of the results obtained in two trials with and without using the admittance model (i.e., exploiting cable tension measurements) is presented. The experimental results indicate improved knee joint tracking performance, smaller control input magnitudes, and reduced cable slackness in the trial that leveraged cable tension feedback compared to the trial that did not exploit tension feedback.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"2 ","pages":"171-184"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552933/9973428/10163824.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50376115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual Control of Coupled Oscillator Networks","authors":"Per Sebastian Skardal;Alex Arenas","doi":"10.1109/OJCSYS.2023.3282438","DOIUrl":"https://doi.org/10.1109/OJCSYS.2023.3282438","url":null,"abstract":"Robust coordination and organization in large ensembles of nonlinear oscillatory units play a vital role in a wide range of natural and engineered system. The control of self-organizing network-coupled systems has recently seen significant attention, but largely in the context of modifying or augmenting existing structures. This leaves a gap in our understanding of reactive control, where and how to design direct interventions, and what we may learn about structure and dynamics from such control strategies. Here we study reactive control of coupled oscillator networks and demonstrate dual control strategies, i.e., two different mechanisms for control, that may each be implemented on their own and interchangeably to achieve synchronization. These diverse strategies exploit different network properties, with the first directly targeting oscillators that are challenging to entrain, and the second focusing on oscillators with a strong influence on others. Thus, in addition to presenting alternative strategies for network control, the distinct control sets illuminate the oscillators' dynamical and structural roles within the system. The applicability of dual control is demonstrated using both synthetic and real networks.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"2 ","pages":"146-154"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552933/9973428/10143236.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50376174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Backward Reachability Analysis of Neural Feedback Loops: Techniques for Linear and Nonlinear Systems","authors":"Nicholas Rober;Sydney M. Katz;Chelsea Sidrane;Esen Yel;Michael Everett;Mykel J. Kochenderfer;Jonathan P. How","doi":"10.1109/OJCSYS.2023.3265901","DOIUrl":"https://doi.org/10.1109/OJCSYS.2023.3265901","url":null,"abstract":"As neural networks (NNs) become more prevalent in safety-critical applications such as control of vehicles, there is a growing need to certify that systems with NN components are safe. This paper presents a set of backward reachability approaches for safety certification of neural feedback loops (NFLs), i.e., closed-loop systems with NN control policies. While backward reachability strategies have been developed for systems without NN components, the nonlinearities in NN activation functions and general noninvertibility of NN weight matrices make backward reachability for NFLs a challenging problem. To avoid the difficulties associated with propagating sets backward through NNs, we introduce a framework that leverages standard forward NN analysis tools to efficiently find over-approximations to backprojection (BP) sets, i.e., sets of states for which an NN policy will lead a system to a given target set. We present frameworks for calculating BP over-approximations for both linear and nonlinear systems with control policies represented by feedforward NNs and propose computationally efficient strategies. We use numerical results from a variety of models to showcase the proposed algorithms, including a demonstration of safety certification for a 6D system.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"2 ","pages":"108-124"},"PeriodicalIF":0.0,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552933/9973428/10097878.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50376173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distributed Data-Driven Control of Network Systems","authors":"Federico Celi;Giacomo Baggio;Fabio Pasqualetti","doi":"10.1109/OJCSYS.2023.3259228","DOIUrl":"https://doi.org/10.1109/OJCSYS.2023.3259228","url":null,"abstract":"Imperfect models lead to imperfect controllers and deriving accurate models from first principles or system identification is especially challenging in networked systems. Instead, data can be used to directly compute controllers, without requiring any system identification or modeling. In this paper we propose a strategy to directly learn control actions when data from past system trajectories is distributed among multiple agents in a network. The approach we develop provably converges to a suboptimal solution in a finite number of steps, bounded by the diameter of the network, and with a sub-optimality gap that can be characterized as a function of data, and that can be made arbitrarily small. We further characterize the robustness properties of our approach and give provable guarantees on its performance when data are affected by noise or by a class of attacks.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"2 ","pages":"93-107"},"PeriodicalIF":0.0,"publicationDate":"2023-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552933/9973428/10076813.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50376172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Policy Evaluation in Decentralized POMDPs With Belief Sharing","authors":"Mert Kayaalp;Fatima Ghadieh;Ali H. Sayed","doi":"10.1109/OJCSYS.2023.3277760","DOIUrl":"https://doi.org/10.1109/OJCSYS.2023.3277760","url":null,"abstract":"Most works on multi-agent reinforcement learning focus on scenarios where the state of the environment is fully observable. In this work, we consider a cooperative policy evaluation task in which agents are not assumed to observe the environment state directly. Instead, agents can only have access to noisy observations and to belief vectors. It is well-known that finding global posterior distributions under multi-agent settings is generally NP-hard. As a remedy, we propose a fully decentralized belief forming strategy that relies on individual updates and on localized interactions over a communication network. In addition to the exchange of the beliefs, agents exploit the communication network by exchanging value function parameter estimates as well. We analytically show that the proposed strategy allows information to diffuse over the network, which in turn allows the agents' parameters to have a bounded difference with a centralized baseline. A multi-sensor target tracking application is considered in the simulations.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"2 ","pages":"125-145"},"PeriodicalIF":0.0,"publicationDate":"2023-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552933/9973428/10129007.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50226357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}