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Relevant bibliographies by topics / Barrier Lyapunov functionals

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Academic literature on the topic 'Barrier Lyapunov functionals'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "Barrier Lyapunov functionals"

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Li, Dong-Juan, Jing Li, and Shu Li. "Adaptive control of nonlinear systems with full state constraints using Integral Barrier Lyapunov Functionals." Neurocomputing 186 (April 2016): 90–96. http://dx.doi.org/10.1016/j.neucom.2015.12.075.

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Wu, Yuxiang, Tian Xu, and Hongqiang Mo. "Adaptive tracking control for nonlinear time-delay systems with time-varying full state constraints." Transactions of the Institute of Measurement and Control 42, no. 12 (2020): 2178–90. http://dx.doi.org/10.1177/0142331220908987.

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This paper presents an adaptive tracking control approach for a class of uncertain nonlinear strict-feedback systems subject to time-varying full state constraints and time-delays. To stabilize such systems, an adaptive tracking controller is structured by combining the neural networks and the backstepping technique. To guarantee all states do not violate the time-varying constraint sets, the appropriate time-varying Barrier Lyapunov functions are employed at each stage of the backstepping procedure. By using the Lyapunov-Krasovskii functionals, the effect of time delay is eliminated. It is pr

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Liu, Yan-Jun, Shaocheng Tong, C. L. Philip Chen, and Dong-Juan Li. "Adaptive NN Control Using Integral Barrier Lyapunov Functionals for Uncertain Nonlinear Block-Triangular Constraint Systems." IEEE Transactions on Cybernetics 47, no. 11 (2017): 3747–57. http://dx.doi.org/10.1109/tcyb.2016.2581173.

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Li, Dongjuan, Dongxing Wang, and Ying Gao. "Adaptive Neural Control and Modeling for Continuous Stirred Tank Reactor with Delays and Full State Constraints." Complexity 2021 (October 7, 2021): 1–12. http://dx.doi.org/10.1155/2021/9948044.

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In this paper, an adaptive neural network control method is described to stabilize a continuous stirred tank reactor (CSTR) subject to unknown time-varying delays and full state constraints. The unknown time delay and state constraints problem of the concentration in the reactor seriously affect the input-output ratio and stability of the entire system. Therefore, the design difficulty of this control scheme is how to debar the effect of time delay in CSTR systems. To deal with time-varying delays, Lyapunov–Krasovskii functionals (LKFs) are utilized in the adaptive controller design. The conve

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Ai, Xiang, Ya Zhang, and Yang-Yang Chen. "Spherical Formation Tracking Control of Non-Holonomic UAVs with State Constraints and Time Delays." Aerospace 10, no. 2 (2023): 118. http://dx.doi.org/10.3390/aerospace10020118.

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This paper addresses a novel spherical formation tracking control problem of multiple UAVs with time-varying delays in the directed communication network, where the dynamics of each UAV is non-holonomic and in the presence of spatiotemporal flowfields. The state constraints (that is, position and velocity constraints) are derived from our previous differential geometry method and the F–S formulas. The state constraints and time delays in the directed communication network bring many difficulties to controller design. To this end, a virtual-structure-like design is given to achieve a formation

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Li, Menghan, Shaobo Li, Junxing Zhang, Fengbin Wu, and Tao Zhang. "Neural Adaptive Funnel Dynamic Surface Control with Disturbance-Observer for the PMSM with Time Delays." Entropy 24, no. 8 (2022): 1028. http://dx.doi.org/10.3390/e24081028.

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This paper suggests an adaptive funnel dynamic surface control method with a disturbance observer for the permanent magnet synchronous motor with time delays. An improved prescribed performance function is integrated with a modified funnel variable at the beginning of the controller design to coordinate the permanent magnet synchronous motor with the output constrained into an unconstrained one, which has a faster convergence rate than ordinary barrier Lyapunov functions. Then, the specific controller is devised by the dynamic surface control technique with first-order filters to the unconstra

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Zhao, Wei, Yanjun Liu, and Lei Liu. "Observer-Based Adaptive Fuzzy Tracking Control Using Integral Barrier Lyapunov Functionals for A Nonlinear System With Full State Constraints." IEEE/CAA Journal of Automatica Sinica 8, no. 3 (2021): 617–27. http://dx.doi.org/10.1109/jas.2021.1003877.

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Wei, Yan, Pingfang Zhou, Yueying Wang, Dengping Duan, and Jiwei Tang. "Adaptive finite-time neural backstepping control for multiple-input–multiple-output uncertain nonlinear systems with full state constraints." Transactions of the Institute of Measurement and Control 43, no. 11 (2021): 2450–60. http://dx.doi.org/10.1177/0142331221989121.

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This paper investigates the issue of finite-time tracking control for multiple-input–multiple-output nonlinear systems subject to uncertainties and full state constraints. To deal with full state constraints directly, integral barrier Lyapunov functionals (iBLF) are introduced. By using finite-time stability theory, an iBLF-based adaptive finite-time neural control scheme is presented. To solve the problem of “explosion of complexity” in the design of traditional backstepping control, a new finite-time convergent differentiator is presented. Through stability analysis, all closed-loop signals

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Xu, Tian, Yuxiang Wu, Haoran Fang, and Fuxi Wan. "Adaptive finite-time tracking control for full state constrained nonlinear systems with time-varying delays and input saturation." Transactions of the Institute of Measurement and Control 44, no. 9 (2021): 1824–35. http://dx.doi.org/10.1177/01423312211065851.

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This paper investigates the adaptive finite-time tracking control problem for a class of nonlinear full state constrained systems with time-varying delays and input saturation. Compared with the previously published work, the considered system involves unknown time-varying delays, asymmetric input saturation, and time-varying asymmetric full state constraints. To ensure the state constraint satisfaction, the appropriate time-varying asymmetric Barrier Lyapunov Functions and the backstepping technique are utilized. Meanwhile, the finite covering lemma and the radial basis function neural networ

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Luo, Zhenguo, Liping Luo, Liu Yang, Zhenghui Gao, and Yunhui Zeng. "Existence and Uniqueness of Positive Periodic Solutions for a Delayed Predator-Prey Model with Dispersion and Impulses." Journal of Applied Mathematics 2014 (2014): 1–21. http://dx.doi.org/10.1155/2014/592543.

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An impulsive Lotka-Volterra type predator-prey model with prey dispersal in two-patch environments and time delays is investigated, where we assume the model of patches with a barrier only as far as the prey population is concerned, whereas the predator population has no barriers between patches. By applying the continuation theorem of coincidence degree theory and by means of a suitable Lyapunov functional, a set of easily verifiable sufficient conditions are obtained to guarantee the existence, uniqueness, and global stability of positive periodic solutions of the system. Some known results

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Dissertations / Theses on the topic "Barrier Lyapunov functionals"

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Annam, Chandrakanth. "Advanced Guidance Laws for Field-of-View and Impact Angle Constrained Engagements." Thesis, 2020. https://etd.iisc.ac.in/handle/2005/4820.

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This thesis deals with the development of guidance laws for interceptors with seeker field-of-view (FOV) and impact angle constraints. Two classes of guidance problems, namely, field-of- view and impact angle constrained guidance; midcourse guidance of dual pulse interceptors with look angle constraints, are considered in this thesis. In the first problem, decision variables are lateral acceleration commands whereas the second problem has an additional decision variable of second thrust pulse firing time. For the first problem, three guidance laws are proposed using nonlinear control the

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Book chapters on the topic "Barrier Lyapunov functionals"

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Kabziński, Jacek, Przemysław Mosiołek, and Marcin Jastrzębski. "Adaptive Position Tracking with Hard Constraints—Barrier Lyapunov Functions Approach." In Advanced Control of Electrical Drives and Power Electronic Converters. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45735-2_2.

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Chatterjee, Krishnendu, Thomas A. Henzinger, Mathias Lechner, and Đorđe Žikelić. "A Learner-Verifier Framework for Neural Network Controllers and Certificates of Stochastic Systems." In Tools and Algorithms for the Construction and Analysis of Systems. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30823-9_1.

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AbstractReinforcement learning has received much attention for learning controllers of deterministic systems. We consider a learner-verifier framework for stochastic control systems and survey recent methods that formally guarantee a conjunction of reachability and safety properties. Given a property and a lower bound on the probability of the property being satisfied, our framework jointly learns a control policy and a formal certificate to ensure the satisfaction of the property with a desired probability threshold. Both the control policy and the formal certificate are continuous functions from states to reals, which are learned as parameterized neural networks. While in the deterministic case, the certificates are invariant and barrier functions for safety, or Lyapunov and ranking functions for liveness, in the stochastic case the certificates are supermartingales. For certificate verification, we use interval arithmetic abstract interpretation to bound the expected values of neural network functions.

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Reis, Matheus F., Pallov Anand, and A. Pedro Aguiar. "Cooperative Path Following with Collision Avoidance Guarantees Using Control Lyapunov and Barrier Functions." In CONTROLO 2022. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10047-5_16.

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Truong, Hoa Thi, and Xuan Bao Nguyen. "Adaptive Control Using Barrier Lyapunov Functions for Omnidirectional Mobile Robot with Time-Varying State Constraints." In Advances in Asian Mechanism and Machine Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-91892-7_38.

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Yang, Zhengfeng, Li Zhang, Xia Zeng, Xiaochao Tang, Chao Peng, and Zhenbing Zeng. "Hybrid Controller Synthesis for Nonlinear Systems Subject to Reach-Avoid Constraints." In Computer Aided Verification. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37706-8_16.

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AbstractThere is a pressing need for learning controllers to endow systems with properties of safety and goal-reaching, which are crucial for many safety-critical systems. Reinforcement learning (RL) has been deployed successfully to synthesize controllers from user-defined reward functions encoding desired system requirements. However, it remains a significant challenge in synthesizing provably correct controllers with safety and goal-reaching requirements. To address this issue, we try to design a special hybrid polynomial-DNN controller which is easy to verify without losing its expressiveness and flexibility. This paper proposes a novel method to synthesize such a hybrid controller based on RL, low-degree polynomial fitting and knowledge distillation. It also gives a computational approach, by building and solving a constrained optimization problem coming from verification conditions to produce barrier certificates and Lyapunov-like functions, which can guarantee every trajectory from the initial set of the system with the resulted controller satisfies the given safety and goal-reaching requirements. We evaluate the proposed hybrid controller synthesis method on a set of benchmark examples, including several high-dimensional systems. The results validate the effectiveness and applicability of our approach.

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Zhang, Tengfei, and Yingmin Jia. "Adaptive Neural Network Control for Uncertain Robotic Manipulators with Output Constraint Using Integral-Barrier Lyapunov Functions." In Proceedings of 2018 Chinese Intelligent Systems Conference. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2291-4_8.

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Panagou, Dimitra, Dušan M. Stipanović, and Petros G. Voulgaris. "Distributed Control of Robot Swarms." In Robotic Systems. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1754-3.ch070.

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This chapter considers the problem of multi-agent coordination and control under multiple objectives, and presents a set-theoretic formulation which is amenable to Lyapunov-based analysis and control design. A novel class of Lyapunov-like barrier functions is introduced and used to encode multiple control objectives, such as collision avoidance, proximity maintenance and convergence to desired destinations. The construction is based on recentered barrier functions and on maximum approximation functions. Thus, a single Lyapunov-like function is used to encode the constrained set of each agent, yielding simple, gradient-based control solutions. The derived control strategies are distributed, i.e., based on information locally available to each agent, which is dictated by sensing and communication limitations. The proposed coordination protocol dictates semi-cooperative conflict resolution among agents, as well as conflict resolution with respect to an agent (the leader) which is not actively participating in collision avoidance, except when necessary. The considered scenario is pertinent to surveillance tasks and involves nonholonomic vehicles. The efficacy of the approach is demonstrated through simulation results.

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Zouari, Farouk, and Amina Boubellouta. "Neural Approximation-Based Adaptive Control for Pure-Feedback Fractional-Order Systems With Output Constraints and Actuator Nonlinearities." In Advanced Synchronization Control and Bifurcation of Chaotic Fractional-Order Systems. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5418-9.ch015.

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In this chapter, an adaptive control approach-based neural approximation is developed for a category of uncertain fractional-order systems with actuator nonlinearities and output constraints. First, to overcome the difficulties arising from the actuator nonlinearities and nonaffine structures, the mean value theorem is introduced. Second, to deal with the uncertain nonlinear dynamics, the unknown control directions and the output constraints, neural networks, smooth Nussbaum-type functions, and asymmetric barrier Lyapunov functions are employed, respectively. Moreover, for satisfactorily designing the control updating laws and to carry out the stability analysis of the overall closed-loop system, the Backstepping technique is used. The main advantage about this research is that (1) the number of parameters to be adapted is much reduced, (2) the tracking errors converge to zero, and (3) the output constraints are not transgressed. At last, simulation results demonstrate the feasibility of the newly presented design techniques.

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Wu, Zhe, Fahad Albalawi, Zhihao Zhang, Junfeng Zhang, Helen Durand, and Panagiotis D. Christofides. "Model Predictive Control for Process Operational Safety: Utilizing Safeness Index-Based Constraints and Control Lyapunov-Barrier Functions." In 13th International Symposium on Process Systems Engineering (PSE 2018). Elsevier, 2018. http://dx.doi.org/10.1016/b978-0-444-64241-7.50079-3.

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Conference papers on the topic "Barrier Lyapunov functionals"

1

Tian, Dongzuo, and Xingyong Song. "Control of a Downhole Drilling System Using Integral Barrier Lyapunov Functionals." In 2019 American Control Conference (ACC). IEEE, 2019. http://dx.doi.org/10.23919/acc.2019.8815370.

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Tee, Keng Peng, and Shuzhi Sam Ge. "Control of state-constrained nonlinear systems using Integral Barrier Lyapunov Functionals." In 2012 IEEE 51st Annual Conference on Decision and Control (CDC). IEEE, 2012. http://dx.doi.org/10.1109/cdc.2012.6426196.

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Li, Jing, and Yan-Jun Liu. "Control of nonlinear systems with full state constraints using integral Barrier Lyapunov Functionals." In 2015 International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS). IEEE, 2015. http://dx.doi.org/10.1109/iccss.2015.7281151.

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Romdlony, Muhammad Zakiyullah, and Bayu Jayawardhana. "Uniting Control Lyapunov and Control Barrier Functions." In 2014 IEEE 53rd Annual Conference on Decision and Control (CDC). IEEE, 2014. http://dx.doi.org/10.1109/cdc.2014.7039737.

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Wei, Yihao, Chuanjiang Li, Yanchao Sun, and Guangfu Ma. "Backstepping approach for controlling a quadrotor using Barrier Lyapunov Functions." In 2017 36th Chinese Control Conference (CCC). IEEE, 2017. http://dx.doi.org/10.23919/chicc.2017.8028349.

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Xiao, Wei, Calin A. Belta, and Christos G. Cassandras. "High Order Control Lyapunov-Barrier Functions for Temporal Logic Specifications." In 2021 American Control Conference (ACC). IEEE, 2021. http://dx.doi.org/10.23919/acc50511.2021.9483028.

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Zhao, Pan, Yanbing Mao, Chuyuan Tao, Naira Hovakimyan, and Xiaofeng Wang. "Adaptive Robust Quadratic Programs using Control Lyapunov and Barrier Functions." In 2020 59th IEEE Conference on Decision and Control (CDC). IEEE, 2020. http://dx.doi.org/10.1109/cdc42340.2020.9303829.

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Duan, Hongtao, Yang Yang, and Wenjun Gao. "Backstepping Sliding Approach for Controlling a Quadrotor Using Barrier Lyapunov Functions." In 2019 3rd International Conference on Electronic Information Technology and Computer Engineering (EITCE). IEEE, 2019. http://dx.doi.org/10.1109/eitce47263.2019.9094998.

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Ren, Wei. "Razumikhin-type Control Lyapunov and Barrier Functions for Time-Delay Systems." In 2021 60th IEEE Conference on Decision and Control (CDC). IEEE, 2021. http://dx.doi.org/10.1109/cdc45484.2021.9682928.

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Du, Desong, Shaohang Han, Naiming Qi, Haitham Bou Ammar, Jun Wang, and Wei Pan. "Reinforcement Learning for Safe Robot Control using Control Lyapunov Barrier Functions." In 2023 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2023. http://dx.doi.org/10.1109/icra48891.2023.10160991.

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